librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId string	author string	last_modified timestamp[us, tz=UTC]	downloads int64	likes int64	library_name string	tags sequence	pipeline_tag string	createdAt timestamp[us, tz=UTC]	card string
tiedaar/summary-longformer-wording	tiedaar	2022-11-27T20:22:11Z	175	0	transformers	[ "transformers", "pytorch", "longformer", "text-classification", "macroeconomics", "automated summary evaluation", "wording", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-19T16:20:03Z	--- language: - "en" thumbnail: "url to a thumbnail used in social sharing" tags: - 'macroeconomics' - 'automated summary evaluation' - 'wording' license: "apache-2.0" metrics: - 'mse' --- # Wording Model This is a longformer model with a regression head designed to predict the wording score of a summary. ## Corpus It was trained on a corpus of 4,233 summaries of 101 sources compiled by Botarleanu et al. (2022). The summaries were graded by expert raters on 6 criteria: Details, Main Point, Cohesion, Paraphrasing, Objective Language, and Language Beyond the Text. A principle component analyis was used to reduce the dimensionality of the outcome variables to two. * Content includes Details, Main Point, and Cohesion * Wording includes Paraphrasing, Objective Language, and Language Beyond the Text ## Score This model predicts the Wording score. The model to predict the Content score can be found [here](https://huggingface.co/tiedaar/summary-longformer-content). The following diagram illustrates the model architecture: ![model diagram](model_diagram.png) When providing input to the model, the summary and the source should be concatenated using the seperator token \</s>. This allows the model to have access to both the summary and the source to provide more accurate scores. The model reported an R2 of 0.66 on the test set of summaries. ![wording scatter](wording_scatter.png) ## Contact For questions or comments about this model, please contact [[email protected]]([email protected]).
ezzouhri/swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat	ezzouhri	2022-11-27T19:58:04Z	60	0	transformers	[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-23T17:49:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.4527928818586258 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.6236 - Accuracy: 0.4528 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 2.1452 \| 1.0 \| 268 \| 2.2034 \| 0.2247 \| \| 0.9905 \| 2.0 \| 536 \| 0.9729 \| 0.7126 \| \| 0.5262 \| 3.0 \| 804 \| 0.5282 \| 0.8314 \| \| 0.36 \| 4.0 \| 1072 \| 0.5618 \| 0.8337 \| \| 0.305 \| 5.0 \| 1340 \| 0.9210 \| 0.6535 \| \| 0.2669 \| 6.0 \| 1608 \| 1.1776 \| 0.6317 \| \| 0.2663 \| 7.0 \| 1876 \| 1.2129 \| 0.6290 \| \| 0.2207 \| 8.0 \| 2144 \| 2.2039 \| 0.4068 \| \| 0.2178 \| 9.0 \| 2412 \| 1.9747 \| 0.4740 \| \| 0.1822 \| 10.0 \| 2680 \| 1.4390 \| 0.5526 \| \| 0.1691 \| 11.0 \| 2948 \| 2.1020 \| 0.3814 \| \| 0.1731 \| 12.0 \| 3216 \| 2.0999 \| 0.4251 \| \| 0.1705 \| 13.0 \| 3484 \| 2.4643 \| 0.3700 \| \| 0.1286 \| 14.0 \| 3752 \| 2.7902 \| 0.4345 \| \| 0.1511 \| 15.0 \| 4020 \| 2.5151 \| 0.4165 \| \| 0.1403 \| 16.0 \| 4288 \| 4.4323 \| 0.3099 \| \| 0.1562 \| 17.0 \| 4556 \| 2.0293 \| 0.5096 \| \| 0.1233 \| 18.0 \| 4824 \| 2.5863 \| 0.4236 \| \| 0.1293 \| 19.0 \| 5092 \| 2.6533 \| 0.4506 \| \| 0.1268 \| 20.0 \| 5360 \| 2.1429 \| 0.4998 \| \| 0.1464 \| 21.0 \| 5628 \| 2.3014 \| 0.5470 \| \| 0.1507 \| 22.0 \| 5896 \| 2.3857 \| 0.4911 \| \| 0.1285 \| 23.0 \| 6164 \| 1.4228 \| 0.6406 \| \| 0.1364 \| 24.0 \| 6432 \| 3.6147 \| 0.4842 \| \| 0.1209 \| 25.0 \| 6700 \| 2.4210 \| 0.4896 \| \| 0.1321 \| 26.0 \| 6968 \| 2.7809 \| 0.5344 \| \| 0.0944 \| 27.0 \| 7236 \| 3.5598 \| 0.4226 \| \| 0.1013 \| 28.0 \| 7504 \| 4.0793 \| 0.3905 \| \| 0.1243 \| 29.0 \| 7772 \| 4.5733 \| 0.3443 \| \| 0.0962 \| 30.0 \| 8040 \| 2.9494 \| 0.4199 \| \| 0.0974 \| 31.0 \| 8308 \| 3.1012 \| 0.4496 \| \| 0.113 \| 32.0 \| 8576 \| 3.9522 \| 0.3764 \| \| 0.1067 \| 33.0 \| 8844 \| 1.9792 \| 0.6053 \| \| 0.095 \| 34.0 \| 9112 \| 2.8795 \| 0.5302 \| \| 0.1015 \| 35.0 \| 9380 \| 5.9943 \| 0.2941 \| \| 0.0912 \| 36.0 \| 9648 \| 2.9536 \| 0.5242 \| \| 0.1193 \| 37.0 \| 9916 \| 3.5187 \| 0.4226 \| \| 0.0906 \| 38.0 \| 10184 \| 3.0049 \| 0.5114 \| \| 0.1109 \| 39.0 \| 10452 \| 2.6823 \| 0.5675 \| \| 0.0903 \| 40.0 \| 10720 \| 4.7151 \| 0.3109 \| \| 0.0846 \| 41.0 \| 10988 \| 3.1118 \| 0.3880 \| \| 0.0986 \| 42.0 \| 11256 \| 3.9827 \| 0.4792 \| \| 0.1244 \| 43.0 \| 11524 \| 4.7544 \| 0.2860 \| \| 0.1039 \| 44.0 \| 11792 \| 4.4297 \| 0.3178 \| \| 0.077 \| 45.0 \| 12060 \| 5.8973 \| 0.3524 \| \| 0.0718 \| 46.0 \| 12328 \| 6.0338 \| 0.3033 \| \| 0.0838 \| 47.0 \| 12596 \| 6.3524 \| 0.3507 \| \| 0.0935 \| 48.0 \| 12864 \| 3.8675 \| 0.4194 \| \| 0.0922 \| 49.0 \| 13132 \| 4.7731 \| 0.3129 \| \| 0.0903 \| 50.0 \| 13400 \| 3.5435 \| 0.4115 \| \| 0.0927 \| 51.0 \| 13668 \| 4.7606 \| 0.4234 \| \| 0.0757 \| 52.0 \| 13936 \| 3.4110 \| 0.4436 \| \| 0.0738 \| 53.0 \| 14204 \| 6.3143 \| 0.3648 \| \| 0.076 \| 54.0 \| 14472 \| 4.9524 \| 0.3604 \| \| 0.0951 \| 55.0 \| 14740 \| 5.5633 \| 0.3680 \| \| 0.1078 \| 56.0 \| 15008 \| 5.9219 \| 0.3082 \| \| 0.0991 \| 57.0 \| 15276 \| 4.9457 \| 0.3344 \| \| 0.0968 \| 58.0 \| 15544 \| 4.0270 \| 0.4271 \| \| 0.0883 \| 59.0 \| 15812 \| 5.3006 \| 0.3574 \| \| 0.0728 \| 60.0 \| 16080 \| 6.9527 \| 0.3119 \| \| 0.0803 \| 61.0 \| 16348 \| 2.9117 \| 0.5 \| \| 0.1022 \| 62.0 \| 16616 \| 5.1631 \| 0.3487 \| \| 0.1155 \| 63.0 \| 16884 \| 5.2602 \| 0.3453 \| \| 0.0737 \| 64.0 \| 17152 \| 6.5281 \| 0.3129 \| \| 0.0735 \| 65.0 \| 17420 \| 4.9847 \| 0.3945 \| \| 0.0948 \| 66.0 \| 17688 \| 3.6684 \| 0.4330 \| \| 0.0765 \| 67.0 \| 17956 \| 4.2188 \| 0.4076 \| \| 0.0597 \| 68.0 \| 18224 \| 3.0067 \| 0.5208 \| \| 0.0866 \| 69.0 \| 18492 \| 3.8993 \| 0.4412 \| \| 0.0825 \| 70.0 \| 18760 \| 3.9058 \| 0.3945 \| \| 0.0897 \| 71.0 \| 19028 \| 4.5870 \| 0.3932 \| \| 0.0687 \| 72.0 \| 19296 \| 4.2837 \| 0.3744 \| \| 0.0774 \| 73.0 \| 19564 \| 4.9028 \| 0.3596 \| \| 0.0755 \| 74.0 \| 19832 \| 5.1321 \| 0.3356 \| \| 0.0728 \| 75.0 \| 20100 \| 4.5533 \| 0.3851 \| \| 0.0753 \| 76.0 \| 20368 \| 4.9765 \| 0.3898 \| \| 0.0582 \| 77.0 \| 20636 \| 5.1959 \| 0.3777 \| \| 0.0714 \| 78.0 \| 20904 \| 4.6735 \| 0.3707 \| \| 0.0928 \| 79.0 \| 21172 \| 3.6359 \| 0.4639 \| \| 0.0593 \| 80.0 \| 21440 \| 5.1507 \| 0.3841 \| \| 0.0972 \| 81.0 \| 21708 \| 5.3122 \| 0.3356 \| \| 0.0903 \| 82.0 \| 21976 \| 3.5833 \| 0.4310 \| \| 0.074 \| 83.0 \| 22244 \| 2.3014 \| 0.6349 \| \| 0.0651 \| 84.0 \| 22512 \| 3.8229 \| 0.4387 \| \| 0.0682 \| 85.0 \| 22780 \| 3.5292 \| 0.4627 \| \| 0.0543 \| 86.0 \| 23048 \| 4.0542 \| 0.4266 \| \| 0.0776 \| 87.0 \| 23316 \| 3.8799 \| 0.5240 \| \| 0.0868 \| 88.0 \| 23584 \| 4.1896 \| 0.4750 \| \| 0.0711 \| 89.0 \| 23852 \| 3.1013 \| 0.5381 \| \| 0.077 \| 90.0 \| 24120 \| 2.9132 \| 0.5650 \| \| 0.0672 \| 91.0 \| 24388 \| 4.4834 \| 0.3806 \| \| 0.0737 \| 92.0 \| 24656 \| 4.0161 \| 0.5116 \| \| 0.0868 \| 93.0 \| 24924 \| 2.9386 \| 0.4956 \| \| 0.0778 \| 94.0 \| 25192 \| 4.4806 \| 0.4478 \| \| 0.0586 \| 95.0 \| 25460 \| 5.0668 \| 0.4313 \| \| 0.0713 \| 96.0 \| 25728 \| 6.4632 \| 0.3043 \| \| 0.0897 \| 97.0 \| 25996 \| 5.0227 \| 0.4674 \| \| 0.073 \| 98.0 \| 26264 \| 3.6177 \| 0.4854 \| \| 0.0775 \| 99.0 \| 26532 \| 5.5003 \| 0.3702 \| \| 0.0709 \| 100.0 \| 26800 \| 5.6101 \| 0.3863 \| \| 0.078 \| 101.0 \| 27068 \| 4.3187 \| 0.4338 \| \| 0.0702 \| 102.0 \| 27336 \| 4.8467 \| 0.4545 \| \| 0.0498 \| 103.0 \| 27604 \| 3.9094 \| 0.4511 \| \| 0.0785 \| 104.0 \| 27872 \| 4.0952 \| 0.3836 \| \| 0.0767 \| 105.0 \| 28140 \| 3.2816 \| 0.4909 \| \| 0.0611 \| 106.0 \| 28408 \| 5.2239 \| 0.4221 \| \| 0.0753 \| 107.0 \| 28676 \| 4.2586 \| 0.4493 \| \| 0.0758 \| 108.0 \| 28944 \| 3.6094 \| 0.4938 \| \| 0.0951 \| 109.0 \| 29212 \| 6.1982 \| 0.3453 \| \| 0.086 \| 110.0 \| 29480 \| 6.4891 \| 0.3191 \| \| 0.0701 \| 111.0 \| 29748 \| 5.8145 \| 0.3235 \| \| 0.0772 \| 112.0 \| 30016 \| 3.7809 \| 0.5133 \| \| 0.0705 \| 113.0 \| 30284 \| 4.9590 \| 0.4372 \| \| 0.0602 \| 114.0 \| 30552 \| 5.5669 \| 0.3959 \| \| 0.0671 \| 115.0 \| 30820 \| 4.4897 \| 0.4429 \| \| 0.0692 \| 116.0 \| 31088 \| 5.1358 \| 0.3319 \| \| 0.0675 \| 117.0 \| 31356 \| 5.0169 \| 0.4226 \| \| 0.0626 \| 118.0 \| 31624 \| 5.6420 \| 0.4170 \| \| 0.0537 \| 119.0 \| 31892 \| 5.1601 \| 0.3683 \| \| 0.0543 \| 120.0 \| 32160 \| 5.4460 \| 0.3663 \| \| 0.0601 \| 121.0 \| 32428 \| 7.2877 \| 0.2981 \| \| 0.0743 \| 122.0 \| 32696 \| 6.5134 \| 0.3337 \| \| 0.0558 \| 123.0 \| 32964 \| 4.4690 \| 0.4469 \| \| 0.0396 \| 124.0 \| 33232 \| 4.4964 \| 0.4212 \| \| 0.0704 \| 125.0 \| 33500 \| 4.5766 \| 0.4011 \| \| 0.0547 \| 126.0 \| 33768 \| 4.0679 \| 0.4538 \| \| 0.0643 \| 127.0 \| 34036 \| 3.3335 \| 0.4545 \| \| 0.0709 \| 128.0 \| 34304 \| 3.6568 \| 0.4750 \| \| 0.0932 \| 129.0 \| 34572 \| 4.7978 \| 0.4614 \| \| 0.0522 \| 130.0 \| 34840 \| 6.1548 \| 0.3366 \| \| 0.0592 \| 131.0 \| 35108 \| 5.0728 \| 0.4409 \| \| 0.0528 \| 132.0 \| 35376 \| 5.5127 \| 0.4088 \| \| 0.087 \| 133.0 \| 35644 \| 4.5838 \| 0.3900 \| \| 0.0566 \| 134.0 \| 35912 \| 4.8733 \| 0.3683 \| \| 0.0474 \| 135.0 \| 36180 \| 3.4370 \| 0.4348 \| \| 0.0517 \| 136.0 \| 36448 \| 4.5547 \| 0.3908 \| \| 0.0627 \| 137.0 \| 36716 \| 4.7011 \| 0.4048 \| \| 0.0693 \| 138.0 \| 36984 \| 4.8039 \| 0.4419 \| \| 0.0753 \| 139.0 \| 37252 \| 4.6905 \| 0.4674 \| \| 0.0542 \| 140.0 \| 37520 \| 4.4103 \| 0.4278 \| \| 0.0629 \| 141.0 \| 37788 \| 4.5332 \| 0.4402 \| \| 0.0636 \| 142.0 \| 38056 \| 4.4822 \| 0.4288 \| \| 0.0551 \| 143.0 \| 38324 \| 5.3970 \| 0.3885 \| \| 0.0677 \| 144.0 \| 38592 \| 4.9337 \| 0.3811 \| \| 0.037 \| 145.0 \| 38860 \| 4.7588 \| 0.3979 \| \| 0.0426 \| 146.0 \| 39128 \| 4.5055 \| 0.4110 \| \| 0.0624 \| 147.0 \| 39396 \| 4.9575 \| 0.3722 \| \| 0.0799 \| 148.0 \| 39664 \| 3.9235 \| 0.4350 \| \| 0.0643 \| 149.0 \| 39932 \| 3.2063 \| 0.5297 \| \| 0.0687 \| 150.0 \| 40200 \| 3.1733 \| 0.5692 \| \| 0.0652 \| 151.0 \| 40468 \| 3.8738 \| 0.5178 \| \| 0.078 \| 152.0 \| 40736 \| 2.7892 \| 0.5319 \| \| 0.0644 \| 153.0 \| 41004 \| 3.4909 \| 0.5185 \| \| 0.0639 \| 154.0 \| 41272 \| 3.7233 \| 0.5005 \| \| 0.0517 \| 155.0 \| 41540 \| 4.9475 \| 0.4152 \| \| 0.0546 \| 156.0 \| 41808 \| 5.0784 \| 0.4251 \| \| 0.0704 \| 157.0 \| 42076 \| 5.3511 \| 0.3987 \| \| 0.0753 \| 158.0 \| 42344 \| 5.0345 \| 0.4538 \| \| 0.0504 \| 159.0 \| 42612 \| 4.1655 \| 0.4701 \| \| 0.0645 \| 160.0 \| 42880 \| 3.9242 \| 0.4936 \| \| 0.0543 \| 161.0 \| 43148 \| 4.8499 \| 0.4533 \| \| 0.0592 \| 162.0 \| 43416 \| 5.1871 \| 0.4345 \| \| 0.0716 \| 163.0 \| 43684 \| 5.4487 \| 0.4325 \| \| 0.0613 \| 164.0 \| 43952 \| 4.3626 \| 0.4711 \| \| 0.0616 \| 165.0 \| 44220 \| 4.8649 \| 0.4807 \| \| 0.0506 \| 166.0 \| 44488 \| 4.1038 \| 0.5133 \| \| 0.0802 \| 167.0 \| 44756 \| 5.0038 \| 0.4889 \| \| 0.0672 \| 168.0 \| 45024 \| 6.3643 \| 0.4009 \| \| 0.0562 \| 169.0 \| 45292 \| 6.1359 \| 0.4372 \| \| 0.0367 \| 170.0 \| 45560 \| 5.6726 \| 0.4340 \| \| 0.0687 \| 171.0 \| 45828 \| 5.2015 \| 0.4254 \| \| 0.061 \| 172.0 \| 46096 \| 5.0398 \| 0.4491 \| \| 0.0444 \| 173.0 \| 46364 \| 5.8819 \| 0.4414 \| \| 0.0685 \| 174.0 \| 46632 \| 6.0729 \| 0.4263 \| \| 0.0548 \| 175.0 \| 46900 \| 5.6388 \| 0.4298 \| \| 0.084 \| 176.0 \| 47168 \| 6.3042 \| 0.4090 \| \| 0.0575 \| 177.0 \| 47436 \| 6.3381 \| 0.4019 \| \| 0.0678 \| 178.0 \| 47704 \| 6.3679 \| 0.4100 \| \| 0.0445 \| 179.0 \| 47972 \| 6.3634 \| 0.4152 \| \| 0.081 \| 180.0 \| 48240 \| 6.4057 \| 0.4051 \| \| 0.0643 \| 181.0 \| 48508 \| 6.6593 \| 0.3648 \| \| 0.0497 \| 182.0 \| 48776 \| 6.7469 \| 0.3799 \| \| 0.0568 \| 183.0 \| 49044 \| 5.9056 \| 0.4221 \| \| 0.0513 \| 184.0 \| 49312 \| 6.4656 \| 0.4046 \| \| 0.0496 \| 185.0 \| 49580 \| 6.1444 \| 0.4140 \| \| 0.0524 \| 186.0 \| 49848 \| 5.9295 \| 0.4357 \| \| 0.0746 \| 187.0 \| 50116 \| 5.6245 \| 0.4612 \| \| 0.0489 \| 188.0 \| 50384 \| 5.6278 \| 0.4476 \| \| 0.0589 \| 189.0 \| 50652 \| 5.6629 \| 0.4595 \| \| 0.0365 \| 190.0 \| 50920 \| 5.9882 \| 0.4392 \| \| 0.0456 \| 191.0 \| 51188 \| 6.0186 \| 0.4496 \| \| 0.0486 \| 192.0 \| 51456 \| 5.6916 \| 0.4427 \| \| 0.0658 \| 193.0 \| 51724 \| 5.7638 \| 0.4461 \| \| 0.0599 \| 194.0 \| 51992 \| 5.7886 \| 0.4387 \| \| 0.0522 \| 195.0 \| 52260 \| 5.7112 \| 0.4464 \| \| 0.0556 \| 196.0 \| 52528 \| 5.7411 \| 0.4419 \| \| 0.0681 \| 197.0 \| 52796 \| 5.6449 \| 0.4516 \| \| 0.0649 \| 198.0 \| 53064 \| 5.6714 \| 0.4508 \| \| 0.0582 \| 199.0 \| 53332 \| 5.6241 \| 0.4521 \| \| 0.0727 \| 200.0 \| 53600 \| 5.6236 \| 0.4528 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.1+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
essayproj/cohesion	essayproj	2022-11-27T18:53:32Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-27T18:47:13Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: cohesion results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # cohesion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7082 - Accuracy: 0.4 - F1: 0.2857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
vipintom/my_awesome_model	vipintom	2022-11-27T18:16:11Z	64	0	transformers	[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-27T16:15:05Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: vipintom/my_awesome_model results: [] --- <!-- This model card has been generated automatically according to the information Keras had access to. You should probably proofread and complete it, then remove this comment. --> # vipintom/my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0683 - Validation Loss: 0.2223 - Train Accuracy: 0.9294 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Accuracy \| Epoch \| \|:----------:\|:---------------:\|:--------------:\|:-----:\| \| 0.2515 \| 0.1947 \| 0.9256 \| 0 \| \| 0.1354 \| 0.2041 \| 0.9253 \| 1 \| \| 0.0683 \| 0.2223 \| 0.9294 \| 2 \| ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2
k3nneth/finetuning-sentiment-model-3000-samples	k3nneth	2022-11-27T17:36:54Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-25T20:16:27Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8712871287128714 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3046 - Accuracy: 0.87 - F1: 0.8713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pucpr/biobertpt-clin	pucpr	2022-11-27T16:55:14Z	150	10	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: "pt" widget: - text: "O paciente recebeu [MASK] do hospital." - text: "O médico receitou a medicação para controlar a [MASK]." thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # BioBERTpt - Portuguese Clinical and Biomedical BERT The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. This model card describes the BioBERTpt(clin) model, a clinical version of BioBERTpt, trained on clinical narratives from electronic health records from Brazilian Hospitals. ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-clin") model = AutoModel.from_pretrained("pucpr/biobertpt-clin") ``` ## More Information Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).
pucpr/biobertpt-bio	pucpr	2022-11-27T16:54:50Z	162	6	transformers	[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-03-02T23:29:05Z	--- language: "pt" widget: - text: "O principal [MASK] da COVID-19 é tosse seca." - text: "O vírus da gripe apresenta um [MASK] constituído por segmentos de ácido ribonucleico." thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # BioBERTpt - Portuguese Clinical and Biomedical BERT The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. This model card describes the BioBERTpt(bio) model, a biomedical version of BioBERTpt, trained on Portuguese biomedical literature from scientific papers from Pubmed and Scielo. ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-bio") model = AutoModel.from_pretrained("pucpr/biobertpt-bio") ``` ## More Information Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).
rifkat/wav2vec2-large-xls-r-300m-uz	rifkat	2022-11-27T16:53:56Z	210	1	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-11T10:24:32Z	--- license: apache-2.0 --- Ushbu model GptUZ loyihasida, uzbek tilidagi ovozli ma'lumotlarni tanib olishda ishlatiladi. Model ochiq va yopiq ma'lumotlar asosida o'qitildi.
kejian/final-cond-25-0.01	kejian	2022-11-27T15:09:29Z	1	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-26T12:24:35Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-25-0.01 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kejian/final-cond-25-0.01 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0.000475}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-25-0.01', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/2gqinonx
mesolitica/finetune-paraphrase-t5-small-standard-bahasa-cased	mesolitica	2022-11-27T13:59:26Z	108	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-25T16:30:46Z	--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-small-standard-bahasa-cased Finetuned T5 small on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 37.598729045833316, '_mean': -1.0, '_ci': -1.0, '_verbose': '62.6/42.5/33.2/27.0 (BP = 0.957 ratio = 0.958 hyp_len = 96781 ref_len = 101064)', 'bp': 0.9567103919247614, 'counts': [60539, 38753, 28443, 21680], 'totals': [96781, 91237, 85693, 80149], 'sys_len': 96781, 'ref_len': 101064, 'precisions': [62.55256713611143, 42.47509234192268, 33.19174261608299, 27.049620082596164], 'prec_str': '62.6/42.5/33.2/27.0', 'ratio': 0.9576209134805668} ```
mesolitica/finetune-paraphrase-t5-base-standard-bahasa-cased	mesolitica	2022-11-27T13:59:04Z	110	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-26T01:52:56Z	--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-base-standard-bahasa-cased Finetuned T5 base on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 35.95965899952292, '_mean': -1.0, '_ci': -1.0, '_verbose': '61.7/41.3/32.0/25.8 (BP = 0.944 ratio = 0.946 hyp_len = 95593 ref_len = 101064)', 'bp': 0.9443747373110852, 'counts': [59014, 37157, 27016, 20383], 'totals': [95593, 90049, 84505, 78961], 'sys_len': 95593, 'ref_len': 101064, 'precisions': [61.73464584226878, 41.263090095392506, 31.969705934560086, 25.81400944770203], 'prec_str': '61.7/41.3/32.0/25.8', 'ratio': 0.9458659859099184} ```
Itsme33/Modelx	Itsme33	2022-11-27T13:55:19Z	0	0	null	[ "arxiv:2202.00512", "arxiv:2112.10752", "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2022-11-19T19:06:38Z	--- license: bigscience-bloom-rail-1.0 --- tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on `768x768` images. ![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/768/merged-0005.png?raw=true) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `768-v-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/768-v-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2#examples) ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - Resources for more information: [GitHub Repository](https://github.com/Stability-AI/). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler): .7z filter=lfs diff=lfs merge=lfs -text .arrow filter=lfs diff=lfs merge=lfs -text .bin filter=lfs diff=lfs merge=lfs -text .bz2 filter=lfs diff=lfs merge=lfs -text .ftz filter=lfs diff=lfs merge=lfs -text .gz filter=lfs diff=lfs merge=lfs -text .h5 filter=lfs diff=lfs merge=lfs -text .joblib filter=lfs diff=lfs merge=lfs -text .lfs. filter=lfs diff=lfs merge=lfs -text .mlmodel filter=lfs diff=lfs merge=lfs -text .model filter=lfs diff=lfs merge=lfs -text .msgpack filter=lfs diff=lfs merge=lfs -text .npy filter=lfs diff=lfs merge=lfs -text .npz filter=lfs diff=lfs merge=lfs -text .onnx filter=lfs diff=lfs merge=lfs -text .ot filter=lfs diff=lfs merge=lfs -text .parquet filter=lfs diff=lfs merge=lfs -text .pb filter=lfs diff=lfs merge=lfs -text .pickle filter=lfs diff=lfs merge=lfs -text .pkl filter=lfs diff=lfs merge=lfs -text .pt filter=lfs diff=lfs merge=lfs -text .pth filter=lfs diff=lfs merge=lfs -text .rar filter=lfs diff=lfs merge=lfs -text .safetensors filter=lfs diff=lfs merge=lfs -text saved_model// filter=lfs diff=lfs merge=lfs -text .tar. filter=lfs diff=lfs merge=lfs -text .tflite filter=lfs diff=lfs merge=lfs -text .tgz filter=lfs diff=lfs merge=lfs -text .wasm filter=lfs diff=lfs merge=lfs -text .xz filter=lfs diff=lfs merge=lfs -text .zip filter=lfs diff=lfs merge=lfs -text .zst filter=lfs diff=lfs merge=lfs -text tfevents filter=lfs diff=lfs merge=lfs -text
mesolitica/finetune-paraphrase-t5-tiny-standard-bahasa-cased	mesolitica	2022-11-27T13:54:24Z	109	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-25T17:24:20Z	--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-tiny-standard-bahasa-cased Finetuned T5 tiny on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 36.92696648298233, '_mean': -1.0, '_ci': -1.0, '_verbose': '62.5/42.3/33.0/26.9 (BP = 0.943 ratio = 0.945 hyp_len = 95496 ref_len = 101064)', 'bp': 0.9433611337299734, 'counts': [59650, 38055, 27875, 21217], 'totals': [95496, 89952, 84408, 78864], 'sys_len': 95496, 'ref_len': 101064, 'precisions': [62.46334925023038, 42.30589647812167, 33.02412093640413, 26.90327652667884], 'prec_str': '62.5/42.3/33.0/26.9', 'ratio': 0.944906198052719} ```
Yagorka/ddpm-butterflies-256	Yagorka	2022-11-27T13:06:28Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-11-20T19:04:19Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-butterflies-256 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-butterflies-256/tensorboard?#scalars)
sd-concepts-library/Rain_and_Monsters	sd-concepts-library	2022-11-27T13:02:02Z	0	10	null	[ "license:openrail", "region:us" ]	null	2022-11-27T12:50:11Z	--- license: openrail --- --- This is Rain and Monsters, my first embedding created for SD 2 - 768 using Gradient accumulation steps. Due to my technical limitations the embedding was trainned at a resolution of 640x640 with not many steps. Take it as a sample of what can be obtained. Renders in Automatic1111 ![04963-1815323483-photography , close view of wolverine AND (monster_0.5), in a rainy night, very detailed, by prue640v2 , movie still, water drop.png](https://s3.amazonaws.com/moonup/production/uploads/1669553747033-630406f20907b9a115c620e6.png) photography , close view of wolverine AND (monster:0.5), in a rainy night, very detailed, by prue640v2 , movie still, water drops, splashes Negative prompt: blur Steps: 26, Sampler: Euler a, CFG scale: 7, Seed: 1815323483, Size: 768x768, Model hash: 2c02b20a, ENSD: -1 ![04906-4269742702-by prue640v2 very detailed.png](https://s3.amazonaws.com/moonup/production/uploads/1669553847018-630406f20907b9a115c620e6.png) by prue640v2 very detailed Steps: 31, Sampler: Euler a, CFG scale: 7, Seed: 4269742702, Size: 640x640, Model hash: 2c02b20a, ENSD: -1
alexziweiwang/mtl_manual_fGroup010304	alexziweiwang	2022-11-27T12:42:00Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-27T12:25:20Z	--- tags: - generated_from_trainer model-index: - name: mtl_manual_fGroup010304 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mtl_manual_fGroup010304 This model is a fine-tuned version of [alexziweiwang/mtl_manual_mGroup0304](https://huggingface.co/alexziweiwang/mtl_manual_mGroup0304) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
alexziweiwang/mtl_manual_270039_epoch1	alexziweiwang	2022-11-27T12:13:28Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-27T08:41:55Z	--- tags: - generated_from_trainer model-index: - name: mtl_manual_270039_epoch1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mtl_manual_270039_epoch1 This is the check point of uaspeech-trianed-foundation model, plus torgo M01 (train-test-valid left out) group split trained This model is a fine-tuned version of [alexziweiwang/mtl_manual_270012_epoch1](https://huggingface.co/alexziweiwang/mtl_manual_270012_epoch1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
BeeBeaver/q-Taxi-v3	BeeBeaver	2022-11-27T12:05:54Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-11-27T12:05:20Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="BeeBeaver/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
jw4169/wav2vec2-large-xls-r-300m-kr-jw4169	jw4169	2022-11-27T11:57:14Z	18	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-25T21:07:46Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-kr-jw4169 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: ko_kr split: train args: ko_kr metrics: - name: Wer type: wer value: 0.519593179778642 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-kr-jw4169 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.9752 - Wer: 0.5196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 35.084 \| 1.39 \| 200 \| 6.8536 \| 1.0 \| \| 4.853 \| 2.78 \| 400 \| 4.6246 \| 1.0 \| \| 4.5491 \| 4.17 \| 600 \| 4.3815 \| 1.0 \| \| 2.799 \| 5.55 \| 800 \| 1.7402 \| 0.8642 \| \| 1.3872 \| 6.94 \| 1000 \| 1.2019 \| 0.7448 \| \| 0.9599 \| 8.33 \| 1200 \| 1.0594 \| 0.7134 \| \| 0.675 \| 9.72 \| 1400 \| 0.9321 \| 0.6404 \| \| 0.4775 \| 11.11 \| 1600 \| 0.9088 \| 0.5911 \| \| 0.3479 \| 12.5 \| 1800 \| 0.9430 \| 0.6010 \| \| 0.2712 \| 13.89 \| 2000 \| 0.8948 \| 0.5854 \| \| 0.2283 \| 15.28 \| 2200 \| 0.9009 \| 0.5495 \| \| 0.1825 \| 16.67 \| 2400 \| 0.9079 \| 0.5501 \| \| 0.161 \| 18.06 \| 2600 \| 0.9518 \| 0.5390 \| \| 0.1394 \| 19.44 \| 2800 \| 0.9529 \| 0.5399 \| \| 0.1266 \| 20.83 \| 3000 \| 0.9505 \| 0.5283 \| \| 0.1102 \| 22.22 \| 3200 \| 0.9748 \| 0.5328 \| \| 0.101 \| 23.61 \| 3400 \| 0.9593 \| 0.5316 \| \| 0.0907 \| 25.0 \| 3600 \| 0.9832 \| 0.5292 \| \| 0.0833 \| 26.39 \| 3800 \| 0.9773 \| 0.5181 \| \| 0.0781 \| 27.78 \| 4000 \| 0.9736 \| 0.5163 \| \| 0.0744 \| 29.17 \| 4200 \| 0.9752 \| 0.5196 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1
alexziweiwang/mtl_manual_m02_4of4	alexziweiwang	2022-11-27T11:55:21Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-27T11:37:57Z	--- tags: - generated_from_trainer model-index: - name: mtl_manual_m02_4of4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mtl_manual_m02_4of4 This model is a fine-tuned version of [alexziweiwang/mtl_manual_m02_3of4](https://huggingface.co/alexziweiwang/mtl_manual_m02_3of4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent	research-backup	2022-11-27T11:55:16Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:54:13Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7862698412698412 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3887240356083086 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.46581434130072263 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.712 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3958333333333333 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9002561398222089 - name: F1 (macro) type: f1_macro value: 0.8933774886390773 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5778373004618788 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6294691224268689 - name: F1 (macro) type: f1_macro value: 0.6170667717596963 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8833563430349135 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8661861485427765 - name: F1 (macro) type: f1_macro value: 0.8629818809654138 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3887240356083086 - Accuracy on BATS: 0.46581434130072263 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.3958333333333333 - Accuracy on Google: 0.712 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9002561398222089 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6294691224268689 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8661861485427765 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7862698412698412 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent	research-backup	2022-11-27T11:51:53Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:50:42Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8185912698412698 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4358288770053476 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4391691394658754 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.519177320733741 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.738 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42105263157894735 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39814814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.906282959168299 - name: F1 (macro) type: f1_macro value: 0.8996950498562719 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8178403755868544 - name: F1 (macro) type: f1_macro value: 0.6064939255543282 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6267605633802817 - name: F1 (macro) type: f1_macro value: 0.6190481484526614 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9549975655560965 - name: F1 (macro) type: f1_macro value: 0.8859714456704498 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8755875900971483 - name: F1 (macro) type: f1_macro value: 0.8727623228512762 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4358288770053476 - Accuracy on SAT: 0.4391691394658754 - Accuracy on BATS: 0.519177320733741 - Accuracy on U2: 0.42105263157894735 - Accuracy on U4: 0.39814814814814814 - Accuracy on Google: 0.738 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.906282959168299 - Micro F1 score on CogALexV: 0.8178403755868544 - Micro F1 score on EVALution: 0.6267605633802817 - Micro F1 score on K&H+N: 0.9549975655560965 - Micro F1 score on ROOT09: 0.8755875900971483 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8185912698412698 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent	research-backup	2022-11-27T11:50:01Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:48:50Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.712579365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5053475935828877 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49554896142433236 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6564758198999444 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.87 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4824561403508772 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4791666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9022148561096881 - name: F1 (macro) type: f1_macro value: 0.8981159765257255 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8265258215962441 - name: F1 (macro) type: f1_macro value: 0.6164099345000134 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6505958829902492 - name: F1 (macro) type: f1_macro value: 0.6353667017193642 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.946442234123948 - name: F1 (macro) type: f1_macro value: 0.8577056022246778 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8815418364149169 - name: F1 (macro) type: f1_macro value: 0.8790128401011933 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5053475935828877 - Accuracy on SAT: 0.49554896142433236 - Accuracy on BATS: 0.6564758198999444 - Accuracy on U2: 0.4824561403508772 - Accuracy on U4: 0.4791666666666667 - Accuracy on Google: 0.87 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9022148561096881 - Micro F1 score on CogALexV: 0.8265258215962441 - Micro F1 score on EVALution: 0.6505958829902492 - Micro F1 score on K&H+N: 0.946442234123948 - Micro F1 score on ROOT09: 0.8815418364149169 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.712579365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent	research-backup	2022-11-27T11:44:51Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:43:46Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5669047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3590504451038576 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6203446359088383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.72 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8995027874039475 - name: F1 (macro) type: f1_macro value: 0.8912344017788291 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8176056338028169 - name: F1 (macro) type: f1_macro value: 0.5805419092552433 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6208017334777898 - name: F1 (macro) type: f1_macro value: 0.606000263040489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9645962300897266 - name: F1 (macro) type: f1_macro value: 0.8936228757914538 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752742087120025 - name: F1 (macro) type: f1_macro value: 0.872873148878676 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3590504451038576 - Accuracy on BATS: 0.6203446359088383 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.72 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8995027874039475 - Micro F1 score on CogALexV: 0.8176056338028169 - Micro F1 score on EVALution: 0.6208017334777898 - Micro F1 score on K&H+N: 0.9645962300897266 - Micro F1 score on ROOT09: 0.8752742087120025 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5669047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent	research-backup	2022-11-27T11:41:36Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:40:29Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6727579365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2967914438502674 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29376854599406527 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5269594219010562 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.658 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3541666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8921199337049872 - name: F1 (macro) type: f1_macro value: 0.8810977870546514 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7835680751173709 - name: F1 (macro) type: f1_macro value: 0.5377409725643296 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6034669555796316 - name: F1 (macro) type: f1_macro value: 0.5863824756486721 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9533977881338248 - name: F1 (macro) type: f1_macro value: 0.8778384539799797 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8517706048260734 - name: F1 (macro) type: f1_macro value: 0.852655623225718 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.2967914438502674 - Accuracy on SAT: 0.29376854599406527 - Accuracy on BATS: 0.5269594219010562 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.3541666666666667 - Accuracy on Google: 0.658 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8921199337049872 - Micro F1 score on CogALexV: 0.7835680751173709 - Micro F1 score on EVALution: 0.6034669555796316 - Micro F1 score on K&H+N: 0.9533977881338248 - Micro F1 score on ROOT09: 0.8517706048260734 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6727579365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 2 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent	research-backup	2022-11-27T11:37:59Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:36:56Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7363888888888889 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4304812834224599 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43026706231454004 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.548082267926626 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.73 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4074074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8963387072472503 - name: F1 (macro) type: f1_macro value: 0.891487326786835 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8039906103286385 - name: F1 (macro) type: f1_macro value: 0.5734260325505077 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6576381365113759 - name: F1 (macro) type: f1_macro value: 0.6449557726437222 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8840358484526234 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8696333437793794 - name: F1 (macro) type: f1_macro value: 0.8680160623253651 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4304812834224599 - Accuracy on SAT: 0.43026706231454004 - Accuracy on BATS: 0.548082267926626 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.4074074074074074 - Accuracy on Google: 0.73 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8963387072472503 - Micro F1 score on CogALexV: 0.8039906103286385 - Micro F1 score on EVALution: 0.6576381365113759 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8696333437793794 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7363888888888889 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
alexziweiwang/mtl_manual_m02_3of4	alexziweiwang	2022-11-27T11:36:11Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-27T11:18:53Z	--- tags: - generated_from_trainer model-index: - name: mtl_manual_m02_3of4 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mtl_manual_m02_3of4 This model is a fine-tuned version of [alexziweiwang/mtl_manual_m02_half1](https://huggingface.co/alexziweiwang/mtl_manual_m02_half1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent	research-backup	2022-11-27T11:34:40Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:33:34Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.756984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4037433155080214 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3887240356083086 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5919955530850473 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.754 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4351851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8952840138616845 - name: F1 (macro) type: f1_macro value: 0.8880442620553469 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7962441314553991 - name: F1 (macro) type: f1_macro value: 0.5610822578719585 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6348862405200434 - name: F1 (macro) type: f1_macro value: 0.6222301227964097 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9495026778882938 - name: F1 (macro) type: f1_macro value: 0.8655789112280514 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8721403948605453 - name: F1 (macro) type: f1_macro value: 0.8700110677538381 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4037433155080214 - Accuracy on SAT: 0.3887240356083086 - Accuracy on BATS: 0.5919955530850473 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.4351851851851852 - Accuracy on Google: 0.754 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8952840138616845 - Micro F1 score on CogALexV: 0.7962441314553991 - Micro F1 score on EVALution: 0.6348862405200434 - Micro F1 score on K&H+N: 0.9495026778882938 - Micro F1 score on ROOT09: 0.8721403948605453 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.756984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent	research-backup	2022-11-27T11:29:48Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:28:46Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5426190476190476 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.48916064480266813 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.662 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8389332529757421 - name: F1 (macro) type: f1_macro value: 0.8239067926315072 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.715962441314554 - name: F1 (macro) type: f1_macro value: 0.416903883488565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5200433369447454 - name: F1 (macro) type: f1_macro value: 0.5091748225464606 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9359393475690339 - name: F1 (macro) type: f1_macro value: 0.8331186543970518 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8166718896897525 - name: F1 (macro) type: f1_macro value: 0.8127585517857737 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.48916064480266813 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.662 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8389332529757421 - Micro F1 score on CogALexV: 0.715962441314554 - Micro F1 score on EVALution: 0.5200433369447454 - Micro F1 score on K&H+N: 0.9359393475690339 - Micro F1 score on ROOT09: 0.8166718896897525 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5426190476190476 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent	research-backup	2022-11-27T11:26:24Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:25:19Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6805555555555556 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49416342412451364 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.744 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4027777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8559590176284467 - name: F1 (macro) type: f1_macro value: 0.8448557692543309 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7284037558685446 - name: F1 (macro) type: f1_macro value: 0.458884277968604 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5325027085590466 - name: F1 (macro) type: f1_macro value: 0.5078999264075337 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9396257911942686 - name: F1 (macro) type: f1_macro value: 0.8402746094684064 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8172986524600439 - name: F1 (macro) type: f1_macro value: 0.8137683267205073 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.49416342412451364 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4027777777777778 - Accuracy on Google: 0.744 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8559590176284467 - Micro F1 score on CogALexV: 0.7284037558685446 - Micro F1 score on EVALution: 0.5325027085590466 - Micro F1 score on K&H+N: 0.9396257911942686 - Micro F1 score on ROOT09: 0.8172986524600439 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6805555555555556 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent	research-backup	2022-11-27T11:19:42Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:18:30Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5626984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47304057809894384 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.724 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3611111111111111 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8758475214705439 - name: F1 (macro) type: f1_macro value: 0.8691352141617384 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7424882629107981 - name: F1 (macro) type: f1_macro value: 0.48330525572765204 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5758396533044421 - name: F1 (macro) type: f1_macro value: 0.5620753826217768 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9492940112679975 - name: F1 (macro) type: f1_macro value: 0.8534235407176715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8314008147916013 - name: F1 (macro) type: f1_macro value: 0.8245727533841717 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.47304057809894384 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3611111111111111 - Accuracy on Google: 0.724 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8758475214705439 - Micro F1 score on CogALexV: 0.7424882629107981 - Micro F1 score on EVALution: 0.5758396533044421 - Micro F1 score on K&H+N: 0.9492940112679975 - Micro F1 score on ROOT09: 0.8314008147916013 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5626984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent	research-backup	2022-11-27T11:16:16Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:15:11Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7075198412698412 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3293768545994065 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6103390772651474 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.782 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8668072924514088 - name: F1 (macro) type: f1_macro value: 0.8578995013371921 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8004694835680752 - name: F1 (macro) type: f1_macro value: 0.5495048165643515 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5585048754062839 - name: F1 (macro) type: f1_macro value: 0.5382998120865616 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9495026778882938 - name: F1 (macro) type: f1_macro value: 0.8730545069423521 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8423691632717015 - name: F1 (macro) type: f1_macro value: 0.8392309386107293 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3293768545994065 - Accuracy on BATS: 0.6103390772651474 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.3449074074074074 - Accuracy on Google: 0.782 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8668072924514088 - Micro F1 score on CogALexV: 0.8004694835680752 - Micro F1 score on EVALution: 0.5585048754062839 - Micro F1 score on K&H+N: 0.9495026778882938 - Micro F1 score on ROOT09: 0.8423691632717015 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7075198412698412 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 4 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent	research-backup	2022-11-27T11:14:40Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:13:32Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8384722222222222 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37298499166203447 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.594 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44212962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8761488624378485 - name: F1 (macro) type: f1_macro value: 0.8674862753224203 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7647887323943662 - name: F1 (macro) type: f1_macro value: 0.512977631266088 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6034669555796316 - name: F1 (macro) type: f1_macro value: 0.5925306935387006 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9543715656952076 - name: F1 (macro) type: f1_macro value: 0.8708484151911093 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8545910372923848 - name: F1 (macro) type: f1_macro value: 0.8542907180407261 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.37298499166203447 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.44212962962962965 - Accuracy on Google: 0.594 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8761488624378485 - Micro F1 score on CogALexV: 0.7647887323943662 - Micro F1 score on EVALution: 0.6034669555796316 - Micro F1 score on K&H+N: 0.9543715656952076 - Micro F1 score on ROOT09: 0.8545910372923848 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8384722222222222 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent	research-backup	2022-11-27T11:09:49Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:08:46Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6259325396825397 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3422459893048128 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38632573652028906 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.472 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3157894736842105 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3402777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564110290794034 - name: F1 (macro) type: f1_macro value: 0.8440168167296423 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7647887323943662 - name: F1 (macro) type: f1_macro value: 0.496728365995928 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.542795232936078 - name: F1 (macro) type: f1_macro value: 0.5330540852169673 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9477637893858246 - name: F1 (macro) type: f1_macro value: 0.8528390601522364 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8382952052648073 - name: F1 (macro) type: f1_macro value: 0.8301168432370699 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3422459893048128 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.38632573652028906 - Accuracy on U2: 0.3157894736842105 - Accuracy on U4: 0.3402777777777778 - Accuracy on Google: 0.472 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8564110290794034 - Micro F1 score on CogALexV: 0.7647887323943662 - Micro F1 score on EVALution: 0.542795232936078 - Micro F1 score on K&H+N: 0.9477637893858246 - Micro F1 score on ROOT09: 0.8382952052648073 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6259325396825397 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 2 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent	research-backup	2022-11-27T11:08:11Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:07:07Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6776984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31283422459893045 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3086053412462908 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6042245692051139 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.746 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3888888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8716287479282808 - name: F1 (macro) type: f1_macro value: 0.858806993883955 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7561032863849765 - name: F1 (macro) type: f1_macro value: 0.49523926791823153 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5455037919826652 - name: F1 (macro) type: f1_macro value: 0.5396914583253821 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9380260137719969 - name: F1 (macro) type: f1_macro value: 0.8440588889140052 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8448762143528674 - name: F1 (macro) type: f1_macro value: 0.838226018147099 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31283422459893045 - Accuracy on SAT: 0.3086053412462908 - Accuracy on BATS: 0.6042245692051139 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3888888888888889 - Accuracy on Google: 0.746 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8716287479282808 - Micro F1 score on CogALexV: 0.7561032863849765 - Micro F1 score on EVALution: 0.5455037919826652 - Micro F1 score on K&H+N: 0.9380260137719969 - Micro F1 score on ROOT09: 0.8448762143528674 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6776984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical	research-backup	2022-11-27T11:03:19Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:02:12Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7979166666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3877005347593583 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5169538632573653 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.748 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4050925925925926 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8945306614434232 - name: F1 (macro) type: f1_macro value: 0.8889500954383515 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8305164319248827 - name: F1 (macro) type: f1_macro value: 0.6378751139859162 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6468039003250271 - name: F1 (macro) type: f1_macro value: 0.6417982065266167 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.955762676497183 - name: F1 (macro) type: f1_macro value: 0.8829548510663163 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8834221247257913 - name: F1 (macro) type: f1_macro value: 0.8817444772279625 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3877005347593583 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.5169538632573653 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4050925925925926 - Accuracy on Google: 0.748 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8945306614434232 - Micro F1 score on CogALexV: 0.8305164319248827 - Micro F1 score on EVALution: 0.6468039003250271 - Micro F1 score on K&H+N: 0.955762676497183 - Micro F1 score on ROOT09: 0.8834221247257913 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7979166666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical	research-backup	2022-11-27T11:01:36Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T11:00:29Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8185912698412698 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4358288770053476 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4391691394658754 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.519177320733741 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.738 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42105263157894735 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39814814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.906282959168299 - name: F1 (macro) type: f1_macro value: 0.8996950498562719 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8178403755868544 - name: F1 (macro) type: f1_macro value: 0.6064939255543282 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6267605633802817 - name: F1 (macro) type: f1_macro value: 0.6190481484526614 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9549975655560965 - name: F1 (macro) type: f1_macro value: 0.8859714456704498 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8755875900971483 - name: F1 (macro) type: f1_macro value: 0.8727623228512762 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4358288770053476 - Accuracy on SAT: 0.4391691394658754 - Accuracy on BATS: 0.519177320733741 - Accuracy on U2: 0.42105263157894735 - Accuracy on U4: 0.39814814814814814 - Accuracy on Google: 0.738 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.906282959168299 - Micro F1 score on CogALexV: 0.8178403755868544 - Micro F1 score on EVALution: 0.6267605633802817 - Micro F1 score on K&H+N: 0.9549975655560965 - Micro F1 score on ROOT09: 0.8755875900971483 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8185912698412698 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical	research-backup	2022-11-27T10:59:55Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:58:50Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7243055555555555 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47593582887700536 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47477744807121663 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6753752084491385 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.868 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43859649122807015 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9029682085279493 - name: F1 (macro) type: f1_macro value: 0.8981565352595764 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8276995305164319 - name: F1 (macro) type: f1_macro value: 0.6302262610207737 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6522210184182016 - name: F1 (macro) type: f1_macro value: 0.6357658526272931 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9555540098768867 - name: F1 (macro) type: f1_macro value: 0.8819875185777288 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.872453776245691 - name: F1 (macro) type: f1_macro value: 0.868959677073872 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.47593582887700536 - Accuracy on SAT: 0.47477744807121663 - Accuracy on BATS: 0.6753752084491385 - Accuracy on U2: 0.43859649122807015 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.868 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9029682085279493 - Micro F1 score on CogALexV: 0.8276995305164319 - Micro F1 score on EVALution: 0.6522210184182016 - Micro F1 score on K&H+N: 0.9555540098768867 - Micro F1 score on ROOT09: 0.872453776245691 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7243055555555555 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical	research-backup	2022-11-27T10:55:00Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:53:33Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5262301587301588 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3422459893048128 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35311572700296734 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5814341300722624 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.696 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8976947416001205 - name: F1 (macro) type: f1_macro value: 0.8898626811561807 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.794131455399061 - name: F1 (macro) type: f1_macro value: 0.5320544436133094 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6153846153846154 - name: F1 (macro) type: f1_macro value: 0.6017116191379441 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9678653404743688 - name: F1 (macro) type: f1_macro value: 0.9013285476834861 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8633657160764651 - name: F1 (macro) type: f1_macro value: 0.8622729537898305 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3422459893048128 - Accuracy on SAT: 0.35311572700296734 - Accuracy on BATS: 0.5814341300722624 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.696 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8976947416001205 - Micro F1 score on CogALexV: 0.794131455399061 - Micro F1 score on EVALution: 0.6153846153846154 - Micro F1 score on K&H+N: 0.9678653404743688 - Micro F1 score on ROOT09: 0.8633657160764651 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5262301587301588 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical	research-backup	2022-11-27T10:51:28Z	101	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:50:22Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6477777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3916913946587537 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6525847693162868 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.83 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4097222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9025161970769926 - name: F1 (macro) type: f1_macro value: 0.8944325918923539 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8093896713615022 - name: F1 (macro) type: f1_macro value: 0.5748135456441361 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6305525460455038 - name: F1 (macro) type: f1_macro value: 0.6156874339832458 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9573624539194547 - name: F1 (macro) type: f1_macro value: 0.8782849835856299 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.8617512701372868 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3916913946587537 - Accuracy on BATS: 0.6525847693162868 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.4097222222222222 - Accuracy on Google: 0.83 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9025161970769926 - Micro F1 score on CogALexV: 0.8093896713615022 - Micro F1 score on EVALution: 0.6305525460455038 - Micro F1 score on K&H+N: 0.9573624539194547 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6477777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical	research-backup	2022-11-27T10:47:52Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:46:46Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7918253968253969 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37433155080213903 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5063924402445803 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.732 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4649122807017544 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4699074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9029682085279493 - name: F1 (macro) type: f1_macro value: 0.9007842298298612 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8000000000000002 - name: F1 (macro) type: f1_macro value: 0.5698574777874919 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6370530877573131 - name: F1 (macro) type: f1_macro value: 0.6301907781696539 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9563886763580719 - name: F1 (macro) type: f1_macro value: 0.8805161918245229 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8693199623942337 - name: F1 (macro) type: f1_macro value: 0.8693436077460507 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37433155080213903 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.5063924402445803 - Accuracy on U2: 0.4649122807017544 - Accuracy on U4: 0.4699074074074074 - Accuracy on Google: 0.732 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9029682085279493 - Micro F1 score on CogALexV: 0.8000000000000002 - Micro F1 score on EVALution: 0.6370530877573131 - Micro F1 score on K&H+N: 0.9563886763580719 - Micro F1 score on ROOT09: 0.8693199623942337 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7918253968253969 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical	research-backup	2022-11-27T10:46:13Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:45:08Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8402777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42513368983957217 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42433234421364985 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5147304057809894 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.726 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4166666666666667 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39351851851851855 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8798722402334079 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5810551586304064 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6511375947995667 - name: F1 (macro) type: f1_macro value: 0.6381606420091618 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.960562008763998 - name: F1 (macro) type: f1_macro value: 0.8816273449850349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.862338882041517 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42513368983957217 - Accuracy on SAT: 0.42433234421364985 - Accuracy on BATS: 0.5147304057809894 - Accuracy on U2: 0.4166666666666667 - Accuracy on U4: 0.39351851851851855 - Accuracy on Google: 0.726 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6511375947995667 - Micro F1 score on K&H+N: 0.960562008763998 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8402777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical	research-backup	2022-11-27T10:42:58Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:41:52Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7941865079365079 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4839572192513369 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49258160237388726 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5714285714285714 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.742 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44298245614035087 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8769104196894718 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8068075117370893 - name: F1 (macro) type: f1_macro value: 0.5788410582872519 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6592632719393283 - name: F1 (macro) type: f1_macro value: 0.6485530008701489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9498504555887877 - name: F1 (macro) type: f1_macro value: 0.8670463800845212 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8539203486771704 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4839572192513369 - Accuracy on SAT: 0.49258160237388726 - Accuracy on BATS: 0.5714285714285714 - Accuracy on U2: 0.44298245614035087 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.742 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8068075117370893 - Micro F1 score on EVALution: 0.6592632719393283 - Micro F1 score on K&H+N: 0.9498504555887877 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7941865079365079 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical	research-backup	2022-11-27T10:41:20Z	105	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:40:12Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.665813492063492 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43315508021390375 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42136498516320475 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6020011117287382 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.822 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44907407407407407 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9016121741750791 - name: F1 (macro) type: f1_macro value: 0.8952063264803337 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8072769953051643 - name: F1 (macro) type: f1_macro value: 0.5850185012296035 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6538461538461539 - name: F1 (macro) type: f1_macro value: 0.6495448120234049 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9568060095986646 - name: F1 (macro) type: f1_macro value: 0.8713838077897916 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740206831714196 - name: F1 (macro) type: f1_macro value: 0.8710453231442221 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.43315508021390375 - Accuracy on SAT: 0.42136498516320475 - Accuracy on BATS: 0.6020011117287382 - Accuracy on U2: 0.4473684210526316 - Accuracy on U4: 0.44907407407407407 - Accuracy on Google: 0.822 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9016121741750791 - Micro F1 score on CogALexV: 0.8072769953051643 - Micro F1 score on EVALution: 0.6538461538461539 - Micro F1 score on K&H+N: 0.9568060095986646 - Micro F1 score on ROOT09: 0.8740206831714196 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.665813492063492 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical	research-backup	2022-11-27T10:39:36Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:38:27Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5426190476190476 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.48916064480266813 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.662 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8389332529757421 - name: F1 (macro) type: f1_macro value: 0.8239067926315072 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.715962441314554 - name: F1 (macro) type: f1_macro value: 0.416903883488565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5200433369447454 - name: F1 (macro) type: f1_macro value: 0.5091748225464606 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9359393475690339 - name: F1 (macro) type: f1_macro value: 0.8331186543970518 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8166718896897525 - name: F1 (macro) type: f1_macro value: 0.8127585517857737 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.48916064480266813 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.662 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8389332529757421 - Micro F1 score on CogALexV: 0.715962441314554 - Micro F1 score on EVALution: 0.5200433369447454 - Micro F1 score on K&H+N: 0.9359393475690339 - Micro F1 score on ROOT09: 0.8166718896897525 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5426190476190476 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical	research-backup	2022-11-27T10:35:54Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:34:44Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6639087301587302 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35561497326203206 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42857142857142855 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.668 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3587962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8540003013409673 - name: F1 (macro) type: f1_macro value: 0.8415316548238166 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6960093896713615 - name: F1 (macro) type: f1_macro value: 0.4121210944195095 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5021668472372698 - name: F1 (macro) type: f1_macro value: 0.4940624714187321 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9437991236001948 - name: F1 (macro) type: f1_macro value: 0.8505990194719038 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.823879661548104 - name: F1 (macro) type: f1_macro value: 0.8171304236528446 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.35561497326203206 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.42857142857142855 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.3587962962962963 - Accuracy on Google: 0.668 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8540003013409673 - Micro F1 score on CogALexV: 0.6960093896713615 - Micro F1 score on EVALution: 0.5021668472372698 - Micro F1 score on K&H+N: 0.9437991236001948 - Micro F1 score on ROOT09: 0.823879661548104 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6639087301587302 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical	research-backup	2022-11-27T10:30:47Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:29:36Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5528769841269842 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3181818181818182 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5780989438576987 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.75 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8646979056802773 - name: F1 (macro) type: f1_macro value: 0.8512823829061237 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7136150234741784 - name: F1 (macro) type: f1_macro value: 0.4307168102299423 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5563380281690141 - name: F1 (macro) type: f1_macro value: 0.5317021379158652 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9493635668080963 - name: F1 (macro) type: f1_macro value: 0.8585118986669762 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8232528987778126 - name: F1 (macro) type: f1_macro value: 0.8208236307090298 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3181818181818182 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5780989438576987 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.75 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8646979056802773 - Micro F1 score on CogALexV: 0.7136150234741784 - Micro F1 score on EVALution: 0.5563380281690141 - Micro F1 score on K&H+N: 0.9493635668080963 - Micro F1 score on ROOT09: 0.8232528987778126 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5528769841269842 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical	research-backup	2022-11-27T10:29:01Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:27:51Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5626984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47304057809894384 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.724 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3611111111111111 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8758475214705439 - name: F1 (macro) type: f1_macro value: 0.8691352141617384 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7424882629107981 - name: F1 (macro) type: f1_macro value: 0.48330525572765204 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5758396533044421 - name: F1 (macro) type: f1_macro value: 0.5620753826217768 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9492940112679975 - name: F1 (macro) type: f1_macro value: 0.8534235407176715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8314008147916013 - name: F1 (macro) type: f1_macro value: 0.8245727533841717 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.47304057809894384 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3611111111111111 - Accuracy on Google: 0.724 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8758475214705439 - Micro F1 score on CogALexV: 0.7424882629107981 - Micro F1 score on EVALution: 0.5758396533044421 - Micro F1 score on K&H+N: 0.9492940112679975 - Micro F1 score on ROOT09: 0.8314008147916013 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5626984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical	research-backup	2022-11-27T10:27:12Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:26:04Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6220436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6425792106725959 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.86 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8737381346994124 - name: F1 (macro) type: f1_macro value: 0.8631099788352631 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7786384976525821 - name: F1 (macro) type: f1_macro value: 0.5127374486425482 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5557963163596966 - name: F1 (macro) type: f1_macro value: 0.5383373253296296 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9456075676427628 - name: F1 (macro) type: f1_macro value: 0.8417797508933453 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.849890316515199 - name: F1 (macro) type: f1_macro value: 0.8461613177196163 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.6425792106725959 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.86 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8737381346994124 - Micro F1 score on CogALexV: 0.7786384976525821 - Micro F1 score on EVALution: 0.5557963163596966 - Micro F1 score on K&H+N: 0.9456075676427628 - Micro F1 score on ROOT09: 0.849890316515199 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6220436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical	research-backup	2022-11-27T10:25:30Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:24:27Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.41785714285714287 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3264094955489614 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5302946081156198 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.648 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3201754385964912 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.877655567274371 - name: F1 (macro) type: f1_macro value: 0.8691271164987491 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7129107981220657 - name: F1 (macro) type: f1_macro value: 0.4196917803483246 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5368364030335862 - name: F1 (macro) type: f1_macro value: 0.5188634387372184 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9531195659734298 - name: F1 (macro) type: f1_macro value: 0.8766279141586715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8031964901284864 - name: F1 (macro) type: f1_macro value: 0.8003449544921196 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3264094955489614 - Accuracy on BATS: 0.5302946081156198 - Accuracy on U2: 0.3201754385964912 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.648 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.877655567274371 - Micro F1 score on CogALexV: 0.7129107981220657 - Micro F1 score on EVALution: 0.5368364030335862 - Micro F1 score on K&H+N: 0.9531195659734298 - Micro F1 score on ROOT09: 0.8031964901284864 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.41785714285714287 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical	research-backup	2022-11-27T10:19:00Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:17:57Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6413492063492063 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.386881600889383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3684210526315789 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752448395359349 - name: F1 (macro) type: f1_macro value: 0.8642915064078064 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.752582159624413 - name: F1 (macro) type: f1_macro value: 0.445502286190483 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.557421451787649 - name: F1 (macro) type: f1_macro value: 0.5384307321042293 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9433122348195033 - name: F1 (macro) type: f1_macro value: 0.8480017401840129 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8429959260419931 - name: F1 (macro) type: f1_macro value: 0.8422459710118972 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.386881600889383 - Accuracy on U2: 0.3684210526315789 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.516 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8752448395359349 - Micro F1 score on CogALexV: 0.752582159624413 - Micro F1 score on EVALution: 0.557421451787649 - Micro F1 score on K&H+N: 0.9433122348195033 - Micro F1 score on ROOT09: 0.8429959260419931 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6413492063492063 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child	research-backup	2022-11-27T10:12:25Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:11:18Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7979166666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3877005347593583 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5169538632573653 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.748 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4050925925925926 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8945306614434232 - name: F1 (macro) type: f1_macro value: 0.8889500954383515 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8305164319248827 - name: F1 (macro) type: f1_macro value: 0.6378751139859162 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6468039003250271 - name: F1 (macro) type: f1_macro value: 0.6417982065266167 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.955762676497183 - name: F1 (macro) type: f1_macro value: 0.8829548510663163 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8834221247257913 - name: F1 (macro) type: f1_macro value: 0.8817444772279625 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3877005347593583 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.5169538632573653 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4050925925925926 - Accuracy on Google: 0.748 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8945306614434232 - Micro F1 score on CogALexV: 0.8305164319248827 - Micro F1 score on EVALution: 0.6468039003250271 - Micro F1 score on K&H+N: 0.955762676497183 - Micro F1 score on ROOT09: 0.8834221247257913 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7979166666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
Euchale/ArcaneInkpunk	Euchale	2022-11-27T10:08:57Z	0	0	null	[ "region:us" ]	null	2022-11-26T22:34:18Z	50/50 Merge of Inkpunk (https://huggingface.co/Envvi/Inkpunk-Diffusion/blob/main/inkpunk-diffusion-v1.ckpt) and Arcane (https://huggingface.co/nitrosocke/Arcane-Diffusion)
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child	research-backup	2022-11-27T10:03:36Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:02:26Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5669047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3590504451038576 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6203446359088383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.72 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8995027874039475 - name: F1 (macro) type: f1_macro value: 0.8912344017788291 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8176056338028169 - name: F1 (macro) type: f1_macro value: 0.5805419092552433 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6208017334777898 - name: F1 (macro) type: f1_macro value: 0.606000263040489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9645962300897266 - name: F1 (macro) type: f1_macro value: 0.8936228757914538 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752742087120025 - name: F1 (macro) type: f1_macro value: 0.872873148878676 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3590504451038576 - Accuracy on BATS: 0.6203446359088383 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.72 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8995027874039475 - Micro F1 score on CogALexV: 0.8176056338028169 - Micro F1 score on EVALution: 0.6208017334777898 - Micro F1 score on K&H+N: 0.9645962300897266 - Micro F1 score on ROOT09: 0.8752742087120025 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5669047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child	research-backup	2022-11-27T10:01:50Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T10:00:47Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6790674603174603 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4144385026737968 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4065281899109792 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6142301278488049 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.736 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37962962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8940786499924664 - name: F1 (macro) type: f1_macro value: 0.8886568208317418 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8112676056338028 - name: F1 (macro) type: f1_macro value: 0.5806627021345718 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.628385698808234 - name: F1 (macro) type: f1_macro value: 0.6195235287229023 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9612575641649858 - name: F1 (macro) type: f1_macro value: 0.8870509987826778 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8655593857724851 - name: F1 (macro) type: f1_macro value: 0.8633665155994553 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4144385026737968 - Accuracy on SAT: 0.4065281899109792 - Accuracy on BATS: 0.6142301278488049 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.37962962962962965 - Accuracy on Google: 0.736 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8940786499924664 - Micro F1 score on CogALexV: 0.8112676056338028 - Micro F1 score on EVALution: 0.628385698808234 - Micro F1 score on K&H+N: 0.9612575641649858 - Micro F1 score on ROOT09: 0.8655593857724851 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6790674603174603 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child	research-backup	2022-11-27T09:58:31Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:57:24Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7464087301587301 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42245989304812837 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42729970326409494 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6286826014452473 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.8 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4517543859649123 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.891366581286726 - name: F1 (macro) type: f1_macro value: 0.8832611892840135 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8321596244131455 - name: F1 (macro) type: f1_macro value: 0.6369879175787183 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6641386782231853 - name: F1 (macro) type: f1_macro value: 0.6521557904641199 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9580580093204424 - name: F1 (macro) type: f1_macro value: 0.8787147026333313 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8746474459417111 - name: F1 (macro) type: f1_macro value: 0.8720645901570915 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42245989304812837 - Accuracy on SAT: 0.42729970326409494 - Accuracy on BATS: 0.6286826014452473 - Accuracy on U2: 0.4517543859649123 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.8 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.891366581286726 - Micro F1 score on CogALexV: 0.8321596244131455 - Micro F1 score on EVALution: 0.6641386782231853 - Micro F1 score on K&H+N: 0.9580580093204424 - Micro F1 score on ROOT09: 0.8746474459417111 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7464087301587301 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child	research-backup	2022-11-27T09:56:47Z	107	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:55:44Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7363888888888889 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4304812834224599 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43026706231454004 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.548082267926626 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.73 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4074074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8963387072472503 - name: F1 (macro) type: f1_macro value: 0.891487326786835 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8039906103286385 - name: F1 (macro) type: f1_macro value: 0.5734260325505077 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6576381365113759 - name: F1 (macro) type: f1_macro value: 0.6449557726437222 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8840358484526234 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8696333437793794 - name: F1 (macro) type: f1_macro value: 0.8680160623253651 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4304812834224599 - Accuracy on SAT: 0.43026706231454004 - Accuracy on BATS: 0.548082267926626 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.4074074074074074 - Accuracy on Google: 0.73 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8963387072472503 - Micro F1 score on CogALexV: 0.8039906103286385 - Micro F1 score on EVALution: 0.6576381365113759 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8696333437793794 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7363888888888889 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child	research-backup	2022-11-27T09:55:10Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:54:02Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8402777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42513368983957217 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42433234421364985 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5147304057809894 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.726 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4166666666666667 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39351851851851855 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8798722402334079 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5810551586304064 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6511375947995667 - name: F1 (macro) type: f1_macro value: 0.6381606420091618 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.960562008763998 - name: F1 (macro) type: f1_macro value: 0.8816273449850349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.862338882041517 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42513368983957217 - Accuracy on SAT: 0.42433234421364985 - Accuracy on BATS: 0.5147304057809894 - Accuracy on U2: 0.4166666666666667 - Accuracy on U4: 0.39351851851851855 - Accuracy on Google: 0.726 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6511375947995667 - Micro F1 score on K&H+N: 0.960562008763998 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8402777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child	research-backup	2022-11-27T09:51:45Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:50:36Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7941865079365079 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4839572192513369 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49258160237388726 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5714285714285714 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.742 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44298245614035087 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8769104196894718 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8068075117370893 - name: F1 (macro) type: f1_macro value: 0.5788410582872519 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6592632719393283 - name: F1 (macro) type: f1_macro value: 0.6485530008701489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9498504555887877 - name: F1 (macro) type: f1_macro value: 0.8670463800845212 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8539203486771704 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4839572192513369 - Accuracy on SAT: 0.49258160237388726 - Accuracy on BATS: 0.5714285714285714 - Accuracy on U2: 0.44298245614035087 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.742 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8068075117370893 - Micro F1 score on EVALution: 0.6592632719393283 - Micro F1 score on K&H+N: 0.9498504555887877 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7941865079365079 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child	research-backup	2022-11-27T09:43:26Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:42:19Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7002579365079366 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3649851632047478 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44691495275152865 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.716 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740394756667169 - name: F1 (macro) type: f1_macro value: 0.8618347056127108 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7246478873239436 - name: F1 (macro) type: f1_macro value: 0.4392658356578824 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5243770314192849 - name: F1 (macro) type: f1_macro value: 0.5089556262366394 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9525631216526397 - name: F1 (macro) type: f1_macro value: 0.8724005012414945 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564713256032591 - name: F1 (macro) type: f1_macro value: 0.8510225054091837 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3649851632047478 - Accuracy on BATS: 0.44691495275152865 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.375 - Accuracy on Google: 0.716 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8740394756667169 - Micro F1 score on CogALexV: 0.7246478873239436 - Micro F1 score on EVALution: 0.5243770314192849 - Micro F1 score on K&H+N: 0.9525631216526397 - Micro F1 score on ROOT09: 0.8564713256032591 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7002579365079366 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child	research-backup	2022-11-27T09:40:05Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:38:59Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5528769841269842 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3181818181818182 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5780989438576987 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.75 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8646979056802773 - name: F1 (macro) type: f1_macro value: 0.8512823829061237 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7136150234741784 - name: F1 (macro) type: f1_macro value: 0.4307168102299423 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5563380281690141 - name: F1 (macro) type: f1_macro value: 0.5317021379158652 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9493635668080963 - name: F1 (macro) type: f1_macro value: 0.8585118986669762 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8232528987778126 - name: F1 (macro) type: f1_macro value: 0.8208236307090298 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3181818181818182 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5780989438576987 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.75 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8646979056802773 - Micro F1 score on CogALexV: 0.7136150234741784 - Micro F1 score on EVALution: 0.5563380281690141 - Micro F1 score on K&H+N: 0.9493635668080963 - Micro F1 score on ROOT09: 0.8232528987778126 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5528769841269842 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
takizawa/xlm-roberta-base-finetuned-panx-all	takizawa	2022-11-27T09:37:33Z	111	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T09:11:26Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2888 \| 1.0 \| 715 \| 0.1779 \| 0.8233 \| \| 0.1437 \| 2.0 \| 1430 \| 0.1570 \| 0.8497 \| \| 0.0931 \| 3.0 \| 2145 \| 0.1608 \| 0.8593 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child	research-backup	2022-11-27T09:36:46Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:35:41Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6220436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6425792106725959 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.86 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8737381346994124 - name: F1 (macro) type: f1_macro value: 0.8631099788352631 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7786384976525821 - name: F1 (macro) type: f1_macro value: 0.5127374486425482 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5557963163596966 - name: F1 (macro) type: f1_macro value: 0.5383373253296296 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9456075676427628 - name: F1 (macro) type: f1_macro value: 0.8417797508933453 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.849890316515199 - name: F1 (macro) type: f1_macro value: 0.8461613177196163 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.6425792106725959 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.86 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8737381346994124 - Micro F1 score on CogALexV: 0.7786384976525821 - Micro F1 score on EVALution: 0.5557963163596966 - Micro F1 score on K&H+N: 0.9456075676427628 - Micro F1 score on ROOT09: 0.849890316515199 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6220436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child	research-backup	2022-11-27T09:35:08Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:34:01Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6000396825396825 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31283422459893045 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5669816564758199 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.718 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8671086334187132 - name: F1 (macro) type: f1_macro value: 0.8580840844941381 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7258215962441315 - name: F1 (macro) type: f1_macro value: 0.4298561084137266 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5460455037919827 - name: F1 (macro) type: f1_macro value: 0.5415508674613216 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9425471238784169 - name: F1 (macro) type: f1_macro value: 0.8536350331018349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.831714196176747 - name: F1 (macro) type: f1_macro value: 0.8272644500838311 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31283422459893045 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5669816564758199 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.3333333333333333 - Accuracy on Google: 0.718 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8671086334187132 - Micro F1 score on CogALexV: 0.7258215962441315 - Micro F1 score on EVALution: 0.5460455037919827 - Micro F1 score on K&H+N: 0.9425471238784169 - Micro F1 score on ROOT09: 0.831714196176747 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6000396825396825 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child	research-backup	2022-11-27T09:30:09Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:28:59Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6335515873015873 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30213903743315507 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2997032640949555 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5403001667593107 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.594 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8618351664908844 - name: F1 (macro) type: f1_macro value: 0.8502274926780541 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7187793427230047 - name: F1 (macro) type: f1_macro value: 0.4123652986554882 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.5145491852283891 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9442860123808862 - name: F1 (macro) type: f1_macro value: 0.8512983083261685 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8122845502977123 - name: F1 (macro) type: f1_macro value: 0.8070813252421513 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.30213903743315507 - Accuracy on SAT: 0.2997032640949555 - Accuracy on BATS: 0.5403001667593107 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.594 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8618351664908844 - Micro F1 score on CogALexV: 0.7187793427230047 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.9442860123808862 - Micro F1 score on ROOT09: 0.8122845502977123 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6335515873015873 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
kejian/final-cond-10-0.1-again-2	kejian	2022-11-27T09:30:05Z	2	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-26T12:19:51Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-10-0.1-again-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kejian/final-cond-10-0.1-again-2 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.1-again-2', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/kjz9xgv1
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child	research-backup	2022-11-27T09:28:24Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-27T09:27:15Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6413492063492063 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.386881600889383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3684210526315789 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752448395359349 - name: F1 (macro) type: f1_macro value: 0.8642915064078064 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.752582159624413 - name: F1 (macro) type: f1_macro value: 0.445502286190483 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.557421451787649 - name: F1 (macro) type: f1_macro value: 0.5384307321042293 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9433122348195033 - name: F1 (macro) type: f1_macro value: 0.8480017401840129 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8429959260419931 - name: F1 (macro) type: f1_macro value: 0.8422459710118972 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.386881600889383 - Accuracy on U2: 0.3684210526315789 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.516 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8752448395359349 - Micro F1 score on CogALexV: 0.752582159624413 - Micro F1 score on EVALution: 0.557421451787649 - Micro F1 score on K&H+N: 0.9433122348195033 - Micro F1 score on ROOT09: 0.8429959260419931 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6413492063492063 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
reallygoodtechdeals/steve-ai-Dialo-GPT-medium	reallygoodtechdeals	2022-11-27T09:07:09Z	117	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:wtfpl", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-27T07:20:01Z	--- license: wtfpl tags: - conversational ---
takizawa/xlm-roberta-base-finetuned-panx-it	takizawa	2022-11-27T08:55:43Z	142	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T08:40:08Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8124233755619126 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - F1: 0.8124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.8193 \| 1.0 \| 70 \| 0.3200 \| 0.7356 \| \| 0.2773 \| 2.0 \| 140 \| 0.2841 \| 0.7882 \| \| 0.1807 \| 3.0 \| 210 \| 0.2630 \| 0.8124 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
takizawa/xlm-roberta-base-finetuned-panx-de-fr	takizawa	2022-11-27T08:20:46Z	112	0	transformers	[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T07:54:20Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2888 \| 1.0 \| 715 \| 0.1779 \| 0.8233 \| \| 0.1437 \| 2.0 \| 1430 \| 0.1570 \| 0.8497 \| \| 0.0931 \| 3.0 \| 2145 \| 0.1608 \| 0.8593 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
takizawa/xlm-roberta-base-finetuned-panx-de	takizawa	2022-11-27T07:44:53Z	110	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T07:21:03Z	--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8648740833380706 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2553 \| 1.0 \| 525 \| 0.1575 \| 0.8279 \| \| 0.1284 \| 2.0 \| 1050 \| 0.1386 \| 0.8463 \| \| 0.0813 \| 3.0 \| 1575 \| 0.1365 \| 0.8649 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
jhakaran1/process-data	jhakaran1	2022-11-27T07:16:54Z	161	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-06T16:01:37Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: process-data results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # process-data This model is a fine-tuned version of [jhakaran1/bert-base-uncased-bert-mlm](https://huggingface.co/jhakaran1/bert-base-uncased-bert-mlm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8087 - Accuracy: 0.6792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.6939 \| 1.0 \| 3907 \| 0.7903 \| 0.6660 \| \| 0.6155 \| 2.0 \| 7814 \| 0.7929 \| 0.6685 \| \| 0.5436 \| 3.0 \| 11721 \| 0.8087 \| 0.6792 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
alexziweiwang/mtl_manual_2601015_epoch1	alexziweiwang	2022-11-27T06:31:44Z	31	0	transformers	[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]	null	2022-11-27T06:15:36Z	--- tags: - generated_from_trainer model-index: - name: mtl_manual_2601015_epoch1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mtl_manual_2601015_epoch1 This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2
minhtoan/t5-small-wikilingua-vietnamese	minhtoan	2022-11-27T06:23:09Z	141	3	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "vi", "dataset:Wikilingua", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-11-24T03:47:40Z	--- language: vi datasets: - Wikilingua tags: - summarization license: mit widget: - text: "Hoa quả và rau thường rẻ hơn khi vào mùa. Thường thì các cửa hàng thực phẩm sẽ giảm giá cho các mặt hàng nông sản đang vào mùa vì họ biết khách hàng luôn chờ đợi những loại nào vào thời gian nào trong năm. Hơn nữa rau quả đúng mùa bao giờ cũng ngon hơn." inference: parameters: max_length: 256 --- # Vietnamese pretrain model for Abstractive Text Summarization task `State-of-the-art lightweights pretrained Transformer-based encoder-decoder model for Vietnamese.` ``Model trained on dataset Wikilingua of vietnamese language with input length = 512, output length = 256`` ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-small-wikilingua_vietnamese") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-small-wikilingua_vietnamese") model.cuda() src = 'Là Thủ tướng khi lạm phát gần 70%, ngân sách thâm hụt, thất nghiệp nhiều, nhưng với tư duy cởi mở, sẵn sàng "không làm được thì từ chức", ông Võ Văn Kiệt đã mang đến nhiều cải cách kinh tế cho Việt Nam. Trong trí nhớ của Chuyên gia kinh tế Phạm Chi Lan, cố Thủ tướng Võ Văn Kiệt là một lãnh đạo có tư duy kinh tế thị trường, chịu khó học hỏi, biết lắng nghe. "Những gì ông thấy tốt cho người dân thì sẽ tiếp thu rất nhanh", bà Lan nói với VnExpress. Còn PGS. TS Trần Minh Trưởng, Viện trưởng Viện Hồ Chí Minh và các lãnh tụ của Đảng (Học viện Chính trị - Hành chính quốc gia) nhận định về ông Võ Văn Kiệt trên Báo Nhân dân như "một trong những người đi đầu trong công cuộc đổi mới ở Việt Nam kể từ năm 1986", "tổng công trình sư nhiều dự án táo bạo". Nhậm chức Thủ tướng giữa muôn vàn khó khăn Năm 1985, sau một thập niên theo mô hình bao cấp, kinh tế Việt Nam ngày càng phụ thuộc vào viện trợ và vay nợ nước ngoài, lên đến 8,5 tỷ ruble (khoảng 3.500 tỷ đồng) và 1,9 tỷ USD (gần 45.600 tỷ đồng). Cứ thêm một năm, mô hình càng trục trặc và lỗi thời. Ngân sách bị thâm hụt và phải bù đắp bằng việc in tiền để chi tiêu. Việc lặp lại sai lầm "giá - lương - tiền" khiến lạm phát phi mã lên gần 775% vào năm 1986 và vẫn ở mức hai chữ số những năm 1990, 1991. Trong khi đó, tỷ lệ thất nghiệp lên đến 12,7%. Những chỉ số đáng báo động đã góp phần thức tỉnh tư duy các nhà lãnh đạo, dẫn đến quyết định đổi mới tại Đại hội VI (tháng 12/1986). Trong đó, đổi mới về kinh tế là một trong những nhiệm vụ trọng tâm, nhằm đưa đất nước thoát khỏi nguy khó. Ông Võ Văn Kiệt, lúc ấy đang là Phó chủ tịch Hội đồng Bộ trưởng, trở thành một lãnh đạo chủ chốt kiến tạo nên công cuộc này.' tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() summary_ids = model.generate( tokenized_text, max_length=256, num_beams=5, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True ) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) output ``` ```python output: Ông Võ Văn Kiệt, lúc ấy đang là Phó chủ tịch Hội đồng Bộ trưởng, trở thành một lãnh đạo chủ chốt kiến tạo nên công cuộc đổi mới ở Việt Nam kể từ năm 1986. Trong khi đó, ông Võ Văn Kiệt đã mang đến nhiều cải cách kinh tế cho Việt Nam. ``` ## Author ` Phan Minh Toan `
venetis/vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred	venetis	2022-11-27T06:04:36Z	188	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-25T19:17:51Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4670 - Accuracy: 0.8781 - Precision: 0.8768 - Recall: 0.8781 - F1: 0.8758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 3.5529 \| 1.0 \| 839 \| 3.3687 \| 0.3096 \| 0.2809 \| 0.3096 \| 0.2246 \| \| 1.7855 \| 2.0 \| 1678 \| 1.6042 \| 0.6378 \| 0.6187 \| 0.6378 \| 0.5996 \| \| 1.1054 \| 3.0 \| 2517 \| 1.0105 \| 0.7556 \| 0.7512 \| 0.7556 \| 0.7385 \| \| 0.8179 \| 4.0 \| 3356 \| 0.7794 \| 0.8033 \| 0.8020 \| 0.8033 \| 0.7934 \| \| 0.6057 \| 5.0 \| 4195 \| 0.6479 \| 0.8294 \| 0.8274 \| 0.8294 \| 0.8212 \| \| 0.4709 \| 6.0 \| 5034 \| 0.5817 \| 0.8478 \| 0.8477 \| 0.8478 \| 0.8428 \| \| 0.3962 \| 7.0 \| 5873 \| 0.5333 \| 0.8571 \| 0.8570 \| 0.8571 \| 0.8527 \| \| 0.346 \| 8.0 \| 6712 \| 0.5073 \| 0.8638 \| 0.8647 \| 0.8638 \| 0.8615 \| \| 0.2772 \| 9.0 \| 7551 \| 0.4881 \| 0.8681 \| 0.8679 \| 0.8681 \| 0.8656 \| \| 0.2136 \| 10.0 \| 8390 \| 0.4777 \| 0.8719 \| 0.8718 \| 0.8719 \| 0.8689 \| \| 0.1937 \| 11.0 \| 9229 \| 0.4737 \| 0.8734 \| 0.8731 \| 0.8734 \| 0.8703 \| \| 0.1754 \| 12.0 \| 10068 \| 0.4604 \| 0.8758 \| 0.8750 \| 0.8758 \| 0.8733 \| \| 0.1111 \| 13.0 \| 10907 \| 0.4561 \| 0.8790 \| 0.8782 \| 0.8790 \| 0.8768 \| \| 0.1128 \| 14.0 \| 11746 \| 0.4519 \| 0.8808 \| 0.8799 \| 0.8808 \| 0.8787 \| \| 0.1018 \| 15.0 \| 12585 \| 0.4497 \| 0.8813 \| 0.8805 \| 0.8813 \| 0.8794 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
amagzari/t5-base-finetuned-samsum-v2	amagzari	2022-11-27T05:42:05Z	106	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-27T05:11:01Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-base-finetuned-samsum-v2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 44.5129 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-samsum-v2 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4881 - Rouge1: 44.5129 - Rouge2: 20.9037 - Rougel: 37.3032 - Rougelsum: 41.2293 - Gen Len: 16.7482 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 1.6551 \| 1.0 \| 1841 \| 1.4881 \| 44.5129 \| 20.9037 \| 37.3032 \| 41.2293 \| 16.7482 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
shibing624/songnet-base-chinese-couplet	shibing624	2022-11-27T05:41:01Z	0	2	null	[ "pytorch", "SongNet", "zh", "Text2Text-Generation", "license:apache-2.0", "region:us" ]	null	2022-11-26T12:05:24Z	--- language: - zh tags: - SongNet - pytorch - zh - Text2Text-Generation license: "apache-2.0" widget: - text: "丹枫江冷人初去" --- # SongNet for Chinese Couplet(songnet-base-chinese-couplet) Model SongNet中文对联仿写模型 `songnet-base-chinese-couplet` evaluate couplet test data： The overall performance of SongNet on couplet test: \|input_text\|predict\| \|:--- \|:--- \| \|一句相思吟岁月，千杯美酒醉风情\|一生只剩诗和酒，满腹无关雪与梅\| 在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，针对性的SongNet网络结构，在语义对仗工整和平仄合律上的效果明显优于T5和GPT2等模型。 SongNet的网络结构： ![arch](songnet-network.png) ## Usage 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持SongNet模型，通过如下命令调用： Install package: ```shell pip install -U textgen ``` ```python import sys sys.path.append('..') from textgen.language_modeling import SongNetModel model = SongNetModel(model_type='songnet', model_name='shibing624/songnet-base-chinese-couplet') sentences = [ "严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。", "<s1><s2>一句相思吟岁月</s>千杯美酒醉风情", "<s1><s2>几树梅花数竿竹</s>一潭秋水半屏山" "<s1><s2>未舍东江开口咏</s>且施妙手点睛来", "<s1><s2>一去二三里</s>烟村四五家", ] print("inputs:", sentences) print("outputs:", model.generate(sentences)) sentences = [ "<s1><s2>一句____月</s>千杯美酒__情", "<s1><s2>一去二三里</s>烟村__家</s>亭台__座</s>八__枝花", ] print("inputs:", sentences) print("outputs:", model.fill_mask(sentences)) ``` output: ```shell inputs: ['严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。', '<s1><s2>一句相思吟岁月</s>千杯美酒醉风情', '<s1><s2>几树梅花数竿竹</s>一潭秋水半屏山<s1><s2>未舍东江开口咏</s>且施妙手点睛来', '<s1><s2>一去二三里</s>烟村四五家'] outputs: ['<bos>盛世欣开新气象</s>春联喜绘大文章</s>春天铺锦笺，宏图更写好山山</s>新篇章</s>新篇章</s>神州高唱好年华</s>', '<bos>一曲琴音添雅韵</s>几回酒醉解愁思</s>', '<bos>三分天下隆中对</s>四面八方九派江山笔底留</s>', '<bos>春深花已老</s>夜静露方浓</s>'] inputs: ['<s1><s2>一句____月</s>千杯美酒__情', '<s1><s2>一去二三里</s>烟村__家</s>亭台__座</s>八__枝花'] outputs: ['<bos>一句佳诗吟盛月</s>千杯美酒祝春情</s>', '<bos>一去二三里</s>烟村百二家</s>亭台十二座</s>八里一枝花</s>'] ``` 模型文件组成： ``` songnet-base-chinese-couplet ├── pytorch_model.bin └── vocab.txt ``` ### 训练数据集 #### 中文对联数据集 - 数据：[对联github](https://github.com/wb14123/couplet-dataset)、[清洗过的对联github](https://github.com/v-zich/couplet-clean-dataset) - 相关内容 - [Huggingface](https://huggingface.co/) - [SongNet paper](https://aclanthology.org/2020.acl-main.68/) - [textgen](https://github.com/shibing624/textgen) 数据格式： ```text head -n 1 couplet_files/couplet/train/in.txt 晚风摇树树还挺 head -n 1 couplet_files/couplet/train/out.txt 晨露润花花更红 ``` 如果需要训练SongNet模型，请参考[https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py](https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py) ## Citation ```latex @software{textgen, author = {Xu Ming}, title = {textgen: Implementation of Text Generation models}, year = {2022}, url = {https://github.com/shibing624/textgen}, } ```
abdulmatinomotoso/pegasus_cnn_news_article_title_25000	abdulmatinomotoso	2022-11-27T05:28:16Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-27T02:01:57Z	--- tags: - generated_from_trainer model-index: - name: pegasus_cnn_news_article_title_25000 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # pegasus_cnn_news_article_title_25000 This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.2711 \| 0.32 \| 500 \| 0.2287 \| \| 0.2009 \| 0.64 \| 1000 \| 0.1924 \| \| 0.2077 \| 0.96 \| 1500 \| 0.1857 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
jutaporn37678/stable-diffusion-v1-0	jutaporn37678	2022-11-27T05:10:46Z	7	1	transformers	[ "transformers", "pytorch", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]	text-to-image	2022-11-27T04:01:04Z	--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: \|- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [๐ค—'s Stable Diffusion with ๐งจDiffusers blog](https://huggingface.co/blog/stable_diffusion). The Stable-Diffusion-v1-4 checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). This weights here are intended to be used with the ๐งจ Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) ## Model Details - Developed by: Robin Rombach, Patrick Esser - Model type: Diffusion-based text-to-image generation model - Language(s): English - License: [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - Model Description: This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - Resources for more information: [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - Cite as: @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples We recommend using [๐ค—'s Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ### PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Run this command to log in with your HF Hub token if you haven't before: ```bash huggingface-cli login ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` Note: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision: ```py import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) pipe.enable_attention_slicing() prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` To swap out the noise scheduler, pass it to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` Note: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to โ€A red cube on top of a blue sphereโ€ - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` after generation of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training Training Data The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) Training Procedure Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide four checkpoints, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - Hardware: 32 x 8 x A100 GPUs - Optimizer: AdamW - Gradient Accumulations: 2 - Batch: 32 x 8 x 2 x 4 = 2048 - Learning rate: warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-variants-scores.jpg) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact Stable Diffusion v1 Estimated Emissions Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - Hardware Type: A100 PCIe 40GB - Hours used: 150000 - Cloud Provider: AWS - Compute Region: US-east - Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid): 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).
pglee/github-issue-classifier	pglee	2022-11-27T05:10:22Z	102	0	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-27T05:04:12Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: github-issue-classifier results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # github-issue-classifier This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0684 - Accuracy: 0.875 - F1: 0.0455 - Precision: 1.0 - Recall: 0.0233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 6 \| 0.0888 \| 0.8720 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 2.0 \| 12 \| 0.0700 \| 0.8720 \| 0.0 \| 0.0 \| 0.0 \| \| No log \| 3.0 \| 18 \| 0.0713 \| 0.8720 \| 0.0851 \| 0.5 \| 0.0465 \| \| No log \| 4.0 \| 24 \| 0.0684 \| 0.875 \| 0.0455 \| 1.0 \| 0.0233 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
razent/SciFive-large-PMC	razent	2022-11-27T04:04:16Z	8	1	transformers	[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "token-classification", "text-classification", "question-answering", "text-generation", "en", "dataset:pmc/open_access", "arxiv:2106.03598", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:05Z	--- language: - en tags: - token-classification - text-classification - question-answering - text2text-generation - text-generation datasets: - pmc/open_access --- # SciFive PMC Large ## Introduction Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598) Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_ ## How to use For more details, do check out [our Github repo](https://github.com/justinphan3110/SciFive). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-PMC") sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ." text = sentence + " </s>" encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") outputs = model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length=256, early_stopping=True ) for output in outputs: line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) print(line) ```
cadaeic/test-digimon-diffusion	cadaeic	2022-11-27T03:41:13Z	0	1	null	[ "license:openrail", "region:us" ]	null	2022-11-27T03:00:59Z	--- license: "openrail" --- Experimental [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) finetune on this [Digimon BLIP Caption Dataset](https://huggingface.co/datasets/ionghin/digimon-blip-captions) Results and output are very rough, trained on slightly less than 900 images
Signorlimone/Caricaturizer	Signorlimone	2022-11-27T02:50:37Z	0	9	null	[ "region:us" ]	null	2022-11-27T02:31:55Z	use pcrc style to kick-in the model
anrilombard/bert-finetuned-ner	anrilombard	2022-11-27T02:46:14Z	128	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-11-27T02:38:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: bert-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
pglee/outputs	pglee	2022-11-27T02:01:47Z	103	1	transformers	[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-26T09:24:02Z	--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: outputs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.8780 - F1: 0.3881 - Precision: 0.5417 - Recall: 0.3023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| No log \| 1.0 \| 6 \| 0.0874 \| 0.8810 \| 0.4118 \| 0.56 \| 0.3256 \| \| No log \| 2.0 \| 12 \| 0.0936 \| 0.8839 \| 0.4000 \| 0.5909 \| 0.3023 \| \| No log \| 3.0 \| 18 \| 0.0922 \| 0.8780 \| 0.3881 \| 0.5417 \| 0.3023 \| \| No log \| 4.0 \| 24 \| 0.0926 \| 0.8780 \| 0.3881 \| 0.5417 \| 0.3023 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1
psrohith98/distilbert-base-uncased-finetuned-emotion	psrohith98	2022-11-27T02:01:11Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-11-21T02:12:03Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.932 - name: F1 type: f1 value: 0.9319377153170552 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1500 - Accuracy: 0.932 - F1: 0.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.7995 \| 1.0 \| 250 \| 0.2679 \| 0.9145 \| 0.9122 \| \| 0.2111 \| 2.0 \| 500 \| 0.1731 \| 0.9265 \| 0.9265 \| \| 0.1399 \| 3.0 \| 750 \| 0.1517 \| 0.938 \| 0.9381 \| \| 0.1118 \| 4.0 \| 1000 \| 0.1500 \| 0.932 \| 0.9319 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
mynameisai/jq	mynameisai	2022-11-27T00:51:12Z	7	0	diffusers	[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2022-11-27T00:48:34Z	--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### JQ Dreambooth model trained by mynameisai with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/mynameisai/jq/resolve/main/sample_images/JQ_(01).png)
jacobthebanana/vit-synthetic-asl-numbers	jacobthebanana	2022-11-26T23:49:08Z	16	0	transformers	[ "transformers", "jax", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-11-26T20:44:22Z	Technical details: - Base model: `google/vit-base-patch16-224` - Dataset: [synthetic ASL number dataset](https://www.kaggle.com/datasets/lexset/synthetic-asl-numbers). - Learning rate: 0.0001 - Effective training batch size: 16 (2 items per TPU core over 8 cores.) > Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
adrien-alloreview/whisper-small-hi	adrien-alloreview	2022-11-26T23:17:31Z	86	0	transformers	[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-26T22:12:16Z	--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 0.0 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---:\| \| 0.0001 \| 40.0 \| 200 \| 0.0001 \| 0.0 \| \| 0.0001 \| 80.0 \| 400 \| 0.0001 \| 0.0 \| ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
amagzari/t5-v1_1-small-finetuned-samsum	amagzari	2022-11-26T22:42:53Z	108	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	summarization	2022-11-26T22:19:28Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-v1_1-small-finetuned-samsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 0.40608242084369006 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-v1_1-small-finetuned-samsum This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0053 - Rouge1: 0.4061 - Rouge2: 0.1804 - Rougel: 0.3478 - Rougelsum: 0.3774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 3.9788 \| 1.0 \| 1842 \| 2.2499 \| 0.3743 \| 0.1569 \| 0.3191 \| 0.3486 \| \| 2.9091 \| 2.0 \| 3684 \| 2.1052 \| 0.3875 \| 0.1680 \| 0.3329 \| 0.3607 \| \| 2.6807 \| 3.0 \| 5526 \| 2.0270 \| 0.4009 \| 0.1778 \| 0.3439 \| 0.3734 \| \| 2.5917 \| 4.0 \| 7368 \| 2.0053 \| 0.4061 \| 0.1804 \| 0.3478 \| 0.3774 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
AtulSingh31/t5-small-finetuned-xsum	AtulSingh31	2022-11-26T22:30:31Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-26T10:09:28Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| No log \| 8.0 \| 51 \| 4.6838 \| 9.554 \| 7.8337 \| 9.3938 \| 9.5417 \| 18.9109 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.7.1 - Tokenizers 0.13.2
huggingtweets/h3xenbrenner2-s4m31p4n-wnbagirlfriend	huggingtweets	2022-11-26T22:07:12Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-11-15T23:41:09Z	--- language: en thumbnail: http://www.huggingtweets.com/h3xenbrenner2-s4m31p4n-wnbagirlfriend/1669500428178/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529675700772302848/uXtYNx_v_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427129645888114693/HsNIpekZ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 & ppigg & jody</div> <div style="text-align: center; font-size: 14px;">@h3xenbrenner2-s4m31p4n-wnbagirlfriend</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 & ppigg & jody. \| Data \| h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 \| ppigg \| jody \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 1302 \| 2858 \| 3144 \| \| Retweets \| 72 \| 975 \| 85 \| \| Short tweets \| 161 \| 569 \| 713 \| \| Tweets kept \| 1069 \| 1314 \| 2346 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uk5vto8w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @h3xenbrenner2-s4m31p4n-wnbagirlfriend's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zuoo2wb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zuoo2wb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/h3xenbrenner2-s4m31p4n-wnbagirlfriend') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About Built by Boris Dayma [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)
ajitjadhav/t5-small-finetuned-t5-summarization_3	ajitjadhav	2022-11-26T21:45:12Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-11-26T17:54:47Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-t5-summarization_3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: train args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.4517 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-t5-summarization_3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.8102 - Rouge1: 24.4517 - Rouge2: 11.7161 - Rougel: 20.205 - Rougelsum: 23.053 - Gen Len: 18.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\| \| 2.0073 \| 1.0 \| 17945 \| 1.8144 \| 24.4341 \| 11.7155 \| 20.1969 \| 23.0364 \| 18.9999 \| \| 1.9855 \| 2.0 \| 35890 \| 1.8102 \| 24.4517 \| 11.7161 \| 20.205 \| 23.053 \| 18.9999 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
jhakaran1/bert-base-uncased-bert-mlm	jhakaran1	2022-11-26T21:35:34Z	199	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-11-26T20:01:48Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-bert-mlm results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-bert-mlm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.2439 \| 1.0 \| 208 \| 1.9789 \| \| 2.008 \| 2.0 \| 416 \| 1.8568 \| \| 1.9535 \| 3.0 \| 624 \| 1.8443 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
kejian/final-cond-25-0.05-again	kejian	2022-11-26T21:11:00Z	2	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-25T18:28:16Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-25-0.05-again results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kejian/final-cond-25-0.05-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0.000475}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-25-0.05-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/27z94dvy
leviethoang/wav2vec2-large-xls-r-300m-turkish-colab	leviethoang	2022-11-26T20:29:44Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-11-26T16:34:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3755 - Wer: 0.3054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.9475 \| 3.67 \| 400 \| 0.7146 \| 0.7507 \| \| 0.4396 \| 7.34 \| 800 \| 0.4402 \| 0.4574 \| \| 0.1955 \| 11.01 \| 1200 \| 0.4027 \| 0.4056 \| \| 0.128 \| 14.68 \| 1600 \| 0.4127 \| 0.3826 \| \| 0.0977 \| 18.35 \| 2000 \| 0.4156 \| 0.3576 \| \| 0.0788 \| 22.02 \| 2400 \| 0.3992 \| 0.3394 \| \| 0.0637 \| 25.69 \| 2800 \| 0.3801 \| 0.3141 \| \| 0.0486 \| 29.36 \| 3200 \| 0.3755 \| 0.3054 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
Euchale/ArcaneSilz	Euchale	2022-11-26T20:17:12Z	0	0	null	[ "region:us" ]	null	2022-11-26T17:40:54Z	A 50:50 merge of Arcane (https://huggingface.co/nitrosocke/Arcane-Diffusion) and Silz (https://huggingface.co/PiyarSquare/stable_diffusion_silz)
kejian/final-cond-10-0.05	kejian	2022-11-26T20:14:37Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]	null	2022-11-25T18:36:06Z	--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-10-0.05 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # kejian/final-cond-10-0.05 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<\|aligned\|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<\|misaligned\|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<\|aligned\|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<\|aligned\|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<\|aligned\|>', '<\|misaligned\|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.05', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3pfuv3vn
amagzari/bart-base-finetuned-cnn_dailymail	amagzari	2022-11-26T19:34:17Z	131	0	transformers	[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	summarization	2022-11-26T11:16:15Z	--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: bart-base-finetuned-cnn_dailymail results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: train args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 0.35105989316705805 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-base-finetuned-cnn_dailymail This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.5396 - Rouge1: 0.3511 - Rouge2: 0.1925 - Rougel: 0.3086 - Rougelsum: 0.3292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\| \| 1.9486 \| 1.0 \| 35890 \| 1.5941 \| 0.3498 \| 0.1893 \| 0.3063 \| 0.3272 \| \| 1.6706 \| 2.0 \| 71780 \| 1.5601 \| 0.3503 \| 0.1916 \| 0.3079 \| 0.3279 \| \| 1.4809 \| 3.0 \| 107670 \| 1.5423 \| 0.3520 \| 0.1923 \| 0.3086 \| 0.3295 \| \| 1.3293 \| 4.0 \| 143560 \| 1.5396 \| 0.3511 \| 0.1925 \| 0.3086 \| 0.3292 \| ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2
research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2	research-backup	2022-11-26T19:32:41Z	108	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-11-26T14:07:12Z	--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6438492063492064 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3649851632047478 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44691495275152865 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.716 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740394756667169 - name: F1 (macro) type: f1_macro value: 0.8618347056127108 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7246478873239436 - name: F1 (macro) type: f1_macro value: 0.4392658356578824 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5243770314192849 - name: F1 (macro) type: f1_macro value: 0.5089556262366394 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9525631216526397 - name: F1 (macro) type: f1_macro value: 0.8724005012414945 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564713256032591 - name: F1 (macro) type: f1_macro value: 0.8510225054091837 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3649851632047478 - Accuracy on BATS: 0.44691495275152865 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.375 - Accuracy on Google: 0.716 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8740394756667169 - Micro F1 score on CogALexV: 0.7246478873239436 - Micro F1 score on EVALution: 0.5243770314192849 - Micro F1 score on K&H+N: 0.9525631216526397 - Micro F1 score on ROOT09: 0.8564713256032591 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6438492063492064 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

tiedaar/summary-longformer-wording

tiedaar

2022-11-27T20:22:11Z

175

0

transformers

[ "transformers", "pytorch", "longformer", "text-classification", "macroeconomics", "automated summary evaluation", "wording", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-19T16:20:03Z

--- language: - "en" thumbnail: "url to a thumbnail used in social sharing" tags: - 'macroeconomics' - 'automated summary evaluation' - 'wording' license: "apache-2.0" metrics: - 'mse' --- # Wording Model This is a longformer model with a regression head designed to predict the wording score of a summary. ## Corpus It was trained on a corpus of 4,233 summaries of 101 sources compiled by Botarleanu et al. (2022). The summaries were graded by expert raters on 6 criteria: Details, Main Point, Cohesion, Paraphrasing, Objective Language, and Language Beyond the Text. A principle component analyis was used to reduce the dimensionality of the outcome variables to two. * **Content** includes Details, Main Point, and Cohesion * **Wording** includes Paraphrasing, Objective Language, and Language Beyond the Text ## Score This model predicts the Wording score. The model to predict the Content score can be found [here](https://huggingface.co/tiedaar/summary-longformer-content). The following diagram illustrates the model architecture: ![model diagram](model_diagram.png) When providing input to the model, the summary and the source should be concatenated using the seperator token \</s>. This allows the model to have access to both the summary and the source to provide more accurate scores. The model reported an R2 of 0.66 on the test set of summaries. ![wording scatter](wording_scatter.png) ## Contact For questions or comments about this model, please contact [[email protected]]([email protected]).

ezzouhri/swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat

ezzouhri

2022-11-27T19:58:04Z

60

0

transformers

[ "transformers", "pytorch", "tensorboard", "swin", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-23T17:49:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy model-index: - name: swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder args: default metrics: - name: Accuracy type: accuracy value: 0.4527928818586258 ---  # swin-tiny-patch4-window7-224-seg-swin-amal-finetuned-eurosat This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 5.6236 - Accuracy: 0.4528 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 200 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 2.1452 | 1.0 | 268 | 2.2034 | 0.2247 | | 0.9905 | 2.0 | 536 | 0.9729 | 0.7126 | | 0.5262 | 3.0 | 804 | 0.5282 | 0.8314 | | 0.36 | 4.0 | 1072 | 0.5618 | 0.8337 | | 0.305 | 5.0 | 1340 | 0.9210 | 0.6535 | | 0.2669 | 6.0 | 1608 | 1.1776 | 0.6317 | | 0.2663 | 7.0 | 1876 | 1.2129 | 0.6290 | | 0.2207 | 8.0 | 2144 | 2.2039 | 0.4068 | | 0.2178 | 9.0 | 2412 | 1.9747 | 0.4740 | | 0.1822 | 10.0 | 2680 | 1.4390 | 0.5526 | | 0.1691 | 11.0 | 2948 | 2.1020 | 0.3814 | | 0.1731 | 12.0 | 3216 | 2.0999 | 0.4251 | | 0.1705 | 13.0 | 3484 | 2.4643 | 0.3700 | | 0.1286 | 14.0 | 3752 | 2.7902 | 0.4345 | | 0.1511 | 15.0 | 4020 | 2.5151 | 0.4165 | | 0.1403 | 16.0 | 4288 | 4.4323 | 0.3099 | | 0.1562 | 17.0 | 4556 | 2.0293 | 0.5096 | | 0.1233 | 18.0 | 4824 | 2.5863 | 0.4236 | | 0.1293 | 19.0 | 5092 | 2.6533 | 0.4506 | | 0.1268 | 20.0 | 5360 | 2.1429 | 0.4998 | | 0.1464 | 21.0 | 5628 | 2.3014 | 0.5470 | | 0.1507 | 22.0 | 5896 | 2.3857 | 0.4911 | | 0.1285 | 23.0 | 6164 | 1.4228 | 0.6406 | | 0.1364 | 24.0 | 6432 | 3.6147 | 0.4842 | | 0.1209 | 25.0 | 6700 | 2.4210 | 0.4896 | | 0.1321 | 26.0 | 6968 | 2.7809 | 0.5344 | | 0.0944 | 27.0 | 7236 | 3.5598 | 0.4226 | | 0.1013 | 28.0 | 7504 | 4.0793 | 0.3905 | | 0.1243 | 29.0 | 7772 | 4.5733 | 0.3443 | | 0.0962 | 30.0 | 8040 | 2.9494 | 0.4199 | | 0.0974 | 31.0 | 8308 | 3.1012 | 0.4496 | | 0.113 | 32.0 | 8576 | 3.9522 | 0.3764 | | 0.1067 | 33.0 | 8844 | 1.9792 | 0.6053 | | 0.095 | 34.0 | 9112 | 2.8795 | 0.5302 | | 0.1015 | 35.0 | 9380 | 5.9943 | 0.2941 | | 0.0912 | 36.0 | 9648 | 2.9536 | 0.5242 | | 0.1193 | 37.0 | 9916 | 3.5187 | 0.4226 | | 0.0906 | 38.0 | 10184 | 3.0049 | 0.5114 | | 0.1109 | 39.0 | 10452 | 2.6823 | 0.5675 | | 0.0903 | 40.0 | 10720 | 4.7151 | 0.3109 | | 0.0846 | 41.0 | 10988 | 3.1118 | 0.3880 | | 0.0986 | 42.0 | 11256 | 3.9827 | 0.4792 | | 0.1244 | 43.0 | 11524 | 4.7544 | 0.2860 | | 0.1039 | 44.0 | 11792 | 4.4297 | 0.3178 | | 0.077 | 45.0 | 12060 | 5.8973 | 0.3524 | | 0.0718 | 46.0 | 12328 | 6.0338 | 0.3033 | | 0.0838 | 47.0 | 12596 | 6.3524 | 0.3507 | | 0.0935 | 48.0 | 12864 | 3.8675 | 0.4194 | | 0.0922 | 49.0 | 13132 | 4.7731 | 0.3129 | | 0.0903 | 50.0 | 13400 | 3.5435 | 0.4115 | | 0.0927 | 51.0 | 13668 | 4.7606 | 0.4234 | | 0.0757 | 52.0 | 13936 | 3.4110 | 0.4436 | | 0.0738 | 53.0 | 14204 | 6.3143 | 0.3648 | | 0.076 | 54.0 | 14472 | 4.9524 | 0.3604 | | 0.0951 | 55.0 | 14740 | 5.5633 | 0.3680 | | 0.1078 | 56.0 | 15008 | 5.9219 | 0.3082 | | 0.0991 | 57.0 | 15276 | 4.9457 | 0.3344 | | 0.0968 | 58.0 | 15544 | 4.0270 | 0.4271 | | 0.0883 | 59.0 | 15812 | 5.3006 | 0.3574 | | 0.0728 | 60.0 | 16080 | 6.9527 | 0.3119 | | 0.0803 | 61.0 | 16348 | 2.9117 | 0.5 | | 0.1022 | 62.0 | 16616 | 5.1631 | 0.3487 | | 0.1155 | 63.0 | 16884 | 5.2602 | 0.3453 | | 0.0737 | 64.0 | 17152 | 6.5281 | 0.3129 | | 0.0735 | 65.0 | 17420 | 4.9847 | 0.3945 | | 0.0948 | 66.0 | 17688 | 3.6684 | 0.4330 | | 0.0765 | 67.0 | 17956 | 4.2188 | 0.4076 | | 0.0597 | 68.0 | 18224 | 3.0067 | 0.5208 | | 0.0866 | 69.0 | 18492 | 3.8993 | 0.4412 | | 0.0825 | 70.0 | 18760 | 3.9058 | 0.3945 | | 0.0897 | 71.0 | 19028 | 4.5870 | 0.3932 | | 0.0687 | 72.0 | 19296 | 4.2837 | 0.3744 | | 0.0774 | 73.0 | 19564 | 4.9028 | 0.3596 | | 0.0755 | 74.0 | 19832 | 5.1321 | 0.3356 | | 0.0728 | 75.0 | 20100 | 4.5533 | 0.3851 | | 0.0753 | 76.0 | 20368 | 4.9765 | 0.3898 | | 0.0582 | 77.0 | 20636 | 5.1959 | 0.3777 | | 0.0714 | 78.0 | 20904 | 4.6735 | 0.3707 | | 0.0928 | 79.0 | 21172 | 3.6359 | 0.4639 | | 0.0593 | 80.0 | 21440 | 5.1507 | 0.3841 | | 0.0972 | 81.0 | 21708 | 5.3122 | 0.3356 | | 0.0903 | 82.0 | 21976 | 3.5833 | 0.4310 | | 0.074 | 83.0 | 22244 | 2.3014 | 0.6349 | | 0.0651 | 84.0 | 22512 | 3.8229 | 0.4387 | | 0.0682 | 85.0 | 22780 | 3.5292 | 0.4627 | | 0.0543 | 86.0 | 23048 | 4.0542 | 0.4266 | | 0.0776 | 87.0 | 23316 | 3.8799 | 0.5240 | | 0.0868 | 88.0 | 23584 | 4.1896 | 0.4750 | | 0.0711 | 89.0 | 23852 | 3.1013 | 0.5381 | | 0.077 | 90.0 | 24120 | 2.9132 | 0.5650 | | 0.0672 | 91.0 | 24388 | 4.4834 | 0.3806 | | 0.0737 | 92.0 | 24656 | 4.0161 | 0.5116 | | 0.0868 | 93.0 | 24924 | 2.9386 | 0.4956 | | 0.0778 | 94.0 | 25192 | 4.4806 | 0.4478 | | 0.0586 | 95.0 | 25460 | 5.0668 | 0.4313 | | 0.0713 | 96.0 | 25728 | 6.4632 | 0.3043 | | 0.0897 | 97.0 | 25996 | 5.0227 | 0.4674 | | 0.073 | 98.0 | 26264 | 3.6177 | 0.4854 | | 0.0775 | 99.0 | 26532 | 5.5003 | 0.3702 | | 0.0709 | 100.0 | 26800 | 5.6101 | 0.3863 | | 0.078 | 101.0 | 27068 | 4.3187 | 0.4338 | | 0.0702 | 102.0 | 27336 | 4.8467 | 0.4545 | | 0.0498 | 103.0 | 27604 | 3.9094 | 0.4511 | | 0.0785 | 104.0 | 27872 | 4.0952 | 0.3836 | | 0.0767 | 105.0 | 28140 | 3.2816 | 0.4909 | | 0.0611 | 106.0 | 28408 | 5.2239 | 0.4221 | | 0.0753 | 107.0 | 28676 | 4.2586 | 0.4493 | | 0.0758 | 108.0 | 28944 | 3.6094 | 0.4938 | | 0.0951 | 109.0 | 29212 | 6.1982 | 0.3453 | | 0.086 | 110.0 | 29480 | 6.4891 | 0.3191 | | 0.0701 | 111.0 | 29748 | 5.8145 | 0.3235 | | 0.0772 | 112.0 | 30016 | 3.7809 | 0.5133 | | 0.0705 | 113.0 | 30284 | 4.9590 | 0.4372 | | 0.0602 | 114.0 | 30552 | 5.5669 | 0.3959 | | 0.0671 | 115.0 | 30820 | 4.4897 | 0.4429 | | 0.0692 | 116.0 | 31088 | 5.1358 | 0.3319 | | 0.0675 | 117.0 | 31356 | 5.0169 | 0.4226 | | 0.0626 | 118.0 | 31624 | 5.6420 | 0.4170 | | 0.0537 | 119.0 | 31892 | 5.1601 | 0.3683 | | 0.0543 | 120.0 | 32160 | 5.4460 | 0.3663 | | 0.0601 | 121.0 | 32428 | 7.2877 | 0.2981 | | 0.0743 | 122.0 | 32696 | 6.5134 | 0.3337 | | 0.0558 | 123.0 | 32964 | 4.4690 | 0.4469 | | 0.0396 | 124.0 | 33232 | 4.4964 | 0.4212 | | 0.0704 | 125.0 | 33500 | 4.5766 | 0.4011 | | 0.0547 | 126.0 | 33768 | 4.0679 | 0.4538 | | 0.0643 | 127.0 | 34036 | 3.3335 | 0.4545 | | 0.0709 | 128.0 | 34304 | 3.6568 | 0.4750 | | 0.0932 | 129.0 | 34572 | 4.7978 | 0.4614 | | 0.0522 | 130.0 | 34840 | 6.1548 | 0.3366 | | 0.0592 | 131.0 | 35108 | 5.0728 | 0.4409 | | 0.0528 | 132.0 | 35376 | 5.5127 | 0.4088 | | 0.087 | 133.0 | 35644 | 4.5838 | 0.3900 | | 0.0566 | 134.0 | 35912 | 4.8733 | 0.3683 | | 0.0474 | 135.0 | 36180 | 3.4370 | 0.4348 | | 0.0517 | 136.0 | 36448 | 4.5547 | 0.3908 | | 0.0627 | 137.0 | 36716 | 4.7011 | 0.4048 | | 0.0693 | 138.0 | 36984 | 4.8039 | 0.4419 | | 0.0753 | 139.0 | 37252 | 4.6905 | 0.4674 | | 0.0542 | 140.0 | 37520 | 4.4103 | 0.4278 | | 0.0629 | 141.0 | 37788 | 4.5332 | 0.4402 | | 0.0636 | 142.0 | 38056 | 4.4822 | 0.4288 | | 0.0551 | 143.0 | 38324 | 5.3970 | 0.3885 | | 0.0677 | 144.0 | 38592 | 4.9337 | 0.3811 | | 0.037 | 145.0 | 38860 | 4.7588 | 0.3979 | | 0.0426 | 146.0 | 39128 | 4.5055 | 0.4110 | | 0.0624 | 147.0 | 39396 | 4.9575 | 0.3722 | | 0.0799 | 148.0 | 39664 | 3.9235 | 0.4350 | | 0.0643 | 149.0 | 39932 | 3.2063 | 0.5297 | | 0.0687 | 150.0 | 40200 | 3.1733 | 0.5692 | | 0.0652 | 151.0 | 40468 | 3.8738 | 0.5178 | | 0.078 | 152.0 | 40736 | 2.7892 | 0.5319 | | 0.0644 | 153.0 | 41004 | 3.4909 | 0.5185 | | 0.0639 | 154.0 | 41272 | 3.7233 | 0.5005 | | 0.0517 | 155.0 | 41540 | 4.9475 | 0.4152 | | 0.0546 | 156.0 | 41808 | 5.0784 | 0.4251 | | 0.0704 | 157.0 | 42076 | 5.3511 | 0.3987 | | 0.0753 | 158.0 | 42344 | 5.0345 | 0.4538 | | 0.0504 | 159.0 | 42612 | 4.1655 | 0.4701 | | 0.0645 | 160.0 | 42880 | 3.9242 | 0.4936 | | 0.0543 | 161.0 | 43148 | 4.8499 | 0.4533 | | 0.0592 | 162.0 | 43416 | 5.1871 | 0.4345 | | 0.0716 | 163.0 | 43684 | 5.4487 | 0.4325 | | 0.0613 | 164.0 | 43952 | 4.3626 | 0.4711 | | 0.0616 | 165.0 | 44220 | 4.8649 | 0.4807 | | 0.0506 | 166.0 | 44488 | 4.1038 | 0.5133 | | 0.0802 | 167.0 | 44756 | 5.0038 | 0.4889 | | 0.0672 | 168.0 | 45024 | 6.3643 | 0.4009 | | 0.0562 | 169.0 | 45292 | 6.1359 | 0.4372 | | 0.0367 | 170.0 | 45560 | 5.6726 | 0.4340 | | 0.0687 | 171.0 | 45828 | 5.2015 | 0.4254 | | 0.061 | 172.0 | 46096 | 5.0398 | 0.4491 | | 0.0444 | 173.0 | 46364 | 5.8819 | 0.4414 | | 0.0685 | 174.0 | 46632 | 6.0729 | 0.4263 | | 0.0548 | 175.0 | 46900 | 5.6388 | 0.4298 | | 0.084 | 176.0 | 47168 | 6.3042 | 0.4090 | | 0.0575 | 177.0 | 47436 | 6.3381 | 0.4019 | | 0.0678 | 178.0 | 47704 | 6.3679 | 0.4100 | | 0.0445 | 179.0 | 47972 | 6.3634 | 0.4152 | | 0.081 | 180.0 | 48240 | 6.4057 | 0.4051 | | 0.0643 | 181.0 | 48508 | 6.6593 | 0.3648 | | 0.0497 | 182.0 | 48776 | 6.7469 | 0.3799 | | 0.0568 | 183.0 | 49044 | 5.9056 | 0.4221 | | 0.0513 | 184.0 | 49312 | 6.4656 | 0.4046 | | 0.0496 | 185.0 | 49580 | 6.1444 | 0.4140 | | 0.0524 | 186.0 | 49848 | 5.9295 | 0.4357 | | 0.0746 | 187.0 | 50116 | 5.6245 | 0.4612 | | 0.0489 | 188.0 | 50384 | 5.6278 | 0.4476 | | 0.0589 | 189.0 | 50652 | 5.6629 | 0.4595 | | 0.0365 | 190.0 | 50920 | 5.9882 | 0.4392 | | 0.0456 | 191.0 | 51188 | 6.0186 | 0.4496 | | 0.0486 | 192.0 | 51456 | 5.6916 | 0.4427 | | 0.0658 | 193.0 | 51724 | 5.7638 | 0.4461 | | 0.0599 | 194.0 | 51992 | 5.7886 | 0.4387 | | 0.0522 | 195.0 | 52260 | 5.7112 | 0.4464 | | 0.0556 | 196.0 | 52528 | 5.7411 | 0.4419 | | 0.0681 | 197.0 | 52796 | 5.6449 | 0.4516 | | 0.0649 | 198.0 | 53064 | 5.6714 | 0.4508 | | 0.0582 | 199.0 | 53332 | 5.6241 | 0.4521 | | 0.0727 | 200.0 | 53600 | 5.6236 | 0.4528 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.1+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

essayproj/cohesion

essayproj

2022-11-27T18:53:32Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-27T18:47:13Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: cohesion results: [] ---  # cohesion This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.7082 - Accuracy: 0.4 - F1: 0.2857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

vipintom/my_awesome_model

vipintom

2022-11-27T18:16:11Z

64

0

transformers

[ "transformers", "tf", "distilbert", "text-classification", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-27T16:15:05Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: vipintom/my_awesome_model results: [] ---  # vipintom/my_awesome_model This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.0683 - Validation Loss: 0.2223 - Train Accuracy: 0.9294 - Epoch: 2 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Accuracy | Epoch | |:----------:|:---------------:|:--------------:|:-----:| | 0.2515 | 0.1947 | 0.9256 | 0 | | 0.1354 | 0.2041 | 0.9253 | 1 | | 0.0683 | 0.2223 | 0.9294 | 2 | ### Framework versions - Transformers 4.24.0 - TensorFlow 2.9.2 - Datasets 2.7.1 - Tokenizers 0.13.2

k3nneth/finetuning-sentiment-model-3000-samples

k3nneth

2022-11-27T17:36:54Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:imdb", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-25T20:16:27Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb config: plain_text split: train args: plain_text metrics: - name: Accuracy type: accuracy value: 0.87 - name: F1 type: f1 value: 0.8712871287128714 ---  # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3046 - Accuracy: 0.87 - F1: 0.8713 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pucpr/biobertpt-clin

pucpr

2022-11-27T16:55:14Z

150

10

transformers

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: "pt" widget: - text: "O paciente recebeu [MASK] do hospital." - text: "O médico receitou a medicação para controlar a [MASK]." thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # BioBERTpt - Portuguese Clinical and Biomedical BERT The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. This model card describes the BioBERTpt(clin) model, a clinical version of BioBERTpt, trained on clinical narratives from electronic health records from Brazilian Hospitals. ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-clin") model = AutoModel.from_pretrained("pucpr/biobertpt-clin") ``` ## More Information Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).

pucpr/biobertpt-bio

pucpr

2022-11-27T16:54:50Z

162

6

transformers

[ "transformers", "pytorch", "tf", "jax", "bert", "fill-mask", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-03-02T23:29:05Z

--- language: "pt" widget: - text: "O principal [MASK] da COVID-19 é tosse seca." - text: "O vírus da gripe apresenta um [MASK] constituído por segmentos de ácido ribonucleico." thumbnail: "https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" --- <img src="https://raw.githubusercontent.com/HAILab-PUCPR/BioBERTpt/master/images/logo-biobertpr1.png" alt="Logo BioBERTpt"> # BioBERTpt - Portuguese Clinical and Biomedical BERT The [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) paper contains clinical and biomedical BERT-based models for Portuguese Language, initialized with BERT-Multilingual-Cased & trained on clinical notes and biomedical literature. This model card describes the BioBERTpt(bio) model, a biomedical version of BioBERTpt, trained on Portuguese biomedical literature from scientific papers from Pubmed and Scielo. ## How to use the model Load the model via the transformers library: ``` from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("pucpr/biobertpt-bio") model = AutoModel.from_pretrained("pucpr/biobertpt-bio") ``` ## More Information Refer to the original paper, [BioBERTpt - A Portuguese Neural Language Model for Clinical Named Entity Recognition](https://www.aclweb.org/anthology/2020.clinicalnlp-1.7/) for additional details and performance on Portuguese NER tasks. ## Acknowledgements This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001. ## Citation ``` @inproceedings{schneider-etal-2020-biobertpt, title = "{B}io{BERT}pt - A {P}ortuguese Neural Language Model for Clinical Named Entity Recognition", author = "Schneider, Elisa Terumi Rubel and de Souza, Jo{\~a}o Vitor Andrioli and Knafou, Julien and Oliveira, Lucas Emanuel Silva e and Copara, Jenny and Gumiel, Yohan Bonescki and Oliveira, Lucas Ferro Antunes de and Paraiso, Emerson Cabrera and Teodoro, Douglas and Barra, Cl{\'a}udia Maria Cabral Moro", booktitle = "Proceedings of the 3rd Clinical Natural Language Processing Workshop", month = nov, year = "2020", address = "Online", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2020.clinicalnlp-1.7", pages = "65--72", abstract = "With the growing number of electronic health record data, clinical NLP tasks have become increasingly relevant to unlock valuable information from unstructured clinical text. Although the performance of downstream NLP tasks, such as named-entity recognition (NER), in English corpus has recently improved by contextualised language models, less research is available for clinical texts in low resource languages. Our goal is to assess a deep contextual embedding model for Portuguese, so called BioBERTpt, to support clinical and biomedical NER. We transfer learned information encoded in a multilingual-BERT model to a corpora of clinical narratives and biomedical-scientific papers in Brazilian Portuguese. To evaluate the performance of BioBERTpt, we ran NER experiments on two annotated corpora containing clinical narratives and compared the results with existing BERT models. Our in-domain model outperformed the baseline model in F1-score by 2.72{\%}, achieving higher performance in 11 out of 13 assessed entities. We demonstrate that enriching contextual embedding models with domain literature can play an important role in improving performance for specific NLP tasks. The transfer learning process enhanced the Portuguese biomedical NER model by reducing the necessity of labeled data and the demand for retraining a whole new model.", } ``` ## Questions? Post a Github issue on the [BioBERTpt repo](https://github.com/HAILab-PUCPR/BioBERTpt).

rifkat/wav2vec2-large-xls-r-300m-uz

rifkat

2022-11-27T16:53:56Z

210

1

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-09-11T10:24:32Z

--- license: apache-2.0 --- Ushbu model GptUZ loyihasida, uzbek tilidagi ovozli ma'lumotlarni tanib olishda ishlatiladi. Model ochiq va yopiq ma'lumotlar asosida o'qitildi.

kejian/final-cond-25-0.01

kejian

2022-11-27T15:09:29Z

1

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-26T12:24:35Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-25-0.01 results: [] ---  # kejian/final-cond-25-0.01 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.01, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.000475}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-25-0.01', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/2gqinonx

mesolitica/finetune-paraphrase-t5-small-standard-bahasa-cased

mesolitica

2022-11-27T13:59:26Z

108

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-25T16:30:46Z

--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-small-standard-bahasa-cased Finetuned T5 small on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 37.598729045833316, '_mean': -1.0, '_ci': -1.0, '_verbose': '62.6/42.5/33.2/27.0 (BP = 0.957 ratio = 0.958 hyp_len = 96781 ref_len = 101064)', 'bp': 0.9567103919247614, 'counts': [60539, 38753, 28443, 21680], 'totals': [96781, 91237, 85693, 80149], 'sys_len': 96781, 'ref_len': 101064, 'precisions': [62.55256713611143, 42.47509234192268, 33.19174261608299, 27.049620082596164], 'prec_str': '62.6/42.5/33.2/27.0', 'ratio': 0.9576209134805668} ```

mesolitica/finetune-paraphrase-t5-base-standard-bahasa-cased

mesolitica

2022-11-27T13:59:04Z

110

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-26T01:52:56Z

--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-base-standard-bahasa-cased Finetuned T5 base on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 35.95965899952292, '_mean': -1.0, '_ci': -1.0, '_verbose': '61.7/41.3/32.0/25.8 (BP = 0.944 ratio = 0.946 hyp_len = 95593 ref_len = 101064)', 'bp': 0.9443747373110852, 'counts': [59014, 37157, 27016, 20383], 'totals': [95593, 90049, 84505, 78961], 'sys_len': 95593, 'ref_len': 101064, 'precisions': [61.73464584226878, 41.263090095392506, 31.969705934560086, 25.81400944770203], 'prec_str': '61.7/41.3/32.0/25.8', 'ratio': 0.9458659859099184} ```

Itsme33/Modelx

Itsme33

2022-11-27T13:55:19Z

0

null

[ "arxiv:2202.00512", "arxiv:2112.10752", "license:bigscience-bloom-rail-1.0", "region:us" ]

null

2022-11-19T19:06:38Z

--- license: bigscience-bloom-rail-1.0 --- tags: - stable-diffusion - text-to-image inference: false --- # Stable Diffusion v2 Model Card This model card focuses on the model associated with the Stable Diffusion v2 model, available [here](https://github.com/Stability-AI/stablediffusion). This `stable-diffusion-2` model is resumed from [stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base) (`512-base-ema.ckpt`) and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on `768x768` images. ![image](https://github.com/Stability-AI/stablediffusion/blob/main/assets/stable-samples/txt2img/768/merged-0005.png?raw=true) - Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `768-v-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/768-v-ema.ckpt). - Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2#examples) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL) - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)). - **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner. ```bash pip install --upgrade git+https://github.com/huggingface/diffusers.git transformers accelerate scipy ``` Running the pipeline (if you don't swap the scheduler it will run with the default DDIM, in this example we are swapping it to EulerDiscreteScheduler): *.7z filter=lfs diff=lfs merge=lfs -text *.arrow filter=lfs diff=lfs merge=lfs -text *.bin filter=lfs diff=lfs merge=lfs -text *.bz2 filter=lfs diff=lfs merge=lfs -text *.ftz filter=lfs diff=lfs merge=lfs -text *.gz filter=lfs diff=lfs merge=lfs -text *.h5 filter=lfs diff=lfs merge=lfs -text *.joblib filter=lfs diff=lfs merge=lfs -text *.lfs.* filter=lfs diff=lfs merge=lfs -text *.mlmodel filter=lfs diff=lfs merge=lfs -text *.model filter=lfs diff=lfs merge=lfs -text *.msgpack filter=lfs diff=lfs merge=lfs -text *.npy filter=lfs diff=lfs merge=lfs -text *.npz filter=lfs diff=lfs merge=lfs -text *.onnx filter=lfs diff=lfs merge=lfs -text *.ot filter=lfs diff=lfs merge=lfs -text *.parquet filter=lfs diff=lfs merge=lfs -text *.pb filter=lfs diff=lfs merge=lfs -text *.pickle filter=lfs diff=lfs merge=lfs -text *.pkl filter=lfs diff=lfs merge=lfs -text *.pt filter=lfs diff=lfs merge=lfs -text *.pth filter=lfs diff=lfs merge=lfs -text *.rar filter=lfs diff=lfs merge=lfs -text *.safetensors filter=lfs diff=lfs merge=lfs -text saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.tar.* filter=lfs diff=lfs merge=lfs -text *.tflite filter=lfs diff=lfs merge=lfs -text *.tgz filter=lfs diff=lfs merge=lfs -text *.wasm filter=lfs diff=lfs merge=lfs -text *.xz filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text

mesolitica/finetune-paraphrase-t5-tiny-standard-bahasa-cased

mesolitica

2022-11-27T13:54:24Z

109

0

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "paraphrase", "ms", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-25T17:24:20Z

--- language: - ms tags: - paraphrase metrics: - sacrebleu --- # finetune-paraphrase-t5-tiny-standard-bahasa-cased Finetuned T5 tiny on MS paraphrase tasks. ## Dataset 1. translated PAWS, https://huggingface.co/datasets/mesolitica/translated-PAWS 2. translated MRPC, https://huggingface.co/datasets/mesolitica/translated-MRPC 3. translated ParaSCI, https://huggingface.co/datasets/mesolitica/translated-paraSCI ## Finetune details 1. Finetune using single RTX 3090 Ti. Scripts at https://github.com/huseinzol05/malaya/tree/master/session/paraphrase/hf-t5 ## Supported prefix 1. `parafrasa: {string}`, for MS paraphrase. ## Evaluation Evaluated on MRPC validation set and ParaSCI Arxiv test set. ``` {'name': 'BLEU', 'score': 36.92696648298233, '_mean': -1.0, '_ci': -1.0, '_verbose': '62.5/42.3/33.0/26.9 (BP = 0.943 ratio = 0.945 hyp_len = 95496 ref_len = 101064)', 'bp': 0.9433611337299734, 'counts': [59650, 38055, 27875, 21217], 'totals': [95496, 89952, 84408, 78864], 'sys_len': 95496, 'ref_len': 101064, 'precisions': [62.46334925023038, 42.30589647812167, 33.02412093640413, 26.90327652667884], 'prec_str': '62.5/42.3/33.0/26.9', 'ratio': 0.944906198052719} ```

Yagorka/ddpm-butterflies-256

Yagorka

2022-11-27T13:06:28Z

0

diffusers

[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]

null

2022-11-20T19:04:19Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] ---  # ddpm-butterflies-256 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 2 - eval_batch_size: 8 - gradient_accumulation_steps: 1 - optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None - lr_scheduler: None - lr_warmup_steps: 500 - ema_inv_gamma: None - ema_inv_gamma: None - ema_inv_gamma: None - mixed_precision: fp16 ### Training results 📈 [TensorBoard logs](https://huggingface.co/Yagorka/ddpm-butterflies-256/tensorboard?#scalars)

sd-concepts-library/Rain_and_Monsters

sd-concepts-library

2022-11-27T13:02:02Z

0

10

null

[ "license:openrail", "region:us" ]

null

2022-11-27T12:50:11Z

--- license: openrail --- --- This is Rain and Monsters, my first embedding created for SD 2 - 768 using Gradient accumulation steps. Due to my technical limitations the embedding was trainned at a resolution of 640x640 with not many steps. Take it as a sample of what can be obtained. Renders in Automatic1111 ![04963-1815323483-photography , close view of wolverine AND (monster_0.5), in a rainy night, very detailed, by prue640v2 , movie still, water drop.png](https://s3.amazonaws.com/moonup/production/uploads/1669553747033-630406f20907b9a115c620e6.png) photography , close view of wolverine AND (monster:0.5), in a rainy night, very detailed, by prue640v2 , movie still, water drops, splashes Negative prompt: blur Steps: 26, Sampler: Euler a, CFG scale: 7, Seed: 1815323483, Size: 768x768, Model hash: 2c02b20a, ENSD: -1 ![04906-4269742702-by prue640v2 very detailed.png](https://s3.amazonaws.com/moonup/production/uploads/1669553847018-630406f20907b9a115c620e6.png) by prue640v2 very detailed Steps: 31, Sampler: Euler a, CFG scale: 7, Seed: 4269742702, Size: 640x640, Model hash: 2c02b20a, ENSD: -1

alexziweiwang/mtl_manual_fGroup010304

alexziweiwang

2022-11-27T12:42:00Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-27T12:25:20Z

--- tags: - generated_from_trainer model-index: - name: mtl_manual_fGroup010304 results: [] ---  # mtl_manual_fGroup010304 This model is a fine-tuned version of [alexziweiwang/mtl_manual_mGroup0304](https://huggingface.co/alexziweiwang/mtl_manual_mGroup0304) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

alexziweiwang/mtl_manual_270039_epoch1

alexziweiwang

2022-11-27T12:13:28Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-27T08:41:55Z

--- tags: - generated_from_trainer model-index: - name: mtl_manual_270039_epoch1 results: [] ---  # mtl_manual_270039_epoch1 This is the check point of uaspeech-trianed-foundation model, plus torgo M01 (train-test-valid left out) group split trained This model is a fine-tuned version of [alexziweiwang/mtl_manual_270012_epoch1](https://huggingface.co/alexziweiwang/mtl_manual_270012_epoch1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

BeeBeaver/q-Taxi-v3

BeeBeaver

2022-11-27T12:05:54Z

0

null

[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-11-27T12:05:20Z

--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # **Q-Learning** Agent playing **Taxi-v3** This is a trained model of a **Q-Learning** agent playing **Taxi-v3** . ## Usage ```python model = load_from_hub(repo_id="BeeBeaver/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```

jw4169/wav2vec2-large-xls-r-300m-kr-jw4169

jw4169

2022-11-27T11:57:14Z

18

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:fleurs", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-25T21:07:46Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - fleurs metrics: - wer model-index: - name: wav2vec2-large-xls-r-300m-kr-jw4169 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: fleurs type: fleurs config: ko_kr split: train args: ko_kr metrics: - name: Wer type: wer value: 0.519593179778642 ---  # wav2vec2-large-xls-r-300m-kr-jw4169 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the fleurs dataset. It achieves the following results on the evaluation set: - Loss: 0.9752 - Wer: 0.5196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 35.084 | 1.39 | 200 | 6.8536 | 1.0 | | 4.853 | 2.78 | 400 | 4.6246 | 1.0 | | 4.5491 | 4.17 | 600 | 4.3815 | 1.0 | | 2.799 | 5.55 | 800 | 1.7402 | 0.8642 | | 1.3872 | 6.94 | 1000 | 1.2019 | 0.7448 | | 0.9599 | 8.33 | 1200 | 1.0594 | 0.7134 | | 0.675 | 9.72 | 1400 | 0.9321 | 0.6404 | | 0.4775 | 11.11 | 1600 | 0.9088 | 0.5911 | | 0.3479 | 12.5 | 1800 | 0.9430 | 0.6010 | | 0.2712 | 13.89 | 2000 | 0.8948 | 0.5854 | | 0.2283 | 15.28 | 2200 | 0.9009 | 0.5495 | | 0.1825 | 16.67 | 2400 | 0.9079 | 0.5501 | | 0.161 | 18.06 | 2600 | 0.9518 | 0.5390 | | 0.1394 | 19.44 | 2800 | 0.9529 | 0.5399 | | 0.1266 | 20.83 | 3000 | 0.9505 | 0.5283 | | 0.1102 | 22.22 | 3200 | 0.9748 | 0.5328 | | 0.101 | 23.61 | 3400 | 0.9593 | 0.5316 | | 0.0907 | 25.0 | 3600 | 0.9832 | 0.5292 | | 0.0833 | 26.39 | 3800 | 0.9773 | 0.5181 | | 0.0781 | 27.78 | 4000 | 0.9736 | 0.5163 | | 0.0744 | 29.17 | 4200 | 0.9752 | 0.5196 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.10.0+cu102 - Datasets 2.6.1 - Tokenizers 0.13.1

alexziweiwang/mtl_manual_m02_4of4

alexziweiwang

2022-11-27T11:55:21Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-27T11:37:57Z

--- tags: - generated_from_trainer model-index: - name: mtl_manual_m02_4of4 results: [] ---  # mtl_manual_m02_4of4 This model is a fine-tuned version of [alexziweiwang/mtl_manual_m02_3of4](https://huggingface.co/alexziweiwang/mtl_manual_m02_3of4) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent

research-backup

2022-11-27T11:55:16Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:54:13Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7862698412698412 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3887240356083086 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.46581434130072263 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.712 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3958333333333333 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9002561398222089 - name: F1 (macro) type: f1_macro value: 0.8933774886390773 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5778373004618788 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6294691224268689 - name: F1 (macro) type: f1_macro value: 0.6170667717596963 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8833563430349135 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8661861485427765 - name: F1 (macro) type: f1_macro value: 0.8629818809654138 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3887240356083086 - Accuracy on BATS: 0.46581434130072263 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.3958333333333333 - Accuracy on Google: 0.712 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9002561398222089 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6294691224268689 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8661861485427765 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7862698412698412 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent

research-backup

2022-11-27T11:51:53Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:50:42Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8185912698412698 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4358288770053476 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4391691394658754 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.519177320733741 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.738 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42105263157894735 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39814814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.906282959168299 - name: F1 (macro) type: f1_macro value: 0.8996950498562719 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8178403755868544 - name: F1 (macro) type: f1_macro value: 0.6064939255543282 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6267605633802817 - name: F1 (macro) type: f1_macro value: 0.6190481484526614 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9549975655560965 - name: F1 (macro) type: f1_macro value: 0.8859714456704498 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8755875900971483 - name: F1 (macro) type: f1_macro value: 0.8727623228512762 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4358288770053476 - Accuracy on SAT: 0.4391691394658754 - Accuracy on BATS: 0.519177320733741 - Accuracy on U2: 0.42105263157894735 - Accuracy on U4: 0.39814814814814814 - Accuracy on Google: 0.738 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.906282959168299 - Micro F1 score on CogALexV: 0.8178403755868544 - Micro F1 score on EVALution: 0.6267605633802817 - Micro F1 score on K&H+N: 0.9549975655560965 - Micro F1 score on ROOT09: 0.8755875900971483 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8185912698412698 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent

research-backup

2022-11-27T11:50:01Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:48:50Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.712579365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5053475935828877 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49554896142433236 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6564758198999444 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.87 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4824561403508772 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4791666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9022148561096881 - name: F1 (macro) type: f1_macro value: 0.8981159765257255 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8265258215962441 - name: F1 (macro) type: f1_macro value: 0.6164099345000134 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6505958829902492 - name: F1 (macro) type: f1_macro value: 0.6353667017193642 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.946442234123948 - name: F1 (macro) type: f1_macro value: 0.8577056022246778 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8815418364149169 - name: F1 (macro) type: f1_macro value: 0.8790128401011933 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5053475935828877 - Accuracy on SAT: 0.49554896142433236 - Accuracy on BATS: 0.6564758198999444 - Accuracy on U2: 0.4824561403508772 - Accuracy on U4: 0.4791666666666667 - Accuracy on Google: 0.87 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9022148561096881 - Micro F1 score on CogALexV: 0.8265258215962441 - Micro F1 score on EVALution: 0.6505958829902492 - Micro F1 score on K&H+N: 0.946442234123948 - Micro F1 score on ROOT09: 0.8815418364149169 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.712579365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent

research-backup

2022-11-27T11:44:51Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:43:46Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5669047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3590504451038576 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6203446359088383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.72 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8995027874039475 - name: F1 (macro) type: f1_macro value: 0.8912344017788291 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8176056338028169 - name: F1 (macro) type: f1_macro value: 0.5805419092552433 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6208017334777898 - name: F1 (macro) type: f1_macro value: 0.606000263040489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9645962300897266 - name: F1 (macro) type: f1_macro value: 0.8936228757914538 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752742087120025 - name: F1 (macro) type: f1_macro value: 0.872873148878676 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3590504451038576 - Accuracy on BATS: 0.6203446359088383 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.72 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8995027874039475 - Micro F1 score on CogALexV: 0.8176056338028169 - Micro F1 score on EVALution: 0.6208017334777898 - Micro F1 score on K&H+N: 0.9645962300897266 - Micro F1 score on ROOT09: 0.8752742087120025 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5669047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent

research-backup

2022-11-27T11:41:36Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:40:29Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6727579365079365 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2967914438502674 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29376854599406527 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5269594219010562 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.658 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3541666666666667 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8921199337049872 - name: F1 (macro) type: f1_macro value: 0.8810977870546514 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7835680751173709 - name: F1 (macro) type: f1_macro value: 0.5377409725643296 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6034669555796316 - name: F1 (macro) type: f1_macro value: 0.5863824756486721 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9533977881338248 - name: F1 (macro) type: f1_macro value: 0.8778384539799797 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8517706048260734 - name: F1 (macro) type: f1_macro value: 0.852655623225718 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.2967914438502674 - Accuracy on SAT: 0.29376854599406527 - Accuracy on BATS: 0.5269594219010562 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.3541666666666667 - Accuracy on Google: 0.658 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8921199337049872 - Micro F1 score on CogALexV: 0.7835680751173709 - Micro F1 score on EVALution: 0.6034669555796316 - Micro F1 score on K&H+N: 0.9533977881338248 - Micro F1 score on ROOT09: 0.8517706048260734 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6727579365079365 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 2 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent

research-backup

2022-11-27T11:37:59Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:36:56Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7363888888888889 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4304812834224599 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43026706231454004 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.548082267926626 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.73 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4074074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8963387072472503 - name: F1 (macro) type: f1_macro value: 0.891487326786835 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8039906103286385 - name: F1 (macro) type: f1_macro value: 0.5734260325505077 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6576381365113759 - name: F1 (macro) type: f1_macro value: 0.6449557726437222 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8840358484526234 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8696333437793794 - name: F1 (macro) type: f1_macro value: 0.8680160623253651 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4304812834224599 - Accuracy on SAT: 0.43026706231454004 - Accuracy on BATS: 0.548082267926626 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.4074074074074074 - Accuracy on Google: 0.73 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8963387072472503 - Micro F1 score on CogALexV: 0.8039906103286385 - Micro F1 score on EVALution: 0.6576381365113759 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8696333437793794 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7363888888888889 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

alexziweiwang/mtl_manual_m02_3of4

alexziweiwang

2022-11-27T11:36:11Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-27T11:18:53Z

--- tags: - generated_from_trainer model-index: - name: mtl_manual_m02_3of4 results: [] ---  # mtl_manual_m02_3of4 This model is a fine-tuned version of [alexziweiwang/mtl_manual_m02_half1](https://huggingface.co/alexziweiwang/mtl_manual_m02_half1) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 9e-06 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent

research-backup

2022-11-27T11:34:40Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:33:34Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.756984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4037433155080214 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3887240356083086 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5919955530850473 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.754 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37719298245614036 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4351851851851852 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8952840138616845 - name: F1 (macro) type: f1_macro value: 0.8880442620553469 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7962441314553991 - name: F1 (macro) type: f1_macro value: 0.5610822578719585 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6348862405200434 - name: F1 (macro) type: f1_macro value: 0.6222301227964097 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9495026778882938 - name: F1 (macro) type: f1_macro value: 0.8655789112280514 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8721403948605453 - name: F1 (macro) type: f1_macro value: 0.8700110677538381 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4037433155080214 - Accuracy on SAT: 0.3887240356083086 - Accuracy on BATS: 0.5919955530850473 - Accuracy on U2: 0.37719298245614036 - Accuracy on U4: 0.4351851851851852 - Accuracy on Google: 0.754 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8952840138616845 - Micro F1 score on CogALexV: 0.7962441314553991 - Micro F1 score on EVALution: 0.6348862405200434 - Micro F1 score on K&H+N: 0.9495026778882938 - Micro F1 score on ROOT09: 0.8721403948605453 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.756984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent

research-backup

2022-11-27T11:29:48Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:28:46Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5426190476190476 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.48916064480266813 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.662 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8389332529757421 - name: F1 (macro) type: f1_macro value: 0.8239067926315072 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.715962441314554 - name: F1 (macro) type: f1_macro value: 0.416903883488565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5200433369447454 - name: F1 (macro) type: f1_macro value: 0.5091748225464606 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9359393475690339 - name: F1 (macro) type: f1_macro value: 0.8331186543970518 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8166718896897525 - name: F1 (macro) type: f1_macro value: 0.8127585517857737 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.48916064480266813 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.662 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8389332529757421 - Micro F1 score on CogALexV: 0.715962441314554 - Micro F1 score on EVALution: 0.5200433369447454 - Micro F1 score on K&H+N: 0.9359393475690339 - Micro F1 score on ROOT09: 0.8166718896897525 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5426190476190476 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent

research-backup

2022-11-27T11:26:24Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:25:19Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6805555555555556 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49416342412451364 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.744 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4027777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8559590176284467 - name: F1 (macro) type: f1_macro value: 0.8448557692543309 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7284037558685446 - name: F1 (macro) type: f1_macro value: 0.458884277968604 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5325027085590466 - name: F1 (macro) type: f1_macro value: 0.5078999264075337 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9396257911942686 - name: F1 (macro) type: f1_macro value: 0.8402746094684064 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8172986524600439 - name: F1 (macro) type: f1_macro value: 0.8137683267205073 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.49416342412451364 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4027777777777778 - Accuracy on Google: 0.744 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8559590176284467 - Micro F1 score on CogALexV: 0.7284037558685446 - Micro F1 score on EVALution: 0.5325027085590466 - Micro F1 score on K&H+N: 0.9396257911942686 - Micro F1 score on ROOT09: 0.8172986524600439 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6805555555555556 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent

research-backup

2022-11-27T11:19:42Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:18:30Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5626984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47304057809894384 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.724 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3611111111111111 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8758475214705439 - name: F1 (macro) type: f1_macro value: 0.8691352141617384 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7424882629107981 - name: F1 (macro) type: f1_macro value: 0.48330525572765204 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5758396533044421 - name: F1 (macro) type: f1_macro value: 0.5620753826217768 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9492940112679975 - name: F1 (macro) type: f1_macro value: 0.8534235407176715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8314008147916013 - name: F1 (macro) type: f1_macro value: 0.8245727533841717 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.47304057809894384 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3611111111111111 - Accuracy on Google: 0.724 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8758475214705439 - Micro F1 score on CogALexV: 0.7424882629107981 - Micro F1 score on EVALution: 0.5758396533044421 - Micro F1 score on K&H+N: 0.9492940112679975 - Micro F1 score on ROOT09: 0.8314008147916013 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5626984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent

research-backup

2022-11-27T11:16:16Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:15:11Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7075198412698412 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3293768545994065 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6103390772651474 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.782 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3449074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8668072924514088 - name: F1 (macro) type: f1_macro value: 0.8578995013371921 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8004694835680752 - name: F1 (macro) type: f1_macro value: 0.5495048165643515 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5585048754062839 - name: F1 (macro) type: f1_macro value: 0.5382998120865616 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9495026778882938 - name: F1 (macro) type: f1_macro value: 0.8730545069423521 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8423691632717015 - name: F1 (macro) type: f1_macro value: 0.8392309386107293 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3293768545994065 - Accuracy on BATS: 0.6103390772651474 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.3449074074074074 - Accuracy on Google: 0.782 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8668072924514088 - Micro F1 score on CogALexV: 0.8004694835680752 - Micro F1 score on EVALution: 0.5585048754062839 - Micro F1 score on K&H+N: 0.9495026778882938 - Micro F1 score on ROOT09: 0.8423691632717015 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7075198412698412 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 4 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent

research-backup

2022-11-27T11:14:40Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:13:32Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8384722222222222 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37298499166203447 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.594 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44212962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8761488624378485 - name: F1 (macro) type: f1_macro value: 0.8674862753224203 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7647887323943662 - name: F1 (macro) type: f1_macro value: 0.512977631266088 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6034669555796316 - name: F1 (macro) type: f1_macro value: 0.5925306935387006 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9543715656952076 - name: F1 (macro) type: f1_macro value: 0.8708484151911093 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8545910372923848 - name: F1 (macro) type: f1_macro value: 0.8542907180407261 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.37298499166203447 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.44212962962962965 - Accuracy on Google: 0.594 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8761488624378485 - Micro F1 score on CogALexV: 0.7647887323943662 - Micro F1 score on EVALution: 0.6034669555796316 - Micro F1 score on K&H+N: 0.9543715656952076 - Micro F1 score on ROOT09: 0.8545910372923848 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8384722222222222 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent

research-backup

2022-11-27T11:09:49Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:08:46Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6259325396825397 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3422459893048128 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34421364985163205 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38632573652028906 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.472 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3157894736842105 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3402777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564110290794034 - name: F1 (macro) type: f1_macro value: 0.8440168167296423 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7647887323943662 - name: F1 (macro) type: f1_macro value: 0.496728365995928 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.542795232936078 - name: F1 (macro) type: f1_macro value: 0.5330540852169673 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9477637893858246 - name: F1 (macro) type: f1_macro value: 0.8528390601522364 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8382952052648073 - name: F1 (macro) type: f1_macro value: 0.8301168432370699 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3422459893048128 - Accuracy on SAT: 0.34421364985163205 - Accuracy on BATS: 0.38632573652028906 - Accuracy on U2: 0.3157894736842105 - Accuracy on U4: 0.3402777777777778 - Accuracy on Google: 0.472 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8564110290794034 - Micro F1 score on CogALexV: 0.7647887323943662 - Micro F1 score on EVALution: 0.542795232936078 - Micro F1 score on K&H+N: 0.9477637893858246 - Micro F1 score on ROOT09: 0.8382952052648073 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6259325396825397 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 2 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent

research-backup

2022-11-27T11:08:11Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:07:07Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6776984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31283422459893045 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3086053412462908 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6042245692051139 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.746 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3888888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8716287479282808 - name: F1 (macro) type: f1_macro value: 0.858806993883955 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7561032863849765 - name: F1 (macro) type: f1_macro value: 0.49523926791823153 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5455037919826652 - name: F1 (macro) type: f1_macro value: 0.5396914583253821 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9380260137719969 - name: F1 (macro) type: f1_macro value: 0.8440588889140052 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8448762143528674 - name: F1 (macro) type: f1_macro value: 0.838226018147099 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31283422459893045 - Accuracy on SAT: 0.3086053412462908 - Accuracy on BATS: 0.6042245692051139 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3888888888888889 - Accuracy on Google: 0.746 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8716287479282808 - Micro F1 score on CogALexV: 0.7561032863849765 - Micro F1 score on EVALution: 0.5455037919826652 - Micro F1 score on K&H+N: 0.9380260137719969 - Micro F1 score on ROOT09: 0.8448762143528674 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6776984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: parent The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-1-parent/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical

research-backup

2022-11-27T11:03:19Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:02:12Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7979166666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3877005347593583 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5169538632573653 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.748 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4050925925925926 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8945306614434232 - name: F1 (macro) type: f1_macro value: 0.8889500954383515 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8305164319248827 - name: F1 (macro) type: f1_macro value: 0.6378751139859162 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6468039003250271 - name: F1 (macro) type: f1_macro value: 0.6417982065266167 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.955762676497183 - name: F1 (macro) type: f1_macro value: 0.8829548510663163 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8834221247257913 - name: F1 (macro) type: f1_macro value: 0.8817444772279625 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3877005347593583 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.5169538632573653 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4050925925925926 - Accuracy on Google: 0.748 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8945306614434232 - Micro F1 score on CogALexV: 0.8305164319248827 - Micro F1 score on EVALution: 0.6468039003250271 - Micro F1 score on K&H+N: 0.955762676497183 - Micro F1 score on ROOT09: 0.8834221247257913 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7979166666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical

research-backup

2022-11-27T11:01:36Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T11:00:29Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8185912698412698 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4358288770053476 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4391691394658754 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.519177320733741 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.738 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42105263157894735 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39814814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.906282959168299 - name: F1 (macro) type: f1_macro value: 0.8996950498562719 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8178403755868544 - name: F1 (macro) type: f1_macro value: 0.6064939255543282 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6267605633802817 - name: F1 (macro) type: f1_macro value: 0.6190481484526614 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9549975655560965 - name: F1 (macro) type: f1_macro value: 0.8859714456704498 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8755875900971483 - name: F1 (macro) type: f1_macro value: 0.8727623228512762 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4358288770053476 - Accuracy on SAT: 0.4391691394658754 - Accuracy on BATS: 0.519177320733741 - Accuracy on U2: 0.42105263157894735 - Accuracy on U4: 0.39814814814814814 - Accuracy on Google: 0.738 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.906282959168299 - Micro F1 score on CogALexV: 0.8178403755868544 - Micro F1 score on EVALution: 0.6267605633802817 - Micro F1 score on K&H+N: 0.9549975655560965 - Micro F1 score on ROOT09: 0.8755875900971483 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8185912698412698 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical

research-backup

2022-11-27T10:59:55Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:58:50Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7243055555555555 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47593582887700536 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47477744807121663 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6753752084491385 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.868 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43859649122807015 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9029682085279493 - name: F1 (macro) type: f1_macro value: 0.8981565352595764 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8276995305164319 - name: F1 (macro) type: f1_macro value: 0.6302262610207737 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6522210184182016 - name: F1 (macro) type: f1_macro value: 0.6357658526272931 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9555540098768867 - name: F1 (macro) type: f1_macro value: 0.8819875185777288 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.872453776245691 - name: F1 (macro) type: f1_macro value: 0.868959677073872 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.47593582887700536 - Accuracy on SAT: 0.47477744807121663 - Accuracy on BATS: 0.6753752084491385 - Accuracy on U2: 0.43859649122807015 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.868 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9029682085279493 - Micro F1 score on CogALexV: 0.8276995305164319 - Micro F1 score on EVALution: 0.6522210184182016 - Micro F1 score on K&H+N: 0.9555540098768867 - Micro F1 score on ROOT09: 0.872453776245691 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7243055555555555 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-d-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical

research-backup

2022-11-27T10:55:00Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:53:33Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5262301587301588 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3422459893048128 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35311572700296734 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5814341300722624 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.696 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8976947416001205 - name: F1 (macro) type: f1_macro value: 0.8898626811561807 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.794131455399061 - name: F1 (macro) type: f1_macro value: 0.5320544436133094 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6153846153846154 - name: F1 (macro) type: f1_macro value: 0.6017116191379441 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9678653404743688 - name: F1 (macro) type: f1_macro value: 0.9013285476834861 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8633657160764651 - name: F1 (macro) type: f1_macro value: 0.8622729537898305 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3422459893048128 - Accuracy on SAT: 0.35311572700296734 - Accuracy on BATS: 0.5814341300722624 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.696 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8976947416001205 - Micro F1 score on CogALexV: 0.794131455399061 - Micro F1 score on EVALution: 0.6153846153846154 - Micro F1 score on K&H+N: 0.9678653404743688 - Micro F1 score on ROOT09: 0.8633657160764651 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5262301587301588 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical

research-backup

2022-11-27T10:51:28Z

101

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:50:22Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6477777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3916913946587537 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6525847693162868 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.83 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4097222222222222 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9025161970769926 - name: F1 (macro) type: f1_macro value: 0.8944325918923539 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8093896713615022 - name: F1 (macro) type: f1_macro value: 0.5748135456441361 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6305525460455038 - name: F1 (macro) type: f1_macro value: 0.6156874339832458 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9573624539194547 - name: F1 (macro) type: f1_macro value: 0.8782849835856299 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.8617512701372868 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3916913946587537 - Accuracy on BATS: 0.6525847693162868 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.4097222222222222 - Accuracy on Google: 0.83 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9025161970769926 - Micro F1 score on CogALexV: 0.8093896713615022 - Micro F1 score on EVALution: 0.6305525460455038 - Micro F1 score on K&H+N: 0.9573624539194547 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6477777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical

research-backup

2022-11-27T10:47:52Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:46:46Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7918253968253969 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37433155080213903 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37388724035608306 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5063924402445803 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.732 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4649122807017544 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4699074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9029682085279493 - name: F1 (macro) type: f1_macro value: 0.9007842298298612 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8000000000000002 - name: F1 (macro) type: f1_macro value: 0.5698574777874919 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6370530877573131 - name: F1 (macro) type: f1_macro value: 0.6301907781696539 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9563886763580719 - name: F1 (macro) type: f1_macro value: 0.8805161918245229 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8693199623942337 - name: F1 (macro) type: f1_macro value: 0.8693436077460507 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37433155080213903 - Accuracy on SAT: 0.37388724035608306 - Accuracy on BATS: 0.5063924402445803 - Accuracy on U2: 0.4649122807017544 - Accuracy on U4: 0.4699074074074074 - Accuracy on Google: 0.732 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9029682085279493 - Micro F1 score on CogALexV: 0.8000000000000002 - Micro F1 score on EVALution: 0.6370530877573131 - Micro F1 score on K&H+N: 0.9563886763580719 - Micro F1 score on ROOT09: 0.8693199623942337 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7918253968253969 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical

research-backup

2022-11-27T10:46:13Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:45:08Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8402777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42513368983957217 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42433234421364985 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5147304057809894 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.726 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4166666666666667 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39351851851851855 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8798722402334079 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5810551586304064 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6511375947995667 - name: F1 (macro) type: f1_macro value: 0.6381606420091618 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.960562008763998 - name: F1 (macro) type: f1_macro value: 0.8816273449850349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.862338882041517 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42513368983957217 - Accuracy on SAT: 0.42433234421364985 - Accuracy on BATS: 0.5147304057809894 - Accuracy on U2: 0.4166666666666667 - Accuracy on U4: 0.39351851851851855 - Accuracy on Google: 0.726 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6511375947995667 - Micro F1 score on K&H+N: 0.960562008763998 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8402777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical

research-backup

2022-11-27T10:42:58Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:41:52Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7941865079365079 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4839572192513369 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49258160237388726 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5714285714285714 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.742 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44298245614035087 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8769104196894718 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8068075117370893 - name: F1 (macro) type: f1_macro value: 0.5788410582872519 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6592632719393283 - name: F1 (macro) type: f1_macro value: 0.6485530008701489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9498504555887877 - name: F1 (macro) type: f1_macro value: 0.8670463800845212 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8539203486771704 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4839572192513369 - Accuracy on SAT: 0.49258160237388726 - Accuracy on BATS: 0.5714285714285714 - Accuracy on U2: 0.44298245614035087 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.742 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8068075117370893 - Micro F1 score on EVALution: 0.6592632719393283 - Micro F1 score on K&H+N: 0.9498504555887877 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7941865079365079 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical

research-backup

2022-11-27T10:41:20Z

105

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:40:12Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.665813492063492 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43315508021390375 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42136498516320475 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6020011117287382 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.822 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44907407407407407 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9016121741750791 - name: F1 (macro) type: f1_macro value: 0.8952063264803337 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8072769953051643 - name: F1 (macro) type: f1_macro value: 0.5850185012296035 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6538461538461539 - name: F1 (macro) type: f1_macro value: 0.6495448120234049 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9568060095986646 - name: F1 (macro) type: f1_macro value: 0.8713838077897916 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740206831714196 - name: F1 (macro) type: f1_macro value: 0.8710453231442221 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.43315508021390375 - Accuracy on SAT: 0.42136498516320475 - Accuracy on BATS: 0.6020011117287382 - Accuracy on U2: 0.4473684210526316 - Accuracy on U4: 0.44907407407407407 - Accuracy on Google: 0.822 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9016121741750791 - Micro F1 score on CogALexV: 0.8072769953051643 - Micro F1 score on EVALution: 0.6538461538461539 - Micro F1 score on K&H+N: 0.9568060095986646 - Micro F1 score on ROOT09: 0.8740206831714196 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.665813492063492 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical

research-backup

2022-11-27T10:39:36Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:38:27Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5426190476190476 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3502673796791444 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.48916064480266813 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.662 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3508771929824561 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8389332529757421 - name: F1 (macro) type: f1_macro value: 0.8239067926315072 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.715962441314554 - name: F1 (macro) type: f1_macro value: 0.416903883488565 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5200433369447454 - name: F1 (macro) type: f1_macro value: 0.5091748225464606 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9359393475690339 - name: F1 (macro) type: f1_macro value: 0.8331186543970518 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8166718896897525 - name: F1 (macro) type: f1_macro value: 0.8127585517857737 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3502673796791444 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.48916064480266813 - Accuracy on U2: 0.3508771929824561 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.662 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8389332529757421 - Micro F1 score on CogALexV: 0.715962441314554 - Micro F1 score on EVALution: 0.5200433369447454 - Micro F1 score on K&H+N: 0.9359393475690339 - Micro F1 score on ROOT09: 0.8166718896897525 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5426190476190476 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical

research-backup

2022-11-27T10:35:54Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:34:44Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6639087301587302 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.35561497326203206 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3560830860534125 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42857142857142855 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.668 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3815789473684211 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3587962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8540003013409673 - name: F1 (macro) type: f1_macro value: 0.8415316548238166 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6960093896713615 - name: F1 (macro) type: f1_macro value: 0.4121210944195095 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5021668472372698 - name: F1 (macro) type: f1_macro value: 0.4940624714187321 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9437991236001948 - name: F1 (macro) type: f1_macro value: 0.8505990194719038 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.823879661548104 - name: F1 (macro) type: f1_macro value: 0.8171304236528446 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.35561497326203206 - Accuracy on SAT: 0.3560830860534125 - Accuracy on BATS: 0.42857142857142855 - Accuracy on U2: 0.3815789473684211 - Accuracy on U4: 0.3587962962962963 - Accuracy on Google: 0.668 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8540003013409673 - Micro F1 score on CogALexV: 0.6960093896713615 - Micro F1 score on EVALution: 0.5021668472372698 - Micro F1 score on K&H+N: 0.9437991236001948 - Micro F1 score on ROOT09: 0.823879661548104 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6639087301587302 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-e-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical

research-backup

2022-11-27T10:30:47Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:29:36Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5528769841269842 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3181818181818182 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5780989438576987 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.75 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8646979056802773 - name: F1 (macro) type: f1_macro value: 0.8512823829061237 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7136150234741784 - name: F1 (macro) type: f1_macro value: 0.4307168102299423 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5563380281690141 - name: F1 (macro) type: f1_macro value: 0.5317021379158652 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9493635668080963 - name: F1 (macro) type: f1_macro value: 0.8585118986669762 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8232528987778126 - name: F1 (macro) type: f1_macro value: 0.8208236307090298 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3181818181818182 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5780989438576987 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.75 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8646979056802773 - Micro F1 score on CogALexV: 0.7136150234741784 - Micro F1 score on EVALution: 0.5563380281690141 - Micro F1 score on K&H+N: 0.9493635668080963 - Micro F1 score on ROOT09: 0.8232528987778126 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5528769841269842 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical

research-backup

2022-11-27T10:29:01Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:27:51Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5626984126984127 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39572192513368987 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3857566765578635 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.47304057809894384 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.724 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37280701754385964 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3611111111111111 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8758475214705439 - name: F1 (macro) type: f1_macro value: 0.8691352141617384 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7424882629107981 - name: F1 (macro) type: f1_macro value: 0.48330525572765204 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5758396533044421 - name: F1 (macro) type: f1_macro value: 0.5620753826217768 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9492940112679975 - name: F1 (macro) type: f1_macro value: 0.8534235407176715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8314008147916013 - name: F1 (macro) type: f1_macro value: 0.8245727533841717 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.39572192513368987 - Accuracy on SAT: 0.3857566765578635 - Accuracy on BATS: 0.47304057809894384 - Accuracy on U2: 0.37280701754385964 - Accuracy on U4: 0.3611111111111111 - Accuracy on Google: 0.724 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8758475214705439 - Micro F1 score on CogALexV: 0.7424882629107981 - Micro F1 score on EVALution: 0.5758396533044421 - Micro F1 score on K&H+N: 0.9492940112679975 - Micro F1 score on ROOT09: 0.8314008147916013 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5626984126984127 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical

research-backup

2022-11-27T10:27:12Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:26:04Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6220436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6425792106725959 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.86 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8737381346994124 - name: F1 (macro) type: f1_macro value: 0.8631099788352631 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7786384976525821 - name: F1 (macro) type: f1_macro value: 0.5127374486425482 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5557963163596966 - name: F1 (macro) type: f1_macro value: 0.5383373253296296 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9456075676427628 - name: F1 (macro) type: f1_macro value: 0.8417797508933453 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.849890316515199 - name: F1 (macro) type: f1_macro value: 0.8461613177196163 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.6425792106725959 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.86 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8737381346994124 - Micro F1 score on CogALexV: 0.7786384976525821 - Micro F1 score on EVALution: 0.5557963163596966 - Micro F1 score on K&H+N: 0.9456075676427628 - Micro F1 score on ROOT09: 0.849890316515199 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6220436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical

research-backup

2022-11-27T10:25:30Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:24:27Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.41785714285714287 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.32887700534759357 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3264094955489614 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5302946081156198 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.648 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3201754385964912 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.877655567274371 - name: F1 (macro) type: f1_macro value: 0.8691271164987491 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7129107981220657 - name: F1 (macro) type: f1_macro value: 0.4196917803483246 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5368364030335862 - name: F1 (macro) type: f1_macro value: 0.5188634387372184 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9531195659734298 - name: F1 (macro) type: f1_macro value: 0.8766279141586715 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8031964901284864 - name: F1 (macro) type: f1_macro value: 0.8003449544921196 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.32887700534759357 - Accuracy on SAT: 0.3264094955489614 - Accuracy on BATS: 0.5302946081156198 - Accuracy on U2: 0.3201754385964912 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.648 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.877655567274371 - Micro F1 score on CogALexV: 0.7129107981220657 - Micro F1 score on EVALution: 0.5368364030335862 - Micro F1 score on K&H+N: 0.9531195659734298 - Micro F1 score on ROOT09: 0.8031964901284864 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.41785714285714287 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical

research-backup

2022-11-27T10:19:00Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:17:57Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6413492063492063 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.386881600889383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3684210526315789 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752448395359349 - name: F1 (macro) type: f1_macro value: 0.8642915064078064 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.752582159624413 - name: F1 (macro) type: f1_macro value: 0.445502286190483 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.557421451787649 - name: F1 (macro) type: f1_macro value: 0.5384307321042293 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9433122348195033 - name: F1 (macro) type: f1_macro value: 0.8480017401840129 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8429959260419931 - name: F1 (macro) type: f1_macro value: 0.8422459710118972 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.386881600889383 - Accuracy on U2: 0.3684210526315789 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.516 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8752448395359349 - Micro F1 score on CogALexV: 0.752582159624413 - Micro F1 score on EVALution: 0.557421451787649 - Micro F1 score on K&H+N: 0.9433122348195033 - Micro F1 score on ROOT09: 0.8429959260419931 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6413492063492063 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child_prototypical The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child-prototypical/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child

research-backup

2022-11-27T10:12:25Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:11:18Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7979166666666667 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3877005347593583 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5169538632573653 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.748 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39473684210526316 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4050925925925926 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8945306614434232 - name: F1 (macro) type: f1_macro value: 0.8889500954383515 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8305164319248827 - name: F1 (macro) type: f1_macro value: 0.6378751139859162 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6468039003250271 - name: F1 (macro) type: f1_macro value: 0.6417982065266167 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.955762676497183 - name: F1 (macro) type: f1_macro value: 0.8829548510663163 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8834221247257913 - name: F1 (macro) type: f1_macro value: 0.8817444772279625 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3877005347593583 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.5169538632573653 - Accuracy on U2: 0.39473684210526316 - Accuracy on U4: 0.4050925925925926 - Accuracy on Google: 0.748 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8945306614434232 - Micro F1 score on CogALexV: 0.8305164319248827 - Micro F1 score on EVALution: 0.6468039003250271 - Micro F1 score on K&H+N: 0.955762676497183 - Micro F1 score on ROOT09: 0.8834221247257913 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7979166666666667 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-e-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

Euchale/ArcaneInkpunk

Euchale

2022-11-27T10:08:57Z

0

null

[ "region:us" ]

null

2022-11-26T22:34:18Z

50/50 Merge of Inkpunk (https://huggingface.co/Envvi/Inkpunk-Diffusion/blob/main/inkpunk-diffusion-v1.ckpt) and Arcane (https://huggingface.co/nitrosocke/Arcane-Diffusion)

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child

research-backup

2022-11-27T10:03:36Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:02:26Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5669047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3590504451038576 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6203446359088383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.72 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39035087719298245 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38425925925925924 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8995027874039475 - name: F1 (macro) type: f1_macro value: 0.8912344017788291 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8176056338028169 - name: F1 (macro) type: f1_macro value: 0.5805419092552433 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6208017334777898 - name: F1 (macro) type: f1_macro value: 0.606000263040489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9645962300897266 - name: F1 (macro) type: f1_macro value: 0.8936228757914538 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752742087120025 - name: F1 (macro) type: f1_macro value: 0.872873148878676 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3590504451038576 - Accuracy on BATS: 0.6203446359088383 - Accuracy on U2: 0.39035087719298245 - Accuracy on U4: 0.38425925925925924 - Accuracy on Google: 0.72 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8995027874039475 - Micro F1 score on CogALexV: 0.8176056338028169 - Micro F1 score on EVALution: 0.6208017334777898 - Micro F1 score on K&H+N: 0.9645962300897266 - Micro F1 score on ROOT09: 0.8752742087120025 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5669047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child

research-backup

2022-11-27T10:01:50Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T10:00:47Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6790674603174603 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4144385026737968 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4065281899109792 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6142301278488049 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.736 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37962962962962965 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8940786499924664 - name: F1 (macro) type: f1_macro value: 0.8886568208317418 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8112676056338028 - name: F1 (macro) type: f1_macro value: 0.5806627021345718 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.628385698808234 - name: F1 (macro) type: f1_macro value: 0.6195235287229023 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9612575641649858 - name: F1 (macro) type: f1_macro value: 0.8870509987826778 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8655593857724851 - name: F1 (macro) type: f1_macro value: 0.8633665155994553 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4144385026737968 - Accuracy on SAT: 0.4065281899109792 - Accuracy on BATS: 0.6142301278488049 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.37962962962962965 - Accuracy on Google: 0.736 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8940786499924664 - Micro F1 score on CogALexV: 0.8112676056338028 - Micro F1 score on EVALution: 0.628385698808234 - Micro F1 score on K&H+N: 0.9612575641649858 - Micro F1 score on ROOT09: 0.8655593857724851 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6790674603174603 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-c-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child

research-backup

2022-11-27T09:58:31Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:57:24Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7464087301587301 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42245989304812837 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42729970326409494 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6286826014452473 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.8 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4517543859649123 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.891366581286726 - name: F1 (macro) type: f1_macro value: 0.8832611892840135 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8321596244131455 - name: F1 (macro) type: f1_macro value: 0.6369879175787183 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6641386782231853 - name: F1 (macro) type: f1_macro value: 0.6521557904641199 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9580580093204424 - name: F1 (macro) type: f1_macro value: 0.8787147026333313 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8746474459417111 - name: F1 (macro) type: f1_macro value: 0.8720645901570915 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42245989304812837 - Accuracy on SAT: 0.42729970326409494 - Accuracy on BATS: 0.6286826014452473 - Accuracy on U2: 0.4517543859649123 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.8 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.891366581286726 - Micro F1 score on CogALexV: 0.8321596244131455 - Micro F1 score on EVALution: 0.6641386782231853 - Micro F1 score on K&H+N: 0.9580580093204424 - Micro F1 score on ROOT09: 0.8746474459417111 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7464087301587301 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child

research-backup

2022-11-27T09:56:47Z

107

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:55:44Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7363888888888889 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4304812834224599 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.43026706231454004 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.548082267926626 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.73 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.41228070175438597 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4074074074074074 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8963387072472503 - name: F1 (macro) type: f1_macro value: 0.891487326786835 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8039906103286385 - name: F1 (macro) type: f1_macro value: 0.5734260325505077 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6576381365113759 - name: F1 (macro) type: f1_macro value: 0.6449557726437222 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8840358484526234 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8696333437793794 - name: F1 (macro) type: f1_macro value: 0.8680160623253651 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4304812834224599 - Accuracy on SAT: 0.43026706231454004 - Accuracy on BATS: 0.548082267926626 - Accuracy on U2: 0.41228070175438597 - Accuracy on U4: 0.4074074074074074 - Accuracy on Google: 0.73 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8963387072472503 - Micro F1 score on CogALexV: 0.8039906103286385 - Micro F1 score on EVALution: 0.6576381365113759 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.8696333437793794 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7363888888888889 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 5 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child

research-backup

2022-11-27T09:55:10Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:54:02Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.8402777777777778 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42513368983957217 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.42433234421364985 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5147304057809894 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.726 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4166666666666667 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.39351851851851855 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8798722402334079 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8049295774647888 - name: F1 (macro) type: f1_macro value: 0.5810551586304064 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6511375947995667 - name: F1 (macro) type: f1_macro value: 0.6381606420091618 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.960562008763998 - name: F1 (macro) type: f1_macro value: 0.8816273449850349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8639924788467563 - name: F1 (macro) type: f1_macro value: 0.862338882041517 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.42513368983957217 - Accuracy on SAT: 0.42433234421364985 - Accuracy on BATS: 0.5147304057809894 - Accuracy on U2: 0.4166666666666667 - Accuracy on U4: 0.39351851851851855 - Accuracy on Google: 0.726 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8049295774647888 - Micro F1 score on EVALution: 0.6511375947995667 - Micro F1 score on K&H+N: 0.960562008763998 - Micro F1 score on ROOT09: 0.8639924788467563 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.8402777777777778 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-b-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child

research-backup

2022-11-27T09:51:45Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:50:36Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7941865079365079 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4839572192513369 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.49258160237388726 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5714285714285714 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.742 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44298245614035087 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4537037037037037 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826276932348952 - name: F1 (macro) type: f1_macro value: 0.8769104196894718 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8068075117370893 - name: F1 (macro) type: f1_macro value: 0.5788410582872519 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6592632719393283 - name: F1 (macro) type: f1_macro value: 0.6485530008701489 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9498504555887877 - name: F1 (macro) type: f1_macro value: 0.8670463800845212 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.857724851143842 - name: F1 (macro) type: f1_macro value: 0.8539203486771704 --- # relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.4839572192513369 - Accuracy on SAT: 0.49258160237388726 - Accuracy on BATS: 0.5714285714285714 - Accuracy on U2: 0.44298245614035087 - Accuracy on U4: 0.4537037037037037 - Accuracy on Google: 0.742 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8826276932348952 - Micro F1 score on CogALexV: 0.8068075117370893 - Micro F1 score on EVALution: 0.6592632719393283 - Micro F1 score on K&H+N: 0.9498504555887877 - Micro F1 score on ROOT09: 0.857724851143842 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7941865079365079 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-mask-prompt-a-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child

research-backup

2022-11-27T09:43:26Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:42:19Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7002579365079366 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3649851632047478 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44691495275152865 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.716 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740394756667169 - name: F1 (macro) type: f1_macro value: 0.8618347056127108 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7246478873239436 - name: F1 (macro) type: f1_macro value: 0.4392658356578824 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5243770314192849 - name: F1 (macro) type: f1_macro value: 0.5089556262366394 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9525631216526397 - name: F1 (macro) type: f1_macro value: 0.8724005012414945 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564713256032591 - name: F1 (macro) type: f1_macro value: 0.8510225054091837 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3649851632047478 - Accuracy on BATS: 0.44691495275152865 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.375 - Accuracy on Google: 0.716 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8740394756667169 - Micro F1 score on CogALexV: 0.7246478873239436 - Micro F1 score on EVALution: 0.5243770314192849 - Micro F1 score on K&H+N: 0.9525631216526397 - Micro F1 score on ROOT09: 0.8564713256032591 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7002579365079366 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child

research-backup

2022-11-27T09:40:05Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:38:59Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5528769841269842 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3181818181818182 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5780989438576987 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.75 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.36574074074074076 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8646979056802773 - name: F1 (macro) type: f1_macro value: 0.8512823829061237 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7136150234741784 - name: F1 (macro) type: f1_macro value: 0.4307168102299423 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5563380281690141 - name: F1 (macro) type: f1_macro value: 0.5317021379158652 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9493635668080963 - name: F1 (macro) type: f1_macro value: 0.8585118986669762 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8232528987778126 - name: F1 (macro) type: f1_macro value: 0.8208236307090298 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3181818181818182 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5780989438576987 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.36574074074074076 - Accuracy on Google: 0.75 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8646979056802773 - Micro F1 score on CogALexV: 0.7136150234741784 - Micro F1 score on EVALution: 0.5563380281690141 - Micro F1 score on K&H+N: 0.9493635668080963 - Micro F1 score on ROOT09: 0.8232528987778126 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5528769841269842 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

takizawa/xlm-roberta-base-finetuned-panx-all

takizawa

2022-11-27T09:37:33Z

111

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T09:11:26Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-all results: [] ---  # xlm-roberta-base-finetuned-panx-all This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child

research-backup

2022-11-27T09:36:46Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:35:41Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6220436507936508 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34759358288770054 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.34718100890207715 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6425792106725959 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.86 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.40789473684210525 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4212962962962963 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8737381346994124 - name: F1 (macro) type: f1_macro value: 0.8631099788352631 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7786384976525821 - name: F1 (macro) type: f1_macro value: 0.5127374486425482 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5557963163596966 - name: F1 (macro) type: f1_macro value: 0.5383373253296296 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9456075676427628 - name: F1 (macro) type: f1_macro value: 0.8417797508933453 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.849890316515199 - name: F1 (macro) type: f1_macro value: 0.8461613177196163 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.34759358288770054 - Accuracy on SAT: 0.34718100890207715 - Accuracy on BATS: 0.6425792106725959 - Accuracy on U2: 0.40789473684210525 - Accuracy on U4: 0.4212962962962963 - Accuracy on Google: 0.86 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8737381346994124 - Micro F1 score on CogALexV: 0.7786384976525821 - Micro F1 score on EVALution: 0.5557963163596966 - Micro F1 score on K&H+N: 0.9456075676427628 - Micro F1 score on ROOT09: 0.849890316515199 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6220436507936508 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 1 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-1-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child

research-backup

2022-11-27T09:35:08Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:34:01Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6000396825396825 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.31283422459893045 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3115727002967359 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5669816564758199 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.718 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3333333333333333 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8671086334187132 - name: F1 (macro) type: f1_macro value: 0.8580840844941381 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7258215962441315 - name: F1 (macro) type: f1_macro value: 0.4298561084137266 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5460455037919827 - name: F1 (macro) type: f1_macro value: 0.5415508674613216 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9425471238784169 - name: F1 (macro) type: f1_macro value: 0.8536350331018349 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.831714196176747 - name: F1 (macro) type: f1_macro value: 0.8272644500838311 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.31283422459893045 - Accuracy on SAT: 0.3115727002967359 - Accuracy on BATS: 0.5669816564758199 - Accuracy on U2: 0.3333333333333333 - Accuracy on U4: 0.3333333333333333 - Accuracy on Google: 0.718 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8671086334187132 - Micro F1 score on CogALexV: 0.7258215962441315 - Micro F1 score on EVALution: 0.5460455037919827 - Micro F1 score on K&H+N: 0.9425471238784169 - Micro F1 score on ROOT09: 0.831714196176747 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6000396825396825 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 8 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-c-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child

research-backup

2022-11-27T09:30:09Z

104

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:28:59Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6335515873015873 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.30213903743315507 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2997032640949555 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5403001667593107 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.594 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37037037037037035 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8618351664908844 - name: F1 (macro) type: f1_macro value: 0.8502274926780541 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7187793427230047 - name: F1 (macro) type: f1_macro value: 0.4123652986554882 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5390032502708559 - name: F1 (macro) type: f1_macro value: 0.5145491852283891 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9442860123808862 - name: F1 (macro) type: f1_macro value: 0.8512983083261685 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8122845502977123 - name: F1 (macro) type: f1_macro value: 0.8070813252421513 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.30213903743315507 - Accuracy on SAT: 0.2997032640949555 - Accuracy on BATS: 0.5403001667593107 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.37037037037037035 - Accuracy on Google: 0.594 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8618351664908844 - Micro F1 score on CogALexV: 0.7187793427230047 - Micro F1 score on EVALution: 0.5390032502708559 - Micro F1 score on K&H+N: 0.9442860123808862 - Micro F1 score on ROOT09: 0.8122845502977123 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6335515873015873 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 10 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-b-loob-0-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

kejian/final-cond-10-0.1-again-2

kejian

2022-11-27T09:30:05Z

2

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-26T12:19:51Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-10-0.1-again-2 results: [] ---  # kejian/final-cond-10-0.1-again-2 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 64 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.1, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.1-again-2', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/kjz9xgv1

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child

research-backup

2022-11-27T09:28:24Z

103

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-27T09:27:15Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6413492063492063 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.37967914438502676 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3827893175074184 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.386881600889383 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3684210526315789 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3773148148148148 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8752448395359349 - name: F1 (macro) type: f1_macro value: 0.8642915064078064 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.752582159624413 - name: F1 (macro) type: f1_macro value: 0.445502286190483 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.557421451787649 - name: F1 (macro) type: f1_macro value: 0.5384307321042293 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9433122348195033 - name: F1 (macro) type: f1_macro value: 0.8480017401840129 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8429959260419931 - name: F1 (macro) type: f1_macro value: 0.8422459710118972 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/analogy.json)): - Accuracy on SAT (full): 0.37967914438502676 - Accuracy on SAT: 0.3827893175074184 - Accuracy on BATS: 0.386881600889383 - Accuracy on U2: 0.3684210526315789 - Accuracy on U4: 0.3773148148148148 - Accuracy on Google: 0.516 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8752448395359349 - Micro F1 score on CogALexV: 0.752582159624413 - Micro F1 score on EVALution: 0.557421451787649 - Micro F1 score on K&H+N: 0.9433122348195033 - Micro F1 score on ROOT09: 0.8429959260419931 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6413492063492063 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None - data_level: child The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-a-loob-2-child/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```

reallygoodtechdeals/steve-ai-Dialo-GPT-medium

reallygoodtechdeals

2022-11-27T09:07:09Z

117

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "conversational", "license:wtfpl", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-27T07:20:01Z

--- license: wtfpl tags: - conversational ---

takizawa/xlm-roberta-base-finetuned-panx-it

takizawa

2022-11-27T08:55:43Z

142

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T08:40:08Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-it results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.it metrics: - name: F1 type: f1 value: 0.8124233755619126 ---  # xlm-roberta-base-finetuned-panx-it This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.2630 - F1: 0.8124 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.8193 | 1.0 | 70 | 0.3200 | 0.7356 | | 0.2773 | 2.0 | 140 | 0.2841 | 0.7882 | | 0.1807 | 3.0 | 210 | 0.2630 | 0.8124 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

takizawa/xlm-roberta-base-finetuned-panx-de-fr

takizawa

2022-11-27T08:20:46Z

112

0

transformers

[ "transformers", "pytorch", "xlm-roberta", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T07:54:20Z

--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de-fr results: [] ---  # xlm-roberta-base-finetuned-panx-de-fr This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1608 - F1: 0.8593 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2888 | 1.0 | 715 | 0.1779 | 0.8233 | | 0.1437 | 2.0 | 1430 | 0.1570 | 0.8497 | | 0.0931 | 3.0 | 2145 | 0.1608 | 0.8593 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

takizawa/xlm-roberta-base-finetuned-panx-de

takizawa

2022-11-27T07:44:53Z

110

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "token-classification", "generated_from_trainer", "dataset:xtreme", "license:mit", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T07:21:03Z

--- license: mit tags: - generated_from_trainer datasets: - xtreme metrics: - f1 model-index: - name: xlm-roberta-base-finetuned-panx-de results: - task: name: Token Classification type: token-classification dataset: name: xtreme type: xtreme args: PAN-X.de metrics: - name: F1 type: f1 value: 0.8648740833380706 ---  # xlm-roberta-base-finetuned-panx-de This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset. It achieves the following results on the evaluation set: - Loss: 0.1365 - F1: 0.8649 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | F1 | |:-------------:|:-----:|:----:|:---------------:|:------:| | 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 | | 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 | | 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

jhakaran1/process-data

jhakaran1

2022-11-27T07:16:54Z

161

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-06T16:01:37Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: process-data results: [] ---  # process-data This model is a fine-tuned version of [jhakaran1/bert-base-uncased-bert-mlm](https://huggingface.co/jhakaran1/bert-base-uncased-bert-mlm) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8087 - Accuracy: 0.6792 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6939 | 1.0 | 3907 | 0.7903 | 0.6660 | | 0.6155 | 2.0 | 7814 | 0.7929 | 0.6685 | | 0.5436 | 3.0 | 11721 | 0.8087 | 0.6792 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

alexziweiwang/mtl_manual_2601015_epoch1

alexziweiwang

2022-11-27T06:31:44Z

31

0

transformers

[ "transformers", "pytorch", "wav2vec2", "generated_from_trainer", "endpoints_compatible", "region:us" ]

null

2022-11-27T06:15:36Z

--- tags: - generated_from_trainer model-index: - name: mtl_manual_2601015_epoch1 results: [] ---  # mtl_manual_2601015_epoch1 This model is a fine-tuned version of [alexziweiwang/exp21-uaspeech-foundation](https://huggingface.co/alexziweiwang/exp21-uaspeech-foundation) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-08 - train_batch_size: 2 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 1.18.3 - Tokenizers 0.13.2

minhtoan/t5-small-wikilingua-vietnamese

minhtoan

2022-11-27T06:23:09Z

141

3

transformers

[ "transformers", "pytorch", "t5", "text2text-generation", "summarization", "vi", "dataset:Wikilingua", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-11-24T03:47:40Z

--- language: vi datasets: - Wikilingua tags: - summarization license: mit widget: - text: "Hoa quả và rau thường rẻ hơn khi vào mùa. Thường thì các cửa hàng thực phẩm sẽ giảm giá cho các mặt hàng nông sản đang vào mùa vì họ biết khách hàng luôn chờ đợi những loại nào vào thời gian nào trong năm. Hơn nữa rau quả đúng mùa bao giờ cũng ngon hơn." inference: parameters: max_length: 256 --- # Vietnamese pretrain model for Abstractive Text Summarization task `State-of-the-art lightweights pretrained Transformer-based encoder-decoder model for Vietnamese.` ``Model trained on dataset Wikilingua of vietnamese language with input length = 512, output length = 256`` ## How to use ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("minhtoan/t5-small-wikilingua_vietnamese") model = AutoModelForSeq2SeqLM.from_pretrained("minhtoan/t5-small-wikilingua_vietnamese") model.cuda() src = 'Là Thủ tướng khi lạm phát gần 70%, ngân sách thâm hụt, thất nghiệp nhiều, nhưng với tư duy cởi mở, sẵn sàng "không làm được thì từ chức", ông Võ Văn Kiệt đã mang đến nhiều cải cách kinh tế cho Việt Nam. Trong trí nhớ của Chuyên gia kinh tế Phạm Chi Lan, cố Thủ tướng Võ Văn Kiệt là một lãnh đạo có tư duy kinh tế thị trường, chịu khó học hỏi, biết lắng nghe. "Những gì ông thấy tốt cho người dân thì sẽ tiếp thu rất nhanh", bà Lan nói với VnExpress. Còn PGS. TS Trần Minh Trưởng, Viện trưởng Viện Hồ Chí Minh và các lãnh tụ của Đảng (Học viện Chính trị - Hành chính quốc gia) nhận định về ông Võ Văn Kiệt trên Báo Nhân dân như "một trong những người đi đầu trong công cuộc đổi mới ở Việt Nam kể từ năm 1986", "tổng công trình sư nhiều dự án táo bạo". Nhậm chức Thủ tướng giữa muôn vàn khó khăn Năm 1985, sau một thập niên theo mô hình bao cấp, kinh tế Việt Nam ngày càng phụ thuộc vào viện trợ và vay nợ nước ngoài, lên đến 8,5 tỷ ruble (khoảng 3.500 tỷ đồng) và 1,9 tỷ USD (gần 45.600 tỷ đồng). Cứ thêm một năm, mô hình càng trục trặc và lỗi thời. Ngân sách bị thâm hụt và phải bù đắp bằng việc in tiền để chi tiêu. Việc lặp lại sai lầm "giá - lương - tiền" khiến lạm phát phi mã lên gần 775% vào năm 1986 và vẫn ở mức hai chữ số những năm 1990, 1991. Trong khi đó, tỷ lệ thất nghiệp lên đến 12,7%. Những chỉ số đáng báo động đã góp phần thức tỉnh tư duy các nhà lãnh đạo, dẫn đến quyết định đổi mới tại Đại hội VI (tháng 12/1986). Trong đó, đổi mới về kinh tế là một trong những nhiệm vụ trọng tâm, nhằm đưa đất nước thoát khỏi nguy khó. Ông Võ Văn Kiệt, lúc ấy đang là Phó chủ tịch Hội đồng Bộ trưởng, trở thành một lãnh đạo chủ chốt kiến tạo nên công cuộc này.' tokenized_text = tokenizer.encode(src, return_tensors="pt").cuda() model.eval() summary_ids = model.generate( tokenized_text, max_length=256, num_beams=5, repetition_penalty=2.5, length_penalty=1.0, early_stopping=True ) output = tokenizer.decode(summary_ids[0], skip_special_tokens=True) output ``` ```python output: Ông Võ Văn Kiệt, lúc ấy đang là Phó chủ tịch Hội đồng Bộ trưởng, trở thành một lãnh đạo chủ chốt kiến tạo nên công cuộc đổi mới ở Việt Nam kể từ năm 1986. Trong khi đó, ông Võ Văn Kiệt đã mang đến nhiều cải cách kinh tế cho Việt Nam. ``` ## Author ` Phan Minh Toan `

venetis/vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred

venetis

2022-11-27T06:04:36Z

188

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-25T19:17:51Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred results: [] ---  # vit-base-patch16-224_album_vitVMMRdb_make_model_album_pred This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.4670 - Accuracy: 0.8781 - Precision: 0.8768 - Recall: 0.8781 - F1: 0.8758 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 15 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:| | 3.5529 | 1.0 | 839 | 3.3687 | 0.3096 | 0.2809 | 0.3096 | 0.2246 | | 1.7855 | 2.0 | 1678 | 1.6042 | 0.6378 | 0.6187 | 0.6378 | 0.5996 | | 1.1054 | 3.0 | 2517 | 1.0105 | 0.7556 | 0.7512 | 0.7556 | 0.7385 | | 0.8179 | 4.0 | 3356 | 0.7794 | 0.8033 | 0.8020 | 0.8033 | 0.7934 | | 0.6057 | 5.0 | 4195 | 0.6479 | 0.8294 | 0.8274 | 0.8294 | 0.8212 | | 0.4709 | 6.0 | 5034 | 0.5817 | 0.8478 | 0.8477 | 0.8478 | 0.8428 | | 0.3962 | 7.0 | 5873 | 0.5333 | 0.8571 | 0.8570 | 0.8571 | 0.8527 | | 0.346 | 8.0 | 6712 | 0.5073 | 0.8638 | 0.8647 | 0.8638 | 0.8615 | | 0.2772 | 9.0 | 7551 | 0.4881 | 0.8681 | 0.8679 | 0.8681 | 0.8656 | | 0.2136 | 10.0 | 8390 | 0.4777 | 0.8719 | 0.8718 | 0.8719 | 0.8689 | | 0.1937 | 11.0 | 9229 | 0.4737 | 0.8734 | 0.8731 | 0.8734 | 0.8703 | | 0.1754 | 12.0 | 10068 | 0.4604 | 0.8758 | 0.8750 | 0.8758 | 0.8733 | | 0.1111 | 13.0 | 10907 | 0.4561 | 0.8790 | 0.8782 | 0.8790 | 0.8768 | | 0.1128 | 14.0 | 11746 | 0.4519 | 0.8808 | 0.8799 | 0.8808 | 0.8787 | | 0.1018 | 15.0 | 12585 | 0.4497 | 0.8813 | 0.8805 | 0.8813 | 0.8794 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

amagzari/t5-base-finetuned-samsum-v2

amagzari

2022-11-27T05:42:05Z

106

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-27T05:11:01Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-base-finetuned-samsum-v2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 44.5129 ---  # t5-base-finetuned-samsum-v2 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4881 - Rouge1: 44.5129 - Rouge2: 20.9037 - Rougel: 37.3032 - Rougelsum: 41.2293 - Gen Len: 16.7482 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 1.6551 | 1.0 | 1841 | 1.4881 | 44.5129 | 20.9037 | 37.3032 | 41.2293 | 16.7482 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

shibing624/songnet-base-chinese-couplet

shibing624

2022-11-27T05:41:01Z

0

2

null

[ "pytorch", "SongNet", "zh", "Text2Text-Generation", "license:apache-2.0", "region:us" ]

null

2022-11-26T12:05:24Z

--- language: - zh tags: - SongNet - pytorch - zh - Text2Text-Generation license: "apache-2.0" widget: - text: "丹枫江冷人初去" --- # SongNet for Chinese Couplet(songnet-base-chinese-couplet) Model SongNet中文对联仿写模型 `songnet-base-chinese-couplet` evaluate couplet test data： The overall performance of SongNet on couplet **test**: |input_text|predict| |:--- |:--- | |一句相思吟岁月，千杯美酒醉风情|一生只剩诗和酒，满腹无关雪与梅| 在Couplet测试集上生成结果满足字数相同、词性对齐、词面对齐、形似要求，针对性的SongNet网络结构，在语义对仗工整和平仄合律上的效果明显优于T5和GPT2等模型。 SongNet的网络结构： ![arch](songnet-network.png) ## Usage 本项目开源在文本生成项目：[textgen](https://github.com/shibing624/textgen)，可支持SongNet模型，通过如下命令调用： Install package: ```shell pip install -U textgen ``` ```python import sys sys.path.append('..') from textgen.language_modeling import SongNetModel model = SongNetModel(model_type='songnet', model_name='shibing624/songnet-base-chinese-couplet') sentences = [ "严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。", "<s1><s2>一句相思吟岁月</s>千杯美酒醉风情", "<s1><s2>几树梅花数竿竹</s>一潭秋水半屏山" "<s1><s2>未舍东江开口咏</s>且施妙手点睛来", "<s1><s2>一去二三里</s>烟村四五家", ] print("inputs:", sentences) print("outputs:", model.generate(sentences)) sentences = [ "<s1><s2>一句____月</s>千杯美酒__情", "<s1><s2>一去二三里</s>烟村__家</s>亭台__座</s>八__枝花", ] print("inputs:", sentences) print("outputs:", model.fill_mask(sentences)) ``` output: ```shell inputs: ['严蕊<s1>如梦令<s2>道是梨花不是。</s>道是杏花不是。</s>白白与红红，别是东风情味。</s>曾记。</s>曾记。</s>人在武陵微醉。', '<s1><s2>一句相思吟岁月</s>千杯美酒醉风情', '<s1><s2>几树梅花数竿竹</s>一潭秋水半屏山<s1><s2>未舍东江开口咏</s>且施妙手点睛来', '<s1><s2>一去二三里</s>烟村四五家'] outputs: ['<bos>盛世欣开新气象</s>春联喜绘大文章</s>春天铺锦笺，宏图更写好山山</s>新篇章</s>新篇章</s>神州高唱好年华</s>', '<bos>一曲琴音添雅韵</s>几回酒醉解愁思</s>', '<bos>三分天下隆中对</s>四面八方九派江山笔底留</s>', '<bos>春深花已老</s>夜静露方浓</s>'] inputs: ['<s1><s2>一句____月</s>千杯美酒__情', '<s1><s2>一去二三里</s>烟村__家</s>亭台__座</s>八__枝花'] outputs: ['<bos>一句佳诗吟盛月</s>千杯美酒祝春情</s>', '<bos>一去二三里</s>烟村百二家</s>亭台十二座</s>八里一枝花</s>'] ``` 模型文件组成： ``` songnet-base-chinese-couplet ├── pytorch_model.bin └── vocab.txt ``` ### 训练数据集 #### 中文对联数据集 - 数据：[对联github](https://github.com/wb14123/couplet-dataset)、[清洗过的对联github](https://github.com/v-zich/couplet-clean-dataset) - 相关内容 - [Huggingface](https://huggingface.co/) - [SongNet paper](https://aclanthology.org/2020.acl-main.68/) - [textgen](https://github.com/shibing624/textgen) 数据格式： ```text head -n 1 couplet_files/couplet/train/in.txt 晚风摇树树还挺 head -n 1 couplet_files/couplet/train/out.txt 晨露润花花更红 ``` 如果需要训练SongNet模型，请参考[https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py](https://github.com/shibing624/textgen/blob/main/examples/language_generation/training_zh_songnet_demo.py) ## Citation ```latex @software{textgen, author = {Xu Ming}, title = {textgen: Implementation of Text Generation models}, year = {2022}, url = {https://github.com/shibing624/textgen}, } ```

abdulmatinomotoso/pegasus_cnn_news_article_title_25000

abdulmatinomotoso

2022-11-27T05:28:16Z

103

0

transformers

[ "transformers", "pytorch", "tensorboard", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-27T02:01:57Z

--- tags: - generated_from_trainer model-index: - name: pegasus_cnn_news_article_title_25000 results: [] ---  # pegasus_cnn_news_article_title_25000 This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1857 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.2711 | 0.32 | 500 | 0.2287 | | 0.2009 | 0.64 | 1000 | 0.1924 | | 0.2077 | 0.96 | 1500 | 0.1857 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

jutaporn37678/stable-diffusion-v1-0

jutaporn37678

2022-11-27T05:10:46Z

7

1

transformers

[ "transformers", "pytorch", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "arxiv:2207.12598", "arxiv:2112.10752", "arxiv:2103.00020", "arxiv:2205.11487", "arxiv:1910.09700", "license:creativeml-openrail-m", "endpoints_compatible", "region:us" ]

text-to-image

2022-11-27T04:01:04Z

--- license: creativeml-openrail-m tags: - stable-diffusion - stable-diffusion-diffusers - text-to-image widget: - text: "A high tech solarpunk utopia in the Amazon rainforest" example_title: Amazon rainforest - text: "A pikachu fine dining with a view to the Eiffel Tower" example_title: Pikachu in Paris - text: "A mecha robot in a favela in expressionist style" example_title: Expressionist robot - text: "an insect robot preparing a delicious meal" example_title: Insect robot - text: "A small cabin on top of a snowy mountain in the style of Disney, artstation" example_title: Snowy disney cabin extra_gated_prompt: |- This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies: 1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content 2. The authors claim no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license 3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license carefully here: https://huggingface.co/spaces/CompVis/stable-diffusion-license extra_gated_heading: Please read the LICENSE to access this model --- # Stable Diffusion v1-4 Model Card Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at [๐ค—'s Stable Diffusion with ๐งจDiffusers blog](https://huggingface.co/blog/stable_diffusion). The **Stable-Diffusion-v1-4** checkpoint was initialized with the weights of the [Stable-Diffusion-v1-2](https:/steps/huggingface.co/CompVis/stable-diffusion-v1-2) checkpoint and subsequently fine-tuned on 225k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). This weights here are intended to be used with the ๐งจ Diffusers library. If you are looking for the weights to be loaded into the CompVis Stable Diffusion codebase, [come here](https://huggingface.co/CompVis/stable-diffusion-v-1-4-original) ## Model Details - **Developed by:** Robin Rombach, Patrick Esser - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** [The CreativeML OpenRAIL M license](https://huggingface.co/spaces/CompVis/stable-diffusion-license) is an [Open RAIL M license](https://www.licenses.ai/blog/2022/8/18/naming-convention-of-responsible-ai-licenses), adapted from the work that [BigScience](https://bigscience.huggingface.co/) and [the RAIL Initiative](https://www.licenses.ai/) are jointly carrying in the area of responsible AI licensing. See also [the article about the BLOOM Open RAIL license](https://bigscience.huggingface.co/blog/the-bigscience-rail-license) on which our license is based. - **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([CLIP ViT-L/14](https://arxiv.org/abs/2103.00020)) as suggested in the [Imagen paper](https://arxiv.org/abs/2205.11487). - **Resources for more information:** [GitHub Repository](https://github.com/CompVis/stable-diffusion), [Paper](https://arxiv.org/abs/2112.10752). - **Cite as:** @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ## Examples We recommend using [๐ค—'s Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion. ### PyTorch ```bash pip install --upgrade diffusers transformers scipy ``` Run this command to log in with your HF Hub token if you haven't before: ```bash huggingface-cli login ``` Running the pipeline with the default PNDM scheduler: ```python import torch from diffusers import StableDiffusionPipeline model_id = "CompVis/stable-diffusion-v1-4" device = "cuda" pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` **Note**: If you are limited by GPU memory and have less than 4GB of GPU RAM available, please make sure to load the StableDiffusionPipeline in float16 precision instead of the default float32 precision as done above. You can do so by telling diffusers to expect the weights to be in float16 precision: ```py import torch pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to(device) pipe.enable_attention_slicing() prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` To swap out the noise scheduler, pass it to `from_pretrained`: ```python from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler model_id = "CompVis/stable-diffusion-v1-4" # Use the Euler scheduler here instead scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler") pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16, revision="fp16") pipe = pipe.to("cuda") prompt = "a photo of an astronaut riding a horse on mars" image = pipe(prompt).images[0] image.save("astronaut_rides_horse.png") ``` ### JAX/Flax To use StableDiffusion on TPUs and GPUs for faster inference you can leverage JAX/Flax. Running the pipeline with default PNDMScheduler ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="flax", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` **Note**: If you are limited by TPU memory, please make sure to load the `FlaxStableDiffusionPipeline` in `bfloat16` precision instead of the default `float32` precision as done above. You can do so by telling diffusers to load the weights from "bf16" branch. ```python import jax import numpy as np from flax.jax_utils import replicate from flax.training.common_utils import shard from diffusers import FlaxStableDiffusionPipeline pipeline, params = FlaxStableDiffusionPipeline.from_pretrained( "CompVis/stable-diffusion-v1-4", revision="bf16", dtype=jax.numpy.bfloat16 ) prompt = "a photo of an astronaut riding a horse on mars" prng_seed = jax.random.PRNGKey(0) num_inference_steps = 50 num_samples = jax.device_count() prompt = num_samples * [prompt] prompt_ids = pipeline.prepare_inputs(prompt) # shard inputs and rng params = replicate(params) prng_seed = jax.random.split(prng_seed, 8) prompt_ids = shard(prompt_ids) images = pipeline(prompt_ids, params, prng_seed, num_inference_steps, jit=True).images images = pipeline.numpy_to_pil(np.asarray(images.reshape((num_samples,) + images.shape[-3:]))) ``` # Uses ## Direct Use The model is intended for research purposes only. Possible research areas and tasks include - Safe deployment of models which have the potential to generate harmful content. - Probing and understanding the limitations and biases of generative models. - Generation of artworks and use in design and other artistic processes. - Applications in educational or creative tools. - Research on generative models. Excluded uses are described below. ### Misuse, Malicious Use, and Out-of-Scope Use _Note: This section is taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), but applies in the same way to Stable Diffusion v1_. The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes. #### Out-of-Scope Use The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model. #### Misuse and Malicious Use Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to: - Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc. - Intentionally promoting or propagating discriminatory content or harmful stereotypes. - Impersonating individuals without their consent. - Sexual content without consent of the people who might see it. - Mis- and disinformation - Representations of egregious violence and gore - Sharing of copyrighted or licensed material in violation of its terms of use. - Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use. ## Limitations and Bias ### Limitations - The model does not achieve perfect photorealism - The model cannot render legible text - The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to โ€A red cube on top of a blue sphereโ€ - Faces and people in general may not be generated properly. - The model was trained mainly with English captions and will not work as well in other languages. - The autoencoding part of the model is lossy - The model was trained on a large-scale dataset [LAION-5B](https://laion.ai/blog/laion-5b/) which contains adult material and is not fit for product use without additional safety mechanisms and considerations. - No additional measures were used to deduplicate the dataset. As a result, we observe some degree of memorization for images that are duplicated in the training data. The training data can be searched at [https://rom1504.github.io/clip-retrieval/](https://rom1504.github.io/clip-retrieval/) to possibly assist in the detection of memorized images. ### Bias While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases. Stable Diffusion v1 was trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/), which consists of images that are primarily limited to English descriptions. Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for. This affects the overall output of the model, as white and western cultures are often set as the default. Further, the ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts. ### Safety Module The intended use of this model is with the [Safety Checker](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/safety_checker.py) in Diffusers. This checker works by checking model outputs against known hard-coded NSFW concepts. The concepts are intentionally hidden to reduce the likelihood of reverse-engineering this filter. Specifically, the checker compares the class probability of harmful concepts in the embedding space of the `CLIPTextModel` *after generation* of the images. The concepts are passed into the model with the generated image and compared to a hand-engineered weight for each NSFW concept. ## Training **Training Data** The model developers used the following dataset for training the model: - LAION-2B (en) and subsets thereof (see next section) **Training Procedure** Stable Diffusion v1-4 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training, - Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4 - Text prompts are encoded through a ViT-L/14 text-encoder. - The non-pooled output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention. - The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We currently provide four checkpoints, which were trained as follows. - [`stable-diffusion-v1-1`](https://huggingface.co/CompVis/stable-diffusion-v1-1): 237,000 steps at resolution `256x256` on [laion2B-en](https://huggingface.co/datasets/laion/laion2B-en). 194,000 steps at resolution `512x512` on [laion-high-resolution](https://huggingface.co/datasets/laion/laion-high-resolution) (170M examples from LAION-5B with resolution `>= 1024x1024`). - [`stable-diffusion-v1-2`](https://huggingface.co/CompVis/stable-diffusion-v1-2): Resumed from `stable-diffusion-v1-1`. 515,000 steps at resolution `512x512` on "laion-improved-aesthetics" (a subset of laion2B-en, filtered to images with an original size `>= 512x512`, estimated aesthetics score `> 5.0`, and an estimated watermark probability `< 0.5`. The watermark estimate is from the LAION-5B metadata, the aesthetics score is estimated using an [improved aesthetics estimator](https://github.com/christophschuhmann/improved-aesthetic-predictor)). - [`stable-diffusion-v1-3`](https://huggingface.co/CompVis/stable-diffusion-v1-3): Resumed from `stable-diffusion-v1-2`. 195,000 steps at resolution `512x512` on "laion-improved-aesthetics" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - [`stable-diffusion-v1-4`](https://huggingface.co/CompVis/stable-diffusion-v1-4) Resumed from `stable-diffusion-v1-2`.225,000 steps at resolution `512x512` on "laion-aesthetics v2 5+" and 10 % dropping of the text-conditioning to improve [classifier-free guidance sampling](https://arxiv.org/abs/2207.12598). - **Hardware:** 32 x 8 x A100 GPUs - **Optimizer:** AdamW - **Gradient Accumulations**: 2 - **Batch:** 32 x 8 x 2 x 4 = 2048 - **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant ## Evaluation Results Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0) and 50 PLMS sampling steps show the relative improvements of the checkpoints: ![pareto](https://huggingface.co/CompVis/stable-diffusion/resolve/main/v1-variants-scores.jpg) Evaluated using 50 PLMS steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores. ## Environmental Impact **Stable Diffusion v1** **Estimated Emissions** Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact. - **Hardware Type:** A100 PCIe 40GB - **Hours used:** 150000 - **Cloud Provider:** AWS - **Compute Region:** US-east - **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 11250 kg CO2 eq. ## Citation ```bibtex @InProceedings{Rombach_2022_CVPR, author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn}, title = {High-Resolution Image Synthesis With Latent Diffusion Models}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {10684-10695} } ``` *This model card was written by: Robin Rombach and Patrick Esser and is based on the [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).*

pglee/github-issue-classifier

pglee

2022-11-27T05:10:22Z

102

0

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-27T05:04:12Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: github-issue-classifier results: [] ---  # github-issue-classifier This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0684 - Accuracy: 0.875 - F1: 0.0455 - Precision: 1.0 - Recall: 0.0233 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 6 | 0.0888 | 0.8720 | 0.0 | 0.0 | 0.0 | | No log | 2.0 | 12 | 0.0700 | 0.8720 | 0.0 | 0.0 | 0.0 | | No log | 3.0 | 18 | 0.0713 | 0.8720 | 0.0851 | 0.5 | 0.0465 | | No log | 4.0 | 24 | 0.0684 | 0.875 | 0.0455 | 1.0 | 0.0233 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

razent/SciFive-large-PMC

razent

2022-11-27T04:04:16Z

8

1

transformers

[ "transformers", "pytorch", "tf", "t5", "text2text-generation", "token-classification", "text-classification", "question-answering", "text-generation", "en", "dataset:pmc/open_access", "arxiv:2106.03598", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:05Z

--- language: - en tags: - token-classification - text-classification - question-answering - text2text-generation - text-generation datasets: - pmc/open_access --- # SciFive PMC Large ## Introduction Paper: [SciFive: a text-to-text transformer model for biomedical literature](https://arxiv.org/abs/2106.03598) Authors: _Long N. Phan, James T. Anibal, Hieu Tran, Shaurya Chanana, Erol Bahadroglu, Alec Peltekian, Grégoire Altan-Bonnet_ ## How to use For more details, do check out [our Github repo](https://github.com/justinphan3110/SciFive). ```python from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("razent/SciFive-large-PMC") model = AutoModelForSeq2SeqLM.from_pretrained("razent/SciFive-large-PMC") sentence = "Identification of APC2 , a homologue of the adenomatous polyposis coli tumour suppressor ." text = sentence + " </s>" encoding = tokenizer.encode_plus(text, pad_to_max_length=True, return_tensors="pt") input_ids, attention_masks = encoding["input_ids"].to("cuda"), encoding["attention_mask"].to("cuda") outputs = model.generate( input_ids=input_ids, attention_mask=attention_masks, max_length=256, early_stopping=True ) for output in outputs: line = tokenizer.decode(output, skip_special_tokens=True, clean_up_tokenization_spaces=True) print(line) ```

cadaeic/test-digimon-diffusion

cadaeic

2022-11-27T03:41:13Z

0

1

null

[ "license:openrail", "region:us" ]

null

2022-11-27T03:00:59Z

--- license: "openrail" --- Experimental [Stable Diffusion 1.5](https://huggingface.co/runwayml/stable-diffusion-v1-5) finetune on this [Digimon BLIP Caption Dataset](https://huggingface.co/datasets/ionghin/digimon-blip-captions) Results and output are very rough, trained on slightly less than 900 images

Signorlimone/Caricaturizer

Signorlimone

2022-11-27T02:50:37Z

0

9

null

[ "region:us" ]

null

2022-11-27T02:31:55Z

use pcrc style to kick-in the model

anrilombard/bert-finetuned-ner

anrilombard

2022-11-27T02:46:14Z

128

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-11-27T02:38:33Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 model-index: - name: bert-finetuned-ner results: [] ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

pglee/outputs

pglee

2022-11-27T02:01:47Z

103

1

transformers

[ "transformers", "pytorch", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-26T09:24:02Z

--- license: mit tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: outputs results: [] ---  # outputs This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0926 - Accuracy: 0.8780 - F1: 0.3881 - Precision: 0.5417 - Recall: 0.3023 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 256 - eval_batch_size: 512 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:| | No log | 1.0 | 6 | 0.0874 | 0.8810 | 0.4118 | 0.56 | 0.3256 | | No log | 2.0 | 12 | 0.0936 | 0.8839 | 0.4000 | 0.5909 | 0.3023 | | No log | 3.0 | 18 | 0.0922 | 0.8780 | 0.3881 | 0.5417 | 0.3023 | | No log | 4.0 | 24 | 0.0926 | 0.8780 | 0.3881 | 0.5417 | 0.3023 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.1.0 - Tokenizers 0.12.1

psrohith98/distilbert-base-uncased-finetuned-emotion

psrohith98

2022-11-27T02:01:11Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-11-21T02:12:03Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.932 - name: F1 type: f1 value: 0.9319377153170552 ---  # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.1500 - Accuracy: 0.932 - F1: 0.9319 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.7995 | 1.0 | 250 | 0.2679 | 0.9145 | 0.9122 | | 0.2111 | 2.0 | 500 | 0.1731 | 0.9265 | 0.9265 | | 0.1399 | 3.0 | 750 | 0.1517 | 0.938 | 0.9381 | | 0.1118 | 4.0 | 1000 | 0.1500 | 0.932 | 0.9319 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

mynameisai/jq

mynameisai

2022-11-27T00:51:12Z

7

0

diffusers

[ "diffusers", "text-to-image", "stable-diffusion", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2022-11-27T00:48:34Z

--- license: creativeml-openrail-m tags: - text-to-image - stable-diffusion --- ### JQ Dreambooth model trained by mynameisai with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb) Or you can run your new concept via `diffusers` [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb) Sample pictures of this concept: ![0](https://huggingface.co/mynameisai/jq/resolve/main/sample_images/JQ_(01).png)

jacobthebanana/vit-synthetic-asl-numbers

jacobthebanana

2022-11-26T23:49:08Z

16

0

transformers

[ "transformers", "jax", "vit", "image-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-11-26T20:44:22Z

Technical details: - Base model: `google/vit-base-patch16-224` - Dataset: [synthetic ASL number dataset](https://www.kaggle.com/datasets/lexset/synthetic-asl-numbers). - Learning rate: 0.0001 - Effective training batch size: 16 (2 items per TPU core over 8 cores.) > Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)

adrien-alloreview/whisper-small-hi

adrien-alloreview

2022-11-26T23:17:31Z

86

0

transformers

[ "transformers", "pytorch", "tensorboard", "whisper", "automatic-speech-recognition", "hf-asr-leaderboard", "generated_from_trainer", "hi", "dataset:mozilla-foundation/common_voice_11_0", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-26T22:12:16Z

--- language: - hi license: apache-2.0 tags: - hf-asr-leaderboard - generated_from_trainer datasets: - mozilla-foundation/common_voice_11_0 metrics: - wer model-index: - name: Whisper Small Hi - Sanchit Gandhi results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Common Voice 11.0 type: mozilla-foundation/common_voice_11_0 args: 'config: hi, split: test' metrics: - name: Wer type: wer value: 0.0 ---  # Whisper Small Hi - Sanchit Gandhi This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset. It achieves the following results on the evaluation set: - Loss: 0.0001 - Wer: 0.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 500 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:---:| | 0.0001 | 40.0 | 200 | 0.0001 | 0.0 | | 0.0001 | 80.0 | 400 | 0.0001 | 0.0 | ### Framework versions - Transformers 4.25.0.dev0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

amagzari/t5-v1_1-small-finetuned-samsum

amagzari

2022-11-26T22:42:53Z

108

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "summarization", "generated_from_trainer", "dataset:samsum", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

summarization

2022-11-26T22:19:28Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - samsum metrics: - rouge model-index: - name: t5-v1_1-small-finetuned-samsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: samsum type: samsum config: samsum split: train args: samsum metrics: - name: Rouge1 type: rouge value: 0.40608242084369006 ---  # t5-v1_1-small-finetuned-samsum This model is a fine-tuned version of [google/t5-v1_1-small](https://huggingface.co/google/t5-v1_1-small) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 2.0053 - Rouge1: 0.4061 - Rouge2: 0.1804 - Rougel: 0.3478 - Rougelsum: 0.3774 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:| | 3.9788 | 1.0 | 1842 | 2.2499 | 0.3743 | 0.1569 | 0.3191 | 0.3486 | | 2.9091 | 2.0 | 3684 | 2.1052 | 0.3875 | 0.1680 | 0.3329 | 0.3607 | | 2.6807 | 3.0 | 5526 | 2.0270 | 0.4009 | 0.1778 | 0.3439 | 0.3734 | | 2.5917 | 4.0 | 7368 | 2.0053 | 0.4061 | 0.1804 | 0.3478 | 0.3774 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

AtulSingh31/t5-small-finetuned-xsum

AtulSingh31

2022-11-26T22:30:31Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-26T10:09:28Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum results: [] ---  # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 8 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | No log | 8.0 | 51 | 4.6838 | 9.554 | 7.8337 | 9.3938 | 9.5417 | 18.9109 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.13.0+cpu - Datasets 2.7.1 - Tokenizers 0.13.2

huggingtweets/h3xenbrenner2-s4m31p4n-wnbagirlfriend

huggingtweets

2022-11-26T22:07:12Z

4

0

transformers

[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-11-15T23:41:09Z

--- language: en thumbnail: http://www.huggingtweets.com/h3xenbrenner2-s4m31p4n-wnbagirlfriend/1669500428178/predictions.png tags: - huggingtweets widget: - text: "My dream is" --- <div class="inline-flex flex-col" style="line-height: 1.5;"> <div class="flex"> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396839225249734657/GG6ve7Qv_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1529675700772302848/uXtYNx_v_400x400.jpg')"> </div> <div style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1427129645888114693/HsNIpekZ_400x400.jpg')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 & ppigg & jody</div> <div style="text-align: center; font-size: 14px;">@h3xenbrenner2-s4m31p4n-wnbagirlfriend</div> </div> I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets). Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)! ## How does it work? The model uses the following pipeline. ![pipeline](https://github.com/borisdayma/huggingtweets/blob/master/img/pipeline.png?raw=true) To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI). ## Training data The model was trained on tweets from h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 & ppigg & jody. | Data | h b 🇧🇷🇫🇷🇳🇱🇪🇸🇵🇹 | ppigg | jody | | --- | --- | --- | --- | | Tweets downloaded | 1302 | 2858 | 3144 | | Retweets | 72 | 975 | 85 | | Short tweets | 161 | 569 | 713 | | Tweets kept | 1069 | 1314 | 2346 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/uk5vto8w/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline. ## Training procedure The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @h3xenbrenner2-s4m31p4n-wnbagirlfriend's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1zuoo2wb) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1zuoo2wb/artifacts) is logged and versioned. ## How to use You can use this model directly with a pipeline for text generation: ```python from transformers import pipeline generator = pipeline('text-generation', model='huggingtweets/h3xenbrenner2-s4m31p4n-wnbagirlfriend') generator("My dream is", num_return_sequences=5) ``` ## Limitations and bias The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias). In addition, the data present in the user's tweets further affects the text generated by the model. ## About *Built by Boris Dayma* [![Follow](https://img.shields.io/twitter/follow/borisdayma?style=social)](https://twitter.com/intent/follow?screen_name=borisdayma) For more details, visit the project repository. [![GitHub stars](https://img.shields.io/github/stars/borisdayma/huggingtweets?style=social)](https://github.com/borisdayma/huggingtweets)

ajitjadhav/t5-small-finetuned-t5-summarization_3

ajitjadhav

2022-11-26T21:45:12Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-11-26T17:54:47Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: t5-small-finetuned-t5-summarization_3 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: train args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 24.4517 ---  # t5-small-finetuned-t5-summarization_3 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.8102 - Rouge1: 24.4517 - Rouge2: 11.7161 - Rougel: 20.205 - Rougelsum: 23.053 - Gen Len: 18.9999 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 2.0073 | 1.0 | 17945 | 1.8144 | 24.4341 | 11.7155 | 20.1969 | 23.0364 | 18.9999 | | 1.9855 | 2.0 | 35890 | 1.8102 | 24.4517 | 11.7161 | 20.205 | 23.053 | 18.9999 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

jhakaran1/bert-base-uncased-bert-mlm

jhakaran1

2022-11-26T21:35:34Z

199

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-11-26T20:01:48Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-uncased-bert-mlm results: [] ---  # bert-base-uncased-bert-mlm This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.8491 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 2.2439 | 1.0 | 208 | 1.9789 | | 2.008 | 2.0 | 416 | 1.8568 | | 1.9535 | 3.0 | 624 | 1.8443 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

kejian/final-cond-25-0.05-again

kejian

2022-11-26T21:11:00Z

2

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-25T18:28:16Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-25-0.05-again results: [] ---  # kejian/final-cond-25-0.05-again This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0.000475}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-25-0.05-again', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/27z94dvy

leviethoang/wav2vec2-large-xls-r-300m-turkish-colab

leviethoang

2022-11-26T20:29:44Z

105

0

transformers

[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-11-26T16:34:26Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-large-xls-r-300m-turkish-colab results: [] ---  # wav2vec2-large-xls-r-300m-turkish-colab This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3755 - Wer: 0.3054 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 3.9475 | 3.67 | 400 | 0.7146 | 0.7507 | | 0.4396 | 7.34 | 800 | 0.4402 | 0.4574 | | 0.1955 | 11.01 | 1200 | 0.4027 | 0.4056 | | 0.128 | 14.68 | 1600 | 0.4127 | 0.3826 | | 0.0977 | 18.35 | 2000 | 0.4156 | 0.3576 | | 0.0788 | 22.02 | 2400 | 0.3992 | 0.3394 | | 0.0637 | 25.69 | 2800 | 0.3801 | 0.3141 | | 0.0486 | 29.36 | 3200 | 0.3755 | 0.3054 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3

Euchale/ArcaneSilz

Euchale

2022-11-26T20:17:12Z

0

null

[ "region:us" ]

null

2022-11-26T17:40:54Z

A 50:50 merge of Arcane (https://huggingface.co/nitrosocke/Arcane-Diffusion) and Silz (https://huggingface.co/PiyarSquare/stable_diffusion_silz)

kejian/final-cond-10-0.05

kejian

2022-11-26T20:14:37Z

3

0

transformers

[ "transformers", "pytorch", "gpt2", "generated_from_trainer", "en", "dataset:kejian/codeparrot-train-more-filter-3.3b-cleaned", "license:apache-2.0", "text-generation-inference", "endpoints_compatible", "region:us" ]

null

2022-11-25T18:36:06Z

--- language: - en license: apache-2.0 tags: - generated_from_trainer datasets: - kejian/codeparrot-train-more-filter-3.3b-cleaned model-index: - name: kejian/final-cond-10-0.05 results: [] ---  # kejian/final-cond-10-0.05 This model was trained from scratch on the kejian/codeparrot-train-more-filter-3.3b-cleaned dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0008 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.01 - training_steps: 50354 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.23.0 - Pytorch 1.13.0+cu116 - Datasets 2.0.0 - Tokenizers 0.12.1 # Full config {'dataset': {'conditional_training_config': {'aligned_prefix': '<|aligned|>', 'drop_token_fraction': 0.05, 'misaligned_prefix': '<|misaligned|>', 'threshold': 0}, 'datasets': ['kejian/codeparrot-train-more-filter-3.3b-cleaned'], 'is_split_by_sentences': True}, 'generation': {'batch_size': 64, 'metrics_configs': [{}, {'n': 1}, {}], 'scenario_configs': [{'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 704, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'unconditional', 'num_samples': 512, 'prefix': '<|aligned|>', 'use_prompt_for_scoring': False}, {'display_as_html': True, 'generate_kwargs': {'do_sample': True, 'eos_token_id': 0, 'max_length': 272, 'min_length': 10, 'temperature': 0.7, 'top_k': 0, 'top_p': 0.9}, 'name': 'functions', 'num_samples': 512, 'prefix': '<|aligned|>', 'prompt_before_control': True, 'prompts_path': 'resources/functions_csnet.jsonl', 'use_prompt_for_scoring': True}], 'scorer_config': {}}, 'kl_gpt3_callback': {'gpt3_kwargs': {'model_name': 'code-cushman-001'}, 'max_tokens': 64, 'num_samples': 4096, 'prefix': '<|aligned|>'}, 'model': {'from_scratch': True, 'gpt2_config_kwargs': {'reorder_and_upcast_attn': True, 'scale_attn_by': True}, 'num_additional_tokens': 2, 'path_or_name': 'codeparrot/codeparrot-small'}, 'objective': {'name': 'MLE'}, 'tokenizer': {'path_or_name': 'codeparrot/codeparrot-small', 'special_tokens': ['<|aligned|>', '<|misaligned|>']}, 'training': {'dataloader_num_workers': 0, 'effective_batch_size': 64, 'evaluation_strategy': 'no', 'fp16': True, 'hub_model_id': 'kejian/final-cond-10-0.05', 'hub_strategy': 'all_checkpoints', 'learning_rate': 0.0008, 'logging_first_step': True, 'logging_steps': 1, 'num_tokens': 3300000000.0, 'output_dir': 'training_output', 'per_device_train_batch_size': 16, 'push_to_hub': True, 'remove_unused_columns': False, 'save_steps': 5000, 'save_strategy': 'steps', 'seed': 42, 'warmup_ratio': 0.01, 'weight_decay': 0.1}} # Wandb URL: https://wandb.ai/kejian/uncategorized/runs/3pfuv3vn

amagzari/bart-base-finetuned-cnn_dailymail

amagzari

2022-11-26T19:34:17Z

131

0

transformers

[ "transformers", "pytorch", "tensorboard", "bart", "text2text-generation", "summarization", "generated_from_trainer", "dataset:cnn_dailymail", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

summarization

2022-11-26T11:16:15Z

--- license: apache-2.0 tags: - summarization - generated_from_trainer datasets: - cnn_dailymail metrics: - rouge model-index: - name: bart-base-finetuned-cnn_dailymail results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: cnn_dailymail type: cnn_dailymail config: 3.0.0 split: train args: 3.0.0 metrics: - name: Rouge1 type: rouge value: 0.35105989316705805 ---  # bart-base-finetuned-cnn_dailymail This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the cnn_dailymail dataset. It achieves the following results on the evaluation set: - Loss: 1.5396 - Rouge1: 0.3511 - Rouge2: 0.1925 - Rougel: 0.3086 - Rougelsum: 0.3292 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5.6e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:| | 1.9486 | 1.0 | 35890 | 1.5941 | 0.3498 | 0.1893 | 0.3063 | 0.3272 | | 1.6706 | 2.0 | 71780 | 1.5601 | 0.3503 | 0.1916 | 0.3079 | 0.3279 | | 1.4809 | 3.0 | 107670 | 1.5423 | 0.3520 | 0.1923 | 0.3086 | 0.3295 | | 1.3293 | 4.0 | 143560 | 1.5396 | 0.3511 | 0.1925 | 0.3086 | 0.3292 | ### Framework versions - Transformers 4.24.0 - Pytorch 1.12.1+cu113 - Datasets 2.7.1 - Tokenizers 0.13.2

research-backup/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2

research-backup

2022-11-26T19:32:41Z

108

0

transformers

[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity_v6", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

feature-extraction

2022-11-26T14:07:12Z

--- datasets: - relbert/semeval2012_relational_similarity_v6 model-index: - name: relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2 results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.6438492063492064 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3609625668449198 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3649851632047478 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.44691495275152865 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.716 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.38596491228070173 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.375 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8740394756667169 - name: F1 (macro) type: f1_macro value: 0.8618347056127108 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7246478873239436 - name: F1 (macro) type: f1_macro value: 0.4392658356578824 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5243770314192849 - name: F1 (macro) type: f1_macro value: 0.5089556262366394 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9525631216526397 - name: F1 (macro) type: f1_macro value: 0.8724005012414945 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8564713256032591 - name: F1 (macro) type: f1_macro value: 0.8510225054091837 --- # relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2 RelBERT fine-tuned from [roberta-base](https://huggingface.co/roberta-base) on [relbert/semeval2012_relational_similarity_v6](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity_v6). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3609625668449198 - Accuracy on SAT: 0.3649851632047478 - Accuracy on BATS: 0.44691495275152865 - Accuracy on U2: 0.38596491228070173 - Accuracy on U4: 0.375 - Accuracy on Google: 0.716 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8740394756667169 - Micro F1 score on CogALexV: 0.7246478873239436 - Micro F1 score on EVALution: 0.5243770314192849 - Micro F1 score on K&H+N: 0.9525631216526397 - Micro F1 score on ROOT09: 0.8564713256032591 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.6438492063492064 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-base - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity_v6 - split: train - split_eval: validation - template_mode: manual - loss_function: info_loob - classification_loss: False - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 9 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 2 - exclude_relation: None - n_sample: 320 - gradient_accumulation: 8 - relation_level: None The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/relbert-roberta-base-semeval2012-v6-average-prompt-d-loob-2/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```