librarian-bots/model_cards_with_metadata · Datasets at Hugging Face

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-07-14 00:44:55	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 519 values	tags listlengths 1 4.05k	pipeline_tag stringclasses 55 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-07-14 00:44:41	card stringlengths 11 1.01M
ctu-aic/xlm-roberta-large-xnli-enfever_nli	ctu-aic	2022-10-21T13:52:57Z	4	0	transformers	[ "transformers", "pytorch", "tf", "xlm-roberta", "text-classification", "arxiv:2201.11115", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-21T13:47:11Z	('---\ndatasets:\n- ctu-aic/enfever_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',) # 🦾 xlm-roberta-large-xnli-enfever_nli Transformer model for Natural Language Inference in ['cs'] languages finetuned on ['ctu-aic/enfever_nli'] datasets. ## 🧰 Usage ### 👾 Using UKPLab `sentence_transformers` `CrossEncoder` The model was trained using the `CrossEncoder` API and we recommend it for its usage. ```python from sentence_transformers.cross_encoder import CrossEncoder model = CrossEncoder('ctu-aic/xlm-roberta-large-xnli-enfever_nli') scores = model.predict([["My first context.", "My first hypothesis."], ["Second context.", "Hypothesis."]]) ``` ### 🤗 Using Huggingface `transformers` ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-xnli-enfever_nli") tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-xnli-enfever_nli") ``` ## 🌳 Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## 👬 Authors The model was trained and uploaded by [ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en) (e-mail: [[email protected]](mailto:[email protected])) The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)). ## 🔐 License [cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0) ## 💬 Citation If you find this repository helpful, feel free to cite our publication: ``` @article{DBLP:journals/corr/abs-2201-11115, author = {Herbert Ullrich and Jan Drchal and Martin R{'{y}}par and Hana Vincourov{'{a}} and V{'{a}}clav Moravec}, title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification}, journal = {CoRR}, volume = {abs/2201.11115}, year = {2022}, url = {https://arxiv.org/abs/2201.11115}, eprinttype = {arXiv}, eprint = {2201.11115}, timestamp = {Tue, 01 Feb 2022 14:59:01 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
manirai91/enlm-r	manirai91	2022-10-21T13:50:54Z	73	0	transformers	[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-01T07:58:38Z	--- tags: - generated_from_trainer model-index: - name: enlm-r 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. --> # enlm-r This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4837 ## 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: 0.0006 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 8192 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 24000 - num_epochs: 81 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 6.4 \| 0.33 \| 160 \| 10.7903 \| \| 6.4 \| 0.66 \| 320 \| 10.1431 \| \| 6.4 \| 0.99 \| 480 \| 9.8708 \| \| 6.4 \| 0.33 \| 640 \| 9.3884 \| \| 6.4 \| 0.66 \| 800 \| 8.7352 \| \| 6.4 \| 0.99 \| 960 \| 8.3341 \| \| 6.4 \| 1.33 \| 1120 \| 8.0614 \| \| 6.4 \| 1.66 \| 1280 \| 7.8582 \| \| 4.2719 \| 1.99 \| 1440 \| 7.4879 \| \| 3.2 \| 3.3 \| 1600 \| 7.2689 \| \| 3.2 \| 3.63 \| 1760 \| 7.1434 \| \| 3.2 \| 3.96 \| 1920 \| 7.0576 \| \| 3.2 \| 4.29 \| 2080 \| 7.0030 \| \| 3.2 \| 4.62 \| 2240 \| 6.9612 \| \| 3.2 \| 4.95 \| 2400 \| 6.9394 \| \| 3.2 \| 5.28 \| 2560 \| 6.9559 \| \| 3.2 \| 5.61 \| 2720 \| 6.8964 \| \| 3.2 \| 5.94 \| 2880 \| 6.8939 \| \| 3.2 \| 6.27 \| 3040 \| 6.8871 \| \| 3.2 \| 6.6 \| 3200 \| 6.8771 \| \| 3.2 \| 6.93 \| 3360 \| 6.8617 \| \| 3.2 \| 7.26 \| 3520 \| 6.8472 \| \| 3.2 \| 7.59 \| 3680 \| 6.8283 \| \| 3.2 \| 7.92 \| 3840 \| 6.8082 \| \| 3.2 \| 8.25 \| 4000 \| 6.8119 \| \| 3.2 \| 8.58 \| 4160 \| 6.7962 \| \| 3.2 \| 8.91 \| 4320 \| 6.7751 \| \| 3.2 \| 9.24 \| 4480 \| 6.7405 \| \| 3.2 \| 9.57 \| 4640 \| 6.7412 \| \| 3.2 \| 9.9 \| 4800 \| 6.7279 \| \| 3.2 \| 10.22 \| 4960 \| 6.7069 \| \| 3.2 \| 10.55 \| 5120 \| 6.6998 \| \| 3.2 \| 10.88 \| 5280 \| 6.6875 \| \| 3.2 \| 11.22 \| 5440 \| 6.6580 \| \| 3.2 \| 11.55 \| 5600 \| 6.6402 \| \| 3.2 \| 11.88 \| 5760 \| 6.6281 \| \| 3.2 \| 12.21 \| 5920 \| 6.6181 \| \| 3.2 \| 12.54 \| 6080 \| 6.5995 \| \| 3.2 \| 12.87 \| 6240 \| 6.5970 \| \| 3.2 \| 13.2 \| 6400 \| 6.5772 \| \| 3.2 \| 13.53 \| 6560 \| 6.5594 \| \| 3.2 \| 13.85 \| 6720 \| 6.5400 \| \| 3.2 \| 14.19 \| 6880 \| 6.5396 \| \| 3.2 \| 14.51 \| 7040 \| 6.5211 \| \| 3.2 \| 14.84 \| 7200 \| 6.5140 \| \| 3.2 \| 15.18 \| 7360 \| 6.4002 \| \| 3.2 \| 15.5 \| 7520 \| 6.3170 \| \| 3.2 \| 15.83 \| 7680 \| 6.2621 \| \| 3.2 \| 16.16 \| 7840 \| 6.2253 \| \| 3.2 \| 16.49 \| 8000 \| 6.1722 \| \| 3.2 \| 16.82 \| 8160 \| 6.1106 \| \| 3.2 \| 17.15 \| 8320 \| 6.1281 \| \| 3.2 \| 17.48 \| 8480 \| 6.0019 \| \| 3.2 \| 17.81 \| 8640 \| 5.9069 \| \| 3.2 \| 18.14 \| 8800 \| 5.7105 \| \| 3.2 \| 18.47 \| 8960 \| 5.2741 \| \| 3.2 \| 18.8 \| 9120 \| 5.0369 \| \| 5.0352 \| 19.13 \| 9280 \| 4.8148 \| \| 4.5102 \| 19.26 \| 9440 \| 4.3175 \| \| 4.1247 \| 19.59 \| 9600 \| 3.9518 \| \| 3.8443 \| 20.12 \| 9760 \| 3.6712 \| \| 3.6334 \| 20.45 \| 9920 \| 3.4654 \| \| 3.4698 \| 20.78 \| 10080 \| 3.2994 \| \| 3.3267 \| 21.11 \| 10240 \| 3.1638 \| \| 3.2173 \| 21.44 \| 10400 \| 3.0672 \| \| 3.1255 \| 21.77 \| 10560 \| 2.9687 \| \| 3.0344 \| 22.1 \| 10720 \| 2.8865 \| \| 2.9645 \| 22.43 \| 10880 \| 2.8104 \| \| 2.9046 \| 22.76 \| 11040 \| 2.7497 \| \| 2.8707 \| 23.09 \| 11200 \| 2.7040 \| \| 2.7903 \| 23.42 \| 11360 \| 2.6416 \| \| 2.7339 \| 23.75 \| 11520 \| 2.5891 \| \| 2.6894 \| 24.08 \| 11680 \| 2.5370 \| \| 2.6461 \| 24.41 \| 11840 \| 2.4960 \| \| 2.5976 \| 24.74 \| 12000 \| 2.4508 \| \| 2.5592 \| 25.07 \| 12160 \| 2.4194 \| \| 2.5305 \| 25.4 \| 12320 \| 2.3790 \| \| 2.4993 \| 25.73 \| 12480 \| 2.3509 \| \| 2.465 \| 26.06 \| 12640 \| 2.3173 \| \| 2.4455 \| 26.39 \| 12800 \| 2.2934 \| \| 2.4107 \| 26.72 \| 12960 \| 2.2701 \| \| 2.3883 \| 27.05 \| 13120 \| 2.2378 \| \| 2.3568 \| 27.38 \| 13280 \| 2.2079 \| \| 2.3454 \| 27.71 \| 13440 \| 2.1919 \| \| 2.3207 \| 28.04 \| 13600 \| 2.1671 \| \| 2.2963 \| 28.37 \| 13760 \| 2.1513 \| \| 2.2738 \| 28.7 \| 13920 \| 2.1326 \| \| 2.2632 \| 29.03 \| 14080 \| 2.1176 \| \| 2.2413 \| 29.36 \| 14240 \| 2.0913 \| \| 2.2193 \| 29.69 \| 14400 \| 2.0772 \| \| 2.2169 \| 30.02 \| 14560 \| 2.0692 \| \| 2.1848 \| 30.35 \| 14720 \| 2.0411 \| \| 2.1693 \| 30.68 \| 14880 \| 2.0290 \| \| 2.1964 \| 31.01 \| 15040 \| 2.0169 \| \| 2.1467 \| 31.34 \| 15200 \| 2.0016 \| \| 2.1352 \| 31.67 \| 15360 \| 1.9880 \| \| 2.1152 \| 32.0 \| 15520 \| 1.9727 \| \| 2.1098 \| 32.33 \| 15680 \| 1.9604 \| \| 2.0888 \| 32.66 \| 15840 \| 1.9521 \| \| 2.0837 \| 32.99 \| 16000 \| 1.9394 \| \| 2.0761 \| 33.32 \| 16160 \| 1.9366 \| \| 2.0635 \| 33.65 \| 16320 \| 1.9200 \| \| 2.0631 \| 33.98 \| 16480 \| 1.9147 \| \| 2.0448 \| 34.31 \| 16640 \| 1.9053 \| \| 2.0452 \| 34.64 \| 16800 \| 1.8937 \| \| 2.0303 \| 34.97 \| 16960 \| 1.8801 \| \| 2.0184 \| 35.3 \| 17120 \| 1.8752 \| \| 2.0115 \| 35.63 \| 17280 \| 1.8667 \| \| 2.0042 \| 35.96 \| 17440 \| 1.8626 \| \| 2.002 \| 36.29 \| 17600 \| 1.8565 \| \| 1.9918 \| 36.62 \| 17760 \| 1.8475 \| \| 1.9868 \| 36.95 \| 17920 \| 1.8420 \| \| 1.9796 \| 37.28 \| 18080 \| 1.8376 \| \| 1.976 \| 37.61 \| 18240 \| 1.8318 \| \| 1.9647 \| 37.94 \| 18400 \| 1.8225 \| \| 1.9561 \| 38.27 \| 18560 \| 1.8202 \| \| 1.9544 \| 38.6 \| 18720 \| 1.8084 \| \| 1.9454 \| 38.93 \| 18880 \| 1.8057 \| \| 1.9333 \| 39.26 \| 19040 \| 1.8030 \| \| 1.9411 \| 39.59 \| 19200 \| 1.7966 \| \| 1.9289 \| 39.92 \| 19360 \| 1.7865 \| \| 1.9261 \| 40.25 \| 19520 \| 1.7815 \| \| 1.9207 \| 40.58 \| 19680 \| 1.7881 \| \| 1.9164 \| 40.91 \| 19840 \| 1.7747 \| \| 1.9152 \| 41.24 \| 20000 \| 1.7786 \| \| 1.914 \| 41.57 \| 20160 \| 1.7664 \| \| 1.901 \| 41.9 \| 20320 \| 1.7586 \| \| 1.8965 \| 42.23 \| 20480 \| 1.7554 \| \| 1.8982 \| 42.56 \| 20640 \| 1.7524 \| \| 1.8941 \| 42.89 \| 20800 \| 1.7460 \| \| 1.8834 \| 43.22 \| 20960 \| 1.7488 \| \| 1.8841 \| 43.55 \| 21120 \| 1.7486 \| \| 1.8846 \| 43.88 \| 21280 \| 1.7424 \| \| 1.8763 \| 44.21 \| 21440 \| 1.7352 \| \| 1.8688 \| 44.54 \| 21600 \| 1.7349 \| \| 1.8714 \| 44.87 \| 21760 \| 1.7263 \| \| 1.8653 \| 45.2 \| 21920 \| 1.7282 \| \| 1.8673 \| 45.53 \| 22080 \| 1.7195 \| \| 1.8682 \| 45.85 \| 22240 \| 1.7266 \| \| 1.8532 \| 46.19 \| 22400 \| 1.7180 \| \| 1.8553 \| 46.51 \| 22560 \| 1.7137 \| \| 1.8569 \| 46.84 \| 22720 \| 1.7158 \| \| 1.8469 \| 47.18 \| 22880 \| 1.7117 \| \| 1.845 \| 47.5 \| 23040 \| 1.7031 \| \| 1.8475 \| 47.83 \| 23200 \| 1.7089 \| \| 1.845 \| 48.16 \| 23360 \| 1.7018 \| \| 1.8391 \| 48.49 \| 23520 \| 1.6945 \| \| 1.8456 \| 48.82 \| 23680 \| 1.7015 \| \| 1.8305 \| 49.15 \| 23840 \| 1.6964 \| \| 1.8324 \| 49.48 \| 24000 \| 1.6900 \| \| 1.7763 \| 49.81 \| 24160 \| 1.6449 \| \| 1.7728 \| 50.14 \| 24320 \| 1.6436 \| \| 1.7576 \| 50.47 \| 24480 \| 1.6268 \| \| 1.7354 \| 50.8 \| 24640 \| 1.6088 \| \| 1.74 \| 51.13 \| 24800 \| 1.6156 \| \| 1.7251 \| 51.06 \| 24960 \| 1.6041 \| \| 1.719 \| 51.39 \| 25120 \| 1.5938 \| \| 1.7257 \| 52.12 \| 25280 \| 1.5983 \| \| 1.7184 \| 52.45 \| 25440 \| 1.5919 \| \| 1.7093 \| 52.78 \| 25600 \| 1.5848 \| \| 1.7114 \| 53.11 \| 25760 \| 1.5922 \| \| 1.7076 \| 53.44 \| 25920 \| 1.5843 \| \| 1.7 \| 53.77 \| 26080 \| 1.5807 \| \| 1.7027 \| 54.1 \| 26240 \| 1.5811 \| \| 1.704 \| 54.43 \| 26400 \| 1.5766 \| \| 1.6958 \| 54.76 \| 26560 \| 1.5756 \| \| 1.6976 \| 55.09 \| 26720 \| 1.5773 \| \| 1.6944 \| 55.42 \| 26880 \| 1.5725 \| \| 1.6891 \| 55.75 \| 27040 \| 1.5685 \| \| 1.6936 \| 56.08 \| 27200 \| 1.5750 \| \| 1.6893 \| 56.41 \| 27360 \| 1.5696 \| \| 1.6886 \| 56.74 \| 27520 \| 1.5643 \| \| 1.6936 \| 57.07 \| 27680 \| 1.5691 \| \| 1.6883 \| 57.4 \| 27840 \| 1.5718 \| \| 1.6832 \| 57.73 \| 28000 \| 1.5660 \| \| 1.9222 \| 28.03 \| 28160 \| 1.7107 \| \| 1.7838 \| 28.19 \| 28320 \| 1.6345 \| \| 1.7843 \| 28.36 \| 28480 \| 1.6445 \| \| 1.7809 \| 28.52 \| 28640 \| 1.6461 \| \| 1.783 \| 28.69 \| 28800 \| 1.6505 \| \| 1.7869 \| 28.85 \| 28960 \| 1.6364 \| \| 1.778 \| 29.02 \| 29120 \| 1.6363 \| \| 1.775 \| 29.18 \| 29280 \| 1.6364 \| \| 1.7697 \| 29.34 \| 29440 \| 1.6345 \| \| 1.7719 \| 29.51 \| 29600 \| 1.6261 \| \| 1.7454 \| 61.16 \| 29760 \| 1.6099 \| \| 1.741 \| 61.49 \| 29920 \| 1.6006 \| \| 1.7314 \| 62.02 \| 30080 \| 1.6041 \| \| 1.7314 \| 62.35 \| 30240 \| 1.5914 \| \| 1.7246 \| 62.68 \| 30400 \| 1.5917 \| \| 1.7642 \| 63.01 \| 30560 \| 1.5923 \| \| 1.7221 \| 63.34 \| 30720 \| 1.5857 \| \| 1.7185 \| 63.67 \| 30880 \| 1.5836 \| \| 1.7022 \| 64.0 \| 31040 \| 1.5836 \| \| 1.7107 \| 64.33 \| 31200 \| 1.5739 \| \| 1.7082 \| 64.66 \| 31360 \| 1.5724 \| \| 1.7055 \| 64.99 \| 31520 \| 1.5734 \| \| 1.7019 \| 65.32 \| 31680 \| 1.5707 \| \| 1.699 \| 65.65 \| 31840 \| 1.5649 \| \| 1.6963 \| 65.98 \| 32000 \| 1.5685 \| \| 1.6935 \| 66.31 \| 32160 \| 1.5673 \| \| 1.6899 \| 66.64 \| 32320 \| 1.5648 \| \| 1.6869 \| 66.97 \| 32480 \| 1.5620 \| \| 1.6867 \| 67.3 \| 32640 \| 1.5564 \| \| 1.6861 \| 67.63 \| 32800 \| 1.5552 \| \| 1.6831 \| 67.96 \| 32960 \| 1.5496 \| \| 1.6778 \| 68.29 \| 33120 \| 1.5479 \| \| 1.6742 \| 68.62 \| 33280 \| 1.5501 \| \| 1.6737 \| 68.95 \| 33440 \| 1.5441 \| \| 1.6725 \| 69.28 \| 33600 \| 1.5399 \| \| 1.6683 \| 69.61 \| 33760 \| 1.5398 \| \| 1.6689 \| 69.94 \| 33920 \| 1.5374 \| \| 1.6634 \| 70.27 \| 34080 \| 1.5385 \| \| 1.6638 \| 70.6 \| 34240 \| 1.5332 \| \| 1.6614 \| 70.93 \| 34400 \| 1.5329 \| \| 1.6544 \| 71.26 \| 34560 \| 1.5292 \| \| 1.6532 \| 71.59 \| 34720 \| 1.5268 \| \| 1.6511 \| 71.92 \| 34880 \| 1.5225 \| \| 1.6506 \| 72.25 \| 35040 \| 1.5219 \| \| 1.6496 \| 72.58 \| 35200 \| 1.5202 \| \| 1.6468 \| 72.91 \| 35360 \| 1.5199 \| \| 1.6424 \| 73.24 \| 35520 \| 1.5220 \| \| 1.642 \| 73.57 \| 35680 \| 1.5145 \| \| 1.6415 \| 73.9 \| 35840 \| 1.5139 \| \| 1.6419 \| 74.23 \| 36000 \| 1.5120 \| \| 1.633 \| 74.56 \| 36160 \| 1.5113 \| \| 1.6354 \| 74.89 \| 36320 \| 1.5139 \| \| 1.6312 \| 75.22 \| 36480 \| 1.5068 \| \| 1.6298 \| 75.55 \| 36640 \| 1.5056 \| \| 1.6268 \| 75.88 \| 36800 \| 1.5000 \| \| 1.6277 \| 76.21 \| 36960 \| 1.5033 \| \| 1.6198 \| 76.54 \| 37120 \| 1.4988 \| \| 1.6246 \| 76.87 \| 37280 \| 1.4978 \| \| 1.6184 \| 77.2 \| 37440 \| 1.4966 \| \| 1.6187 \| 77.53 \| 37600 \| 1.4954 \| \| 1.6192 \| 77.85 \| 37760 \| 1.4951 \| \| 1.6134 \| 78.19 \| 37920 \| 1.4936 \| \| 1.6176 \| 78.51 \| 38080 \| 1.4908 \| \| 1.6103 \| 78.84 \| 38240 \| 1.4904 \| \| 1.612 \| 79.18 \| 38400 \| 1.4919 \| \| 1.611 \| 79.5 \| 38560 \| 1.4891 \| \| 1.6082 \| 79.83 \| 38720 \| 1.4837 \| \| 1.6047 \| 80.16 \| 38880 \| 1.4859 \| \| 1.6058 \| 80.49 \| 39040 \| 1.4814 \| \| 1.602 \| 80.82 \| 39200 \| 1.4837 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
DemangeJeremy/4-sentiments-with-flaubert	DemangeJeremy	2022-10-21T13:46:12Z	13	0	transformers	[ "transformers", "pytorch", "flaubert", "text-classification", "sentiments", "french", "flaubert-large", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-03-02T23:29:04Z	--- language: fr tags: - sentiments - text-classification - flaubert - french - flaubert-large --- # Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive) ### Comment l'utiliser ? ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased') loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert") nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer) print(nlp("Je suis plutôt confiant.")) ``` ``` [{'label': 'OBJECTIVE', 'score': 0.3320835530757904}] ``` ## Résultats de l'évaluation du modèle \| Epoch \| Validation Loss \| Samples Per Second \| \|:------:\|:--------------:\|:------------------:\| \| 1 \| 2.219246 \| 49.476000 \| \| 2 \| 1.883753 \| 47.259000 \| \| 3 \| 1.747969 \| 44.957000 \| \| 4 \| 1.695606 \| 43.872000 \| \| 5 \| 1.641470 \| 45.726000 \| ## Citation Pour toute utilisation de ce modèle, merci d'utiliser cette citation : > Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>
orkg/orkgnlp-predicates-clustering	orkg	2022-10-21T13:40:57Z	0	0	null	[ "onnx", "license:mit", "region:us" ]	null	2022-05-09T08:02:12Z	--- license: mit --- This Repository includes the files required to run the `Predicates Clustering` ORKG-NLP service. Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. The [Scikit-Learn](https://scikit-learn.org/stable/) models are converted using [skl2onnx](https://github.com/onnx/sklearn-onnx) and may not include all original scikit-learn functionalities.
asi/albert-act-base	asi	2022-10-21T13:26:29Z	10	1	transformers	[ "transformers", "pytorch", "tf", "tensorboard", "albert_act", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:1603.08983", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	null	2022-10-11T20:33:26Z	--- license: apache-2.0 language: en datasets: - wikipedia - bookcorpus model-index: - name: asi/albert-act-base results: - task: type: text-classification name: CoLA dataset: type: glue name: CoLA # General Language Understanding Evaluation benchmark (GLUE) split: cola metrics: - type: matthews_correlation value: 36.7 name: Matthew's Corr - task: type: text-classification name: SST-2 dataset: type: glue name: SST-2 # The Stanford Sentiment Treebank split: sst2 metrics: - type: accuracy value: 87.8 name: Accuracy - task: type: text-classification name: MRPC dataset: type: glue name: MRPC # Microsoft Research Paraphrase Corpus split: mrpc metrics: - type: accuracy value: 81.4 name: Accuracy - type: f1 value: 86.5 name: F1 - task: type: text-similarity name: STS-B dataset: type: glue name: STS-B # Semantic Textual Similarity Benchmark split: stsb metrics: - type: spearmanr value: 83.0 name: Spearman Corr - type: pearsonr value: 84.2 name: Pearson Corr - task: type: text-classification name: QQP dataset: type: glue name: QQP # Quora Question Pairs split: qqp metrics: - type: f1 value: 68.5 name: F1 - type: accuracy value: 87.7 name: Accuracy - task: type: text-classification name: MNLI-m dataset: type: glue name: MNLI-m # MultiNLI Matched split: mnli_matched metrics: - type: accuracy value: 79.9 name: Accuracy - task: type: text-classification name: MNLI-mm dataset: type: glue name: MNLI-mm # MultiNLI Matched split: mnli_mismatched metrics: - type: accuracy value: 79.2 name: Accuracy - task: type: text-classification name: QNLI dataset: type: glue name: QNLI # Question NLI split: qnli metrics: - type: accuracy value: 89.0 name: Accuracy - task: type: text-classification name: RTE dataset: type: glue name: RTE # Recognizing Textual Entailment split: rte metrics: - type: accuracy value: 63.0 name: Accuracy - task: type: text-classification name: WNLI dataset: type: glue name: WNLI # Winograd NLI split: wnli metrics: - type: accuracy value: 65.1 name: Accuracy --- # Adaptive Depth Transformers Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. ## Model architecture We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token. We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state. ## Model use The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first: ```bash !pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$ ``` Then you can use the model directly. ```python from act import AlbertActConfig, AlbertActModel, TFAlbertActModel from transformers import AlbertTokenizer tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base') model = AlbertActModel.from_pretrained('asi/albert-act-base') _ = model.eval() inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt") outputs = model(**inputs) outputs.updates # tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]]) ``` ## Citations ### BibTeX entry and citation info If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/): ```bibtex @inproceedings{simoulin-crabbe-2021-many, title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers", author = "Simoulin, Antoine and Crabb{\'e}, Benoit", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-srw.23", doi = "10.18653/v1/2021.acl-srw.23", pages = "221--228", } ``` ### References ><div id="graves-2016">Alex Graves. 2016. <a href="https://arxiv.org/abs/1603.08983" target="_blank">Adaptive computation time for recurrent neural networks.</a> CoRR, abs/1603.08983.</div>
huggingtweets/tszzl	huggingtweets	2022-10-21T12:32:06Z	5	1	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-03-02T23:29:05Z	--- language: en thumbnail: http://www.huggingtweets.com/tszzl/1666355521581/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/1572784789291401216/1WrwslUF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">roon</div> <div style="text-align: center; font-size: 14px;">@tszzl</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 roon. \| Data \| roon \| \| --- \| --- \| \| Tweets downloaded \| 3207 \| \| Retweets \| 779 \| \| Short tweets \| 375 \| \| Tweets kept \| 2053 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nr9oggv1/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 @tszzl's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7/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/tszzl') 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)
Yinxing/ddpm-butterflies-128	Yinxing	2022-10-21T12:05:23Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-21T10:51:28Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset 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-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` 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: 16 - eval_batch_size: 16 - 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/Yinxing/ddpm-butterflies-128/tensorboard?#scalars)
ashish23993/t5-small-finetuned-xsum-a	ashish23993	2022-10-21T10:48:19Z	6	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	2022-10-21T10:43:22Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum-a 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-a This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| No log \| 1.0 \| 8 \| 2.2554 \| 21.1449 \| 9.0713 \| 17.7765 \| 20.1134 \| 19.0 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
hezzze/a2c-AntBulletEnv-v0	hezzze	2022-10-21T09:34:26Z	2	0	stable-baselines3	[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T09:33:16Z	--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1658.74 +/- 204.55 name: mean_reward verified: false --- # A2C Agent playing AntBulletEnv-v0 This is a trained model of a A2C agent playing AntBulletEnv-v0 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
asapcreditrepairusa/Credit-Repair-Houston	asapcreditrepairusa	2022-10-21T09:33:48Z	0	0	null	[ "region:us" ]	null	2022-10-21T09:33:11Z	ASAP Credit Repair has two critical missions, 1) to provide an effective and inexpensive option for credit repair and 2) to provide the best customer service experience along the way. We hope you choose [ASAP Credit Repair](https://asapcreditrepairusa.com) for your future credit repair needs.
nicolarici/LawBERT-IT_trained	nicolarici	2022-10-21T08:00:23Z	1	0	transformers	[ "transformers", "pytorch", "bert", "pretraining", "endpoints_compatible", "region:us" ]	null	2022-10-21T07:45:05Z	LawBERT-IT An Italian BERT model for the legal domain. The code used for developing and training the model and the dataset used to extract the new words and continue the training of the BERT model are available on [GitHub](https://github.com/nicolarici/LawBERT-IT).
teacookies/autotrain-21102022-cert-1827562840	teacookies	2022-10-21T07:41:52Z	12	0	transformers	[ "transformers", "pytorch", "autotrain", "token-classification", "unk", "dataset:teacookies/autotrain-data-21102022-cert", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	token-classification	2022-10-21T07:29:56Z	--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - teacookies/autotrain-data-21102022-cert co2_eq_emissions: emissions: 19.94429730071814 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1827562840 - CO2 Emissions (in grams): 19.9443 ## Validation Metrics - Loss: 0.028 - Accuracy: 0.992 - Precision: 0.820 - Recall: 0.885 - F1: 0.851 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-21102022-cert-1827562840 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
Bugpie/dummy-model	Bugpie	2022-10-21T07:04:44Z	15	0	transformers	[ "transformers", "pytorch", "camembert", "fill-mask", "fr", "dataset:oscar", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-19T11:49:58Z	--- language: fr license: mit datasets: - oscar --- ## Model description CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. ## Evaluation The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI). ## Limitations and bias Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). This model was pretrinaed on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the [OSCAR dataset card](https://huggingface.co/datasets/oscar), include the following: > The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages. > Constructed from Common Crawl, Personal and sensitive information might be present. ## Training data OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture. ## How to use -Filling masks using pipeline ```python >>> from transformers import pipeline >>> camembert_fill_mask = pipeline("fill-mask", model="camembert-base") >>> results = camembert_fill_mask("Le camembert est <mask> :)") >>> result [{'score': 0.49091097712516785, 'token': 7200, 'token_str': 'délicieux', 'sequence': 'Le camembert est délicieux :)'}, {'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent', 'sequence': 'Le camembert est excellent :)'}, {'score': 0.03453319892287254, 'token': 26202, 'token_str': 'succulent', 'sequence': 'Le camembert est succulent :)'}, {'score': 0.03303128108382225, 'token': 528, 'token_str': 'meilleur', 'sequence': 'Le camembert est meilleur :)'}, {'score': 0.030076386407017708, 'token': 1654, 'token_str': 'parfait', 'sequence': 'Le camembert est parfait :)'}] ``` -Extract contextual embedding features from Camembert output ```python import torch >>> tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") >>> encoded_sentence = tokenizer.encode(tokenized_sentence) # Can be done in one step : tokenize.encode("J'aime le camembert !") >>> tokenized_sentence ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] >>> encoded_sentence [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] ``` ![128791.gif](https://s3.amazonaws.com/moonup/production/uploads/1666329291279-634fe2e8cfefce6e57795f69.gif) [more about](https://youtu.be/dMTy6C4UiQ4)
nlp-waseda/roberta-large-japanese-with-auto-jumanpp	nlp-waseda	2022-10-21T06:55:27Z	1,733	3	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "ja", "dataset:wikipedia", "dataset:cc100", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-15T05:40:40Z	--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia - cc100 mask_token: "[MASK]" widget: - text: "早稲田大学で自然言語処理を[MASK]する。" --- # nlp-waseda/roberta-large-japanese-with-auto-jumanpp ## Model description This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-with-auto-jumanpp") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-with-auto-jumanpp") sentence = '早稲田大学で自然言語処理を[MASK]する。' encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. ## Tokenization `BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). ## Vocabulary The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). ## Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took two weeks using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 6e-5 - per_device_train_batch_size: 103 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 5 - total_train_batch_size: 4120 - max_seq_length: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6 - lr_scheduler_type: linear - training_steps: 670000 - warmup_steps: 10000 - mixed_precision_training: Native AMP ## Performance on JGLUE See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.
salascorp/distilroberta-base-mrpc-glue-oscar-salas2	salascorp	2022-10-21T06:40:47Z	3	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-21T06:36:59Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer model-index: - name: distilroberta-base-mrpc-glue-oscar-salas2 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. --> # distilroberta-base-mrpc-glue-oscar-salas2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 1.5094 ## 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 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
NinedayWang/PolyCoder-0.4B	NinedayWang	2022-10-21T06:03:41Z	97	4	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "arxiv:2202.13169", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-20T09:45:17Z	This is a PolyCoder model with 0.4B parameters, presented in the paper ["A Systematic Evaluation of Large Language Models of Code"](https://arxiv.org/pdf/2202.13169.pdf) (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code). The model was trained on 249 GB of code across 12 programming languages. Note - this model requires `transformers` version of at least 4.23.0: ``` pip install transformers==4.23.0 ``` For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs) If you use this model, please cite: ``` @inproceedings{ xu2022polycoder, title={A Systematic Evaluation of Large Language Models of Code}, author={Frank F. Xu and Uri Alon and Graham Neubig and Vincent Josua Hellendoorn}, booktitle={Deep Learning for Code Workshop}, year={2022}, url={https://openreview.net/forum?id=SLcEnoObJZq} } ```
NinedayWang/PolyCoder-2.7B	NinedayWang	2022-10-21T06:03:23Z	314	50	transformers	[ "transformers", "pytorch", "gpt_neox", "text-generation", "arxiv:2202.13169", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-20T09:47:34Z	This is a PolyCoder model with 2.7B parameters, presented in the paper ["A Systematic Evaluation of Large Language Models of Code"](https://arxiv.org/pdf/2202.13169.pdf) (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code). The model was trained on 249 GB of code across 12 programming languages. Note - this model requires `transformers` version of at least 4.23.0: ``` pip install transformers==4.23.0 ``` For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs) If you use this model, please cite: ``` @inproceedings{ xu2022polycoder, title={A Systematic Evaluation of Large Language Models of Code}, author={Frank F. Xu and Uri Alon and Graham Neubig and Vincent Josua Hellendoorn}, booktitle={Deep Learning for Code Workshop}, year={2022}, url={https://openreview.net/forum?id=SLcEnoObJZq} } ```
jo-kwsm/distilbert-base-uncased-finetuned-emotion	jo-kwsm	2022-10-21T06:02:46Z	3	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	2022-10-21T03:31:19Z	--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9253582087556043 --- <!-- 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: 0.2244 - Accuracy: 0.9255 - F1: 0.9254 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8602 \| 1.0 \| 250 \| 0.3344 \| 0.901 \| 0.8979 \| \| 0.263 \| 2.0 \| 500 \| 0.2244 \| 0.9255 \| 0.9254 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3
stanford-crfm/levanter-gpt	stanford-crfm	2022-10-21T05:33:26Z	9	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-03T03:16:18Z	--- pipeline_tag: text-generation widget: text: You could not prevent a thunderstorm, but you could use --- Levanter GPT is trained on OpenWebText2. More complete model card will be made in the future.
api19750904/VM-Fast_Check	api19750904	2022-10-21T04:30:55Z	72	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-21T04:30:42Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: VM-Fast_Check results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9101123809814453 --- # VM-Fast_Check Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### person drinking ![person drinking](images/person_drinking.jpg) #### person smoking ![person smoking](images/person_smoking.jpg) #### swimsuit boy ![swimsuit boy](images/swimsuit_boy.jpg) #### swimsuit girl ![swimsuit girl](images/swimsuit_girl.jpg)
edbeeching/atari_2B_atari_yarsrevenge_2222	edbeeching	2022-10-21T04:26:27Z	3	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T04:25:25Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_yarsrevenge type: atari_yarsrevenge metrics: - type: mean_reward value: 336431.19 +/- 148269.98 name: mean_reward verified: false --- A(n) APPO model trained on the atari_yarsrevenge environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_2B_atari_wizardofwor_2222	edbeeching	2022-10-21T04:21:31Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T04:20:36Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_wizardofwor type: atari_wizardofwor metrics: - type: mean_reward value: 61420.00 +/- 23105.79 name: mean_reward verified: false --- A(n) APPO model trained on the atari_wizardofwor environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
huggingtweets/elonmusk-mar15sa-sergiorocks	huggingtweets	2022-10-21T04:07:50Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-21T04:06:32Z	--- language: en thumbnail: http://www.huggingtweets.com/elonmusk-mar15sa-sergiorocks/1666325239514/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/1580062742693699584/RJ5EI7PS_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/1142324885550751744/wVNatx7J_400x400.png')"> </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/566329118489194496/f_ALTi7v_400x400.jpeg')"> </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">Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg</div> <div style="text-align: center; font-size: 14px;">@elonmusk-mar15sa-sergiorocks</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 Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg. \| Data \| Elon Musk \| Sergio Pereira 🚀 \| Marissa Goldberg \| \| --- \| --- \| --- \| --- \| \| Tweets downloaded \| 3200 \| 3250 \| 3248 \| \| Retweets \| 133 \| 18 \| 301 \| \| Short tweets \| 949 \| 54 \| 110 \| \| Tweets kept \| 2118 \| 3178 \| 2837 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ahul38aq/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 @elonmusk-mar15sa-sergiorocks's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2/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/elonmusk-mar15sa-sergiorocks') 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)
edbeeching/atari_2B_atari_timepilot_2222	edbeeching	2022-10-21T03:38:54Z	0	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T03:37:51Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_timepilot type: atari_timepilot metrics: - type: mean_reward value: 88855.00 +/- 25100.17 name: mean_reward verified: false --- A(n) APPO model trained on the atari_timepilot environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_2B_atari_tennis_2222	edbeeching	2022-10-21T03:32:46Z	1	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T03:31:38Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tennis type: atari_tennis metrics: - type: mean_reward value: 23.00 +/- 1.10 name: mean_reward verified: false --- A(n) APPO model trained on the atari_tennis environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
huggingtweets/levelsio	huggingtweets	2022-10-21T03:28:44Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-21T03:27:24Z	--- language: en thumbnail: http://www.huggingtweets.com/levelsio/1666322920443/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/1562107516066095106/IUccJ78Y_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">@levelsio</div> <div style="text-align: center; font-size: 14px;">@levelsio</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 @levelsio. \| Data \| @levelsio \| \| --- \| --- \| \| Tweets downloaded \| 3243 \| \| Retweets \| 173 \| \| Short tweets \| 535 \| \| Tweets kept \| 2535 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tof4zha8/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 @levelsio's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur/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/levelsio') 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)
Adipta/setfit-model-test-2	Adipta	2022-10-21T02:39:05Z	2	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-21T02:38:54Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(*encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
xxxxxxxxxxxxxxxxxxxxxx/model-y	xxxxxxxxxxxxxxxxxxxxxx	2022-10-21T01:49:43Z	0	0	null	[ "license:wtfpl", "region:us" ]	null	2022-10-17T07:21:03Z	--- license: wtfpl --- # wwww ```typescript import React, { CSSProperties, PropsWithRef } from 'react'; import MarkdownPreview, { MarkdownPreviewProps } from '@uiw/react-markdown-preview'; import { ITextAreaProps } from './components/TextArea'; import { ICommand } from './commands'; import { ContextStore, PreviewType } from './Context'; import './index.less'; export interface IProps { prefixCls?: string; className?: string; } export interface MDEditorProps extends Omit<React.HTMLAttributes<HTMLDivElement>, 'onChange'>, IProps { /** * The Markdown value. / value?: string; /* * Event handler for the `onChange` event. / onChange?: (value?: string, event?: React.ChangeEvent<HTMLTextAreaElement>, state?: ContextStore) => void; /* * editor height change listener / onHeightChange?: (value?: CSSProperties['height'], oldValue?: CSSProperties['height'], state?: ContextStore) => void; /* * Can be used to make `Markdown Editor` focus itself on initialization. Defaults to on. * it will be set to true when either the source `textarea` is focused, * or it has an `autofocus` attribute and no other element is focused. / autoFocus?: ITextAreaProps['autoFocus']; /* * The height of the editor. * ⚠️ `Dragbar` is invalid when `height` parameter percentage. / height?: CSSProperties['height']; /* * Custom toolbar heigth * @default 29px * * @deprecated toolbar height adaptive: https://github.com/uiwjs/react-md-editor/issues/427 * / toolbarHeight?: number; /* * Show drag and drop tool. Set the height of the editor. / visibleDragbar?: boolean; /* * @deprecated use `visibleDragbar` / visiableDragbar?: boolean; /* * Show markdown preview. / preview?: PreviewType; /* * Full screen display editor. / fullscreen?: boolean; /* * Disable `fullscreen` setting body styles / overflow?: boolean; /* * Maximum drag height. `visibleDragbar=true` / maxHeight?: number; /* * Minimum drag height. `visibleDragbar=true` / minHeight?: number; /* * This is reset [react-markdown](https://github.com/rexxars/react-markdown) settings. / previewOptions?: Omit<MarkdownPreviewProps, 'source'>; /* * Set the `textarea` related props. / textareaProps?: ITextAreaProps; /* * Use div to replace TextArea or re-render TextArea * @deprecated Please use ~~`renderTextarea`~~ -> `components` / renderTextarea?: ITextAreaProps['renderTextarea']; /* * re-render element / components?: { /* Use div to replace TextArea or re-render TextArea / textarea?: ITextAreaProps['renderTextarea']; /* * Override the default command element * _`toolbar`_ < _`command[].render`_ / toolbar?: ICommand['render']; /* Custom markdown preview / preview?: (source: string, state: ContextStore, dispath: React.Dispatch<ContextStore>) => JSX.Element; }; /* * Disable editing area code highlighting. The value is `false`, which increases the editing speed. * @default true / highlightEnable?: boolean; /* * The number of characters to insert when pressing tab key. * Default `2` spaces. / tabSize?: number; /* * If `false`, the `tab` key inserts a tab character into the textarea. If `true`, the `tab` key executes default behavior e.g. focus shifts to next element. / defaultTabEnable?: boolean; /* * You can create your own commands or reuse existing commands. / commands?: ICommand[]; /* * Filter or modify your commands. * https://github.com/uiwjs/react-md-editor/issues/296 / commandsFilter?: (command: ICommand, isExtra: boolean) => false \| ICommand; /* * You can create your own commands or reuse existing commands. / extraCommands?: ICommand[]; /* * Hide the tool bar / hideToolbar?: boolean; /* Whether to enable scrolling / enableScroll?: boolean; /* Toolbar on bottom */ toolbarBottom?: boolean; } declare type Editor = React.FC<PropsWithRef<MDEditorProps>> & { Markdown: typeof MarkdownPreview; }; declare const mdEditor: Editor; export default mdEditor; ``` ## asdjk ### lskjdflskj as d s d
edbeeching/atari_2B_atari_yarsrevenge_1111	edbeeching	2022-10-21T00:01:51Z	7	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-21T00:00:47Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_yarsrevenge type: atari_yarsrevenge metrics: - type: mean_reward value: 224390.75 +/- 197367.31 name: mean_reward verified: false --- A(n) APPO model trained on the atari_yarsrevenge environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
edbeeching/atari_2B_atari_videopinball_1111	edbeeching	2022-10-20T23:54:10Z	6	0	sample-factory	[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-20T23:52:57Z	--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_videopinball type: atari_videopinball metrics: - type: mean_reward value: 372372.91 +/- 274249.66 name: mean_reward verified: false --- A(n) APPO model trained on the atari_videopinball environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
salascorp/distilroberta-base-mrpc-glue-oscar-salas	salascorp	2022-10-20T22:48:41Z	4	0	transformers	[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-20T01:44:30Z	--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue model-index: - name: distilroberta-base-mrpc-glue-oscar-salas 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. --> # distilroberta-base-mrpc-glue-oscar-salas This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6456 - eval_accuracy: 0.8260 - eval_f1: 0.8795 - eval_runtime: 30.3289 - eval_samples_per_second: 13.453 - eval_steps_per_second: 1.682 - epoch: 1.09 - step: 500 ## 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 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cpu - Datasets 2.6.1 - Tokenizers 0.13.1
huggingtweets/jiswooning-the3ammusician	huggingtweets	2022-10-20T22:27:14Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-20T22:26:00Z	--- language: en thumbnail: http://www.huggingtweets.com/jiswooning-the3ammusician/1666304830215/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/1560736534143422465/3oAu6oCD_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/1521185553382883334/fHjvh84L_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">TOR Kate & K8 misses KARD</div> <div style="text-align: center; font-size: 14px;">@jiswooning-the3ammusician</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 TOR Kate & K8 misses KARD. \| Data \| TOR Kate \| K8 misses KARD \| \| --- \| --- \| --- \| \| Tweets downloaded \| 3234 \| 3193 \| \| Retweets \| 1038 \| 1194 \| \| Short tweets \| 310 \| 208 \| \| Tweets kept \| 1886 \| 1791 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vcg0753/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 @jiswooning-the3ammusician's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii/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/jiswooning-the3ammusician') 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)
jayanta/cvt-13-384-22k-fv-finetuned-memes	jayanta	2022-10-20T22:05:58Z	42	0	transformers	[ "transformers", "pytorch", "tensorboard", "cvt", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-20T21:40:10Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: cvt-13-384-22k-fv-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8315301391035549 - name: Precision type: precision value: 0.8302128280229624 - name: Recall type: recall value: 0.8315301391035549 - name: F1 type: f1 value: 0.8292026505769348 --- <!-- 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. --> # cvt-13-384-22k-fv-finetuned-memes This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5761 - Accuracy: 0.8315 - Precision: 0.8302 - Recall: 0.8315 - F1: 0.8292 ## 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: 0.00012 - 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.3821 \| 0.99 \| 20 \| 1.2780 \| 0.4969 \| 0.5083 \| 0.4969 \| 0.4458 \| \| 1.0785 \| 1.99 \| 40 \| 0.8633 \| 0.6669 \| 0.6658 \| 0.6669 \| 0.6500 \| \| 0.8862 \| 2.99 \| 60 \| 0.7110 \| 0.7218 \| 0.7258 \| 0.7218 \| 0.7013 \| \| 0.665 \| 3.99 \| 80 \| 0.5515 \| 0.8045 \| 0.8137 \| 0.8045 \| 0.8050 \| \| 0.6056 \| 4.99 \| 100 \| 0.5956 \| 0.7960 \| 0.8041 \| 0.7960 \| 0.7846 \| \| 0.4779 \| 5.99 \| 120 \| 0.6229 \| 0.7937 \| 0.7945 \| 0.7937 \| 0.7857 \| \| 0.4554 \| 6.99 \| 140 \| 0.5355 \| 0.8099 \| 0.8126 \| 0.8099 \| 0.8086 \| \| 0.4249 \| 7.99 \| 160 \| 0.5447 \| 0.8269 \| 0.8275 \| 0.8269 \| 0.8236 \| \| 0.4313 \| 8.99 \| 180 \| 0.5530 \| 0.8153 \| 0.8140 \| 0.8153 \| 0.8132 \| \| 0.423 \| 9.99 \| 200 \| 0.5346 \| 0.8238 \| 0.8230 \| 0.8238 \| 0.8223 \| \| 0.3997 \| 10.99 \| 220 \| 0.5413 \| 0.8338 \| 0.8347 \| 0.8338 \| 0.8338 \| \| 0.4095 \| 11.99 \| 240 \| 0.5999 \| 0.8207 \| 0.8231 \| 0.8207 \| 0.8177 \| \| 0.3979 \| 12.99 \| 260 \| 0.5632 \| 0.8284 \| 0.8255 \| 0.8284 \| 0.8250 \| \| 0.3408 \| 13.99 \| 280 \| 0.5725 \| 0.8207 \| 0.8198 \| 0.8207 \| 0.8196 \| \| 0.3828 \| 14.99 \| 300 \| 0.5631 \| 0.8277 \| 0.8258 \| 0.8277 \| 0.8260 \| \| 0.3595 \| 15.99 \| 320 \| 0.6005 \| 0.8308 \| 0.8297 \| 0.8308 \| 0.8275 \| \| 0.3789 \| 16.99 \| 340 \| 0.5840 \| 0.8300 \| 0.8271 \| 0.8300 \| 0.8273 \| \| 0.3545 \| 17.99 \| 360 \| 0.5983 \| 0.8246 \| 0.8226 \| 0.8246 \| 0.8222 \| \| 0.3472 \| 18.99 \| 380 \| 0.5795 \| 0.8416 \| 0.8382 \| 0.8416 \| 0.8390 \| \| 0.355 \| 19.99 \| 400 \| 0.5761 \| 0.8315 \| 0.8302 \| 0.8315 \| 0.8292 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1
imodels/gpt-neo-2.7B-titles	imodels	2022-10-20T21:17:47Z	5	0	transformers	[ "transformers", "pytorch", "gpt_neo", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-10-17T18:36:43Z	--- license: apache-2.0 widget: - text: "2021\n\n" --- Full code and details at https://github.com/csinva/gpt-paper-title-generator Model - finetunes starting from the[gpt-neo-2.7B checkpoint](https://huggingface.co/EleutherAI/gpt-neo-2.7B) - for training details see [the training script](https://github.com/csinva/gpt-paper-title-generator/blob/0157f26be9b0763b4ea6480e5b149fdb8dff4626/gptneo/02_finetune_hf.py) - inference - should prepend with a year and two newlines before querying for a title, e.g. `2022\n\n` ```python from transformers import AutoModelForCausalLM, pipeline, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("csinva/gpt-neo-2.7B-titles") tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") pipe = pipeline('text-generation', model=model, tokenizer=tokenizer) pipe('2022\n\n') ``` Data - all [papers on arXiv](https://www.kaggle.com/datasets/Cornell-University/arxiv) in the categories cs.AI, cs.LG, stat.ML - date cutoff: only finetuned on papers with dat on or before Apr 1, 2022 - random 5% of papers also excluded - this results in 98,388 papers for finetuning - during finetuning each paper title was given starting with the prompt `<year>\n\n <title>\n` (e.g. `2022\n\n Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models\n`)
jayanta/swin-large-patch4-window7-224-fv-finetuned-memes	jayanta	2022-10-20T21:16:39Z	64	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	2022-10-20T19:49:36Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: swin-large-patch4-window7-224-fv-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8601236476043277 - name: Precision type: precision value: 0.8582306285016578 - name: Recall type: recall value: 0.8601236476043277 - name: F1 type: f1 value: 0.8582797853944862 --- <!-- 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-large-patch4-window7-224-fv-finetuned-memes This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6502 - Accuracy: 0.8601 - Precision: 0.8582 - Recall: 0.8601 - F1: 0.8583 ## 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: 0.00012 - 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.2077 \| 0.99 \| 20 \| 0.9499 \| 0.6461 \| 0.6764 \| 0.6461 \| 0.5863 \| \| 0.5687 \| 1.99 \| 40 \| 0.5365 \| 0.7975 \| 0.8018 \| 0.7975 \| 0.7924 \| \| 0.3607 \| 2.99 \| 60 \| 0.4007 \| 0.8423 \| 0.8419 \| 0.8423 \| 0.8398 \| \| 0.203 \| 3.99 \| 80 \| 0.3751 \| 0.8509 \| 0.8502 \| 0.8509 \| 0.8503 \| \| 0.1728 \| 4.99 \| 100 \| 0.4168 \| 0.8509 \| 0.8519 \| 0.8509 \| 0.8506 \| \| 0.0963 \| 5.99 \| 120 \| 0.4351 \| 0.8586 \| 0.8573 \| 0.8586 \| 0.8555 \| \| 0.0956 \| 6.99 \| 140 \| 0.4415 \| 0.8547 \| 0.8542 \| 0.8547 \| 0.8541 \| \| 0.079 \| 7.99 \| 160 \| 0.5312 \| 0.8501 \| 0.8475 \| 0.8501 \| 0.8459 \| \| 0.0635 \| 8.99 \| 180 \| 0.5376 \| 0.8601 \| 0.8578 \| 0.8601 \| 0.8577 \| \| 0.0593 \| 9.99 \| 200 \| 0.5060 \| 0.8609 \| 0.8615 \| 0.8609 \| 0.8604 \| \| 0.0656 \| 10.99 \| 220 \| 0.4997 \| 0.8617 \| 0.8573 \| 0.8617 \| 0.8587 \| \| 0.0561 \| 11.99 \| 240 \| 0.5430 \| 0.8586 \| 0.8604 \| 0.8586 \| 0.8589 \| \| 0.0523 \| 12.99 \| 260 \| 0.5354 \| 0.8624 \| 0.8643 \| 0.8624 \| 0.8626 \| \| 0.0489 \| 13.99 \| 280 \| 0.5539 \| 0.8609 \| 0.8572 \| 0.8609 \| 0.8577 \| \| 0.0487 \| 14.99 \| 300 \| 0.5785 \| 0.8609 \| 0.8591 \| 0.8609 \| 0.8591 \| \| 0.0485 \| 15.99 \| 320 \| 0.6186 \| 0.8601 \| 0.8578 \| 0.8601 \| 0.8573 \| \| 0.0518 \| 16.99 \| 340 \| 0.6342 \| 0.8624 \| 0.8612 \| 0.8624 \| 0.8606 \| \| 0.0432 \| 17.99 \| 360 \| 0.6302 \| 0.8586 \| 0.8598 \| 0.8586 \| 0.8580 \| \| 0.0469 \| 18.99 \| 380 \| 0.6323 \| 0.8617 \| 0.8606 \| 0.8617 \| 0.8604 \| \| 0.0426 \| 19.99 \| 400 \| 0.6502 \| 0.8601 \| 0.8582 \| 0.8601 \| 0.8583 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1
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yuik/ppo-LunarLander-v2	yuik	2022-10-20T21:09:21Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-20T21:08:56Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.74 +/- 20.11 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
jinhybr/layoutlm-funsd-tf	jinhybr	2022-10-20T20:48:26Z	10	0	transformers	[ "transformers", "tf", "tensorboard", "layoutlm", "token-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-20T20:10:28Z	--- tags: - generated_from_keras_callback model-index: - name: layoutlm-funsd-tf 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. --> # layoutlm-funsd-tf This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2509 - Validation Loss: 0.6942 - Train Overall Precision: 0.7291 - Train Overall Recall: 0.7888 - Train Overall F1: 0.7578 - Train Overall Accuracy: 0.8067 - Epoch: 7 ## 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': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Train Overall Precision \| Train Overall Recall \| Train Overall F1 \| Train Overall Accuracy \| Epoch \| \|:----------:\|:---------------:\|:-----------------------:\|:--------------------:\|:----------------:\|:----------------------:\|:-----:\| \| 1.6886 \| 1.4100 \| 0.2324 \| 0.2313 \| 0.2318 \| 0.5009 \| 0 \| \| 1.1702 \| 0.8486 \| 0.5971 \| 0.6618 \| 0.6278 \| 0.7338 \| 1 \| \| 0.7521 \| 0.7032 \| 0.6561 \| 0.7341 \| 0.6929 \| 0.7687 \| 2 \| \| 0.5727 \| 0.6268 \| 0.6736 \| 0.7662 \| 0.7169 \| 0.7957 \| 3 \| \| 0.4586 \| 0.6322 \| 0.6909 \| 0.7772 \| 0.7315 \| 0.7999 \| 4 \| \| 0.3725 \| 0.6378 \| 0.7134 \| 0.7782 \| 0.7444 \| 0.8096 \| 5 \| \| 0.2987 \| 0.6835 \| 0.7270 \| 0.7777 \| 0.7515 \| 0.8056 \| 6 \| \| 0.2509 \| 0.6942 \| 0.7291 \| 0.7888 \| 0.7578 \| 0.8067 \| 7 \| ### Framework versions - Transformers 4.23.1 - TensorFlow 2.6.0 - Datasets 2.6.1 - Tokenizers 0.13.1
asapcreditcolumbus/asapcreditrepaircolumbus	asapcreditcolumbus	2022-10-20T20:36:15Z	0	0	null	[ "region:us" ]	null	2022-10-20T20:35:14Z	Are you looking for [credit repair in Columbus](https://columbus.asapcreditrepairusa.com/)? You are at the right place. We’re not your average credit repair firm, we truly care, so we only charge for the items we pursue on your report. Not only does this make us one of the FASTEST credit restoration companies, but we’re also one of the most affordable.
Shaier/longformer_quail	Shaier	2022-10-20T19:58:53Z	4	0	transformers	[ "transformers", "pytorch", "longformer", "multiple-choice", "generated_from_trainer", "dataset:quail", "endpoints_compatible", "region:us" ]	multiple-choice	2022-10-20T15:42:17Z	--- tags: - generated_from_trainer datasets: - quail model-index: - name: longformer_quail 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. --> # longformer_quail This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the quail dataset. It achieves the following results on the evaluation set: - eval_loss: 1.9568 - eval_accuracy: 0.5791 - eval_runtime: 44.254 - eval_samples_per_second: 12.564 - eval_steps_per_second: 6.282 - epoch: 4.0 - step: 816 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 25 - total_train_batch_size: 50 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0
allenai/drug_combinations_lm_pubmedbert	allenai	2022-10-20T18:25:13Z	39	2	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "biomedical", "bioNLP", "en", "arxiv:2205.02289", "arxiv:2007.15779", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-19T11:25:49Z	--- language: - en tags: - biomedical - bioNLP --- This is a version of [PubmedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene.) which has been domain-adapted (via additional pretraining) to a set of PubMed abstracts that likely discuss multiple-drug therapies. This model was the strongest contextualized encoder in the experiments in the paper ["A Dataset for N-ary Relation Extraction of Drug Combinations"](https://arxiv.org/abs/2205.02289), when used as a component of a larger relation classification model (also hosted [here on Huggingface](https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt)). If you use this model, cite both ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, } ``` and ```latex @inproceedings{Tiktinsky2022ADF, title = "A Dataset for N-ary Relation Extraction of Drug Combinations", author = "Tiktinsky, Aryeh and Viswanathan, Vijay and Niezni, Danna and Meron Azagury, Dana and Shamay, Yosi and Taub-Tabib, Hillel and Hope, Tom and Goldberg, Yoav", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.233", doi = "10.18653/v1/2022.naacl-main.233", pages = "3190--3203", } ```
allenai/drug-combo-classifier-pubmedbert-dapt	allenai	2022-10-20T18:23:30Z	23	5	transformers	[ "transformers", "pytorch", "bert", "en", "arxiv:2205.02289", "license:mit", "endpoints_compatible", "region:us" ]	null	2022-05-04T03:20:11Z	--- language: en license: mit --- This is the baseline model used in most experiments in the paper ["A Dataset for N-ary Relation Extraction of Drug Combinations"](https://arxiv.org/abs/2205.02289). (for just the domain-adapted masked language model that we use underneath this model, see [here](https://huggingface.co/allenai/drug_combinations_lm_pubmedbert?text=Paxlovid+works+well+in+combination+with+%5BMASK%5D+for+treating+breast+cancer.)) Steps to load this model 1) Download accompanying code: ``` git clone https://github.com/allenai/drug-combo-extraction.git conda create --name drug_combo python=3.8.5 conda activate drug_combo ``` 2) Download model from Huggingface: ``` git lfs install git clone https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt ``` 3) Load model (`in Python`): ``` from modeling.model import load_model checkpoint_path = "drug-combo-classifier-pubmedbert-dapt" model, tokenizer, metadata = load_model(checkpoint_path) ```
jxm/u-PMLM-R	jxm	2022-10-20T18:05:26Z	5	2	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2004.11579", "endpoints_compatible", "region:us" ]	feature-extraction	2022-06-01T16:08:29Z	PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from [the authors' original implementation](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PMLM).
jxm/u-PMLM-A	jxm	2022-10-20T18:05:03Z	5	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2004.11579", "endpoints_compatible", "region:us" ]	feature-extraction	2022-06-01T17:37:45Z	PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-A" variant, adapted from [the authors' original implementation](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PMLM).
mprzibilla/super_large_finetune_M01	mprzibilla	2022-10-20T17:56:53Z	5	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-19T12:05:24Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: super_large_finetune_M01 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. --> # super_large_finetune_M01 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9906 - Wer: 1.0 ## 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: 0.0001 - train_batch_size: 20 - 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: 35440 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:------:\|:---------------:\|:---:\| \| 10.0626 \| 20.0 \| 70880 \| 3.0307 \| 1.0 \| \| 2.5319 \| 40.0 \| 141760 \| 3.0316 \| 1.0 \| \| 2.4978 \| 60.0 \| 212640 \| 3.0123 \| 1.0 \| \| 2.4849 \| 80.0 \| 283520 \| 2.9923 \| 1.0 \| \| 2.4776 \| 100.0 \| 354400 \| 3.0092 \| 1.0 \| \| 2.4733 \| 120.0 \| 425280 \| 2.9964 \| 1.0 \| \| 2.4702 \| 140.0 \| 496160 \| 2.9968 \| 1.0 \| \| 2.4686 \| 160.0 \| 567040 \| 2.9937 \| 1.0 \| \| 2.4669 \| 180.0 \| 637920 \| 2.9908 \| 1.0 \| \| 2.4661 \| 200.0 \| 708800 \| 2.9906 \| 1.0 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
rbroc/contrastive-user-encoder-singlepost	rbroc	2022-10-20T16:56:21Z	4	0	transformers	[ "transformers", "pytorch", "distilbert", "feature-extraction", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	feature-extraction	2022-10-19T08:56:38Z	--- language: - en license: apache-2.0 library_name: transformers --- ### Contrastive user encoder (single post) This model is a `DistilBertModel` trained by fine-tuning `distilbert-base-uncased` on author-based triplet loss. #### Details Training and evaluation details are provided in our EMNLP Findings paper: - Rocca, R., & Yarkoni, T. (2022), Language as a fingerprint: Self-supervised learning of user encodings using transformers, to appear in Findings of the Association for Computational Linguistics: EMNLP 2022 #### Training We fine-tuned DistilBERT on triplets consisting of: - a single Reddit submission from a given user (the "anchor") - see ```rbroc/contrastive-user-encoder-multipost``` for a model trained on aggregated embeddings of multiple anchors; - an additional post from the same user (a "positive example"); - a post from a different, randomly selected user (the "negative example") To compute the loss, we use [CLS] encoding of the anchor, positive example and negative example from the last layer of the DistilBERT encoder. We optimize for \\(max(\|\|f(a) - f(n)\|\| - \|\|f(a) - f(p)\|\| + \alpha,0)\\) where: - \\( f(a)\\) is the [CLS] encoding of the anchor; - \\( f(n) \\) is the [CLS] encoding of the negative example; - \\( f(p) \\) is the [CLS] encoding of the positive example; - \\( \alpha \\) is a tunable parameter called margin. Here, we tuned this to \\( \alpha = 1.0\\) #### Evaluation and usage The model yields performance advantages downstream user-based classification tasks. We encourage usage and benchmarking on tasks involving: - prediction of user traits (e.g., personality); - extraction of user-aware text encodings (e.g., style modeling); - contextualized text modeling, where standard text representations are complemented with compact user representations #### Limitations Being exclusively trained on Reddit data, our models probably overfit to linguistic markers and traits which are relevant to characterizing the Reddit user population, but less salient in the general population. Domain-specific fine-tuning may be required before deployment. Furthermore, our self-supervised approach enforces little or no control over biases, which models may actively use as part of their heuristics in contrastive and downstream tasks.
tringuyexn/ppo-LunarLander-v2	tringuyexn	2022-10-20T16:55:51Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-20T16:55:27Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.09 +/- 23.08 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
north/t5_base_scand3M	north	2022-10-20T16:16:52Z	4	0	transformers	[ "transformers", "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "no", "nn", "sv", "da", "is", "en", "dataset:nbailab/NCC", "dataset:mc4", "dataset:wikipedia", "arxiv:2104.09617", "arxiv:1910.10683", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-13T09:02:03Z	--- language: - no - nn - sv - da - is - en datasets: - nbailab/NCC - mc4 - wikipedia widget: - text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsråd på <extra_id_1>. Dette organet er øverste <extra_id_2> i Norge. For at møtet skal være <extra_id_3>, må over halvparten av regjeringens <extra_id_4> være til stede. - text: På <extra_id_0> kan man <extra_id_1> en bok, og man kan også <extra_id_2> seg ned og lese den. license: other --- The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to translation. \| \|Small <br />_60M_\|Base <br />_220M_\|Large <br />_770M_\|XL <br />_3B_\|XXL <br />_11B_\| \|:-----------\|:------------:\|:------------:\|:------------:\|:------------:\|:------------:\| \|North-T5‑NCC\|[🤗](https://huggingface.co/north/t5_small_NCC)\|[🤗](https://huggingface.co/north/t5_base_NCC)\|[🤗](https://huggingface.co/north/t5_large_NCC)\|[🤗](https://huggingface.co/north/t5_xl_NCC)\|[🤗](https://huggingface.co/north/t5_xxl_NCC)\|\| \|North-T5‑NCC‑lm\|[🤗](https://huggingface.co/north/t5_small_NCC_lm)\|[🤗](https://huggingface.co/north/t5_base_NCC_lm)\|[🤗](https://huggingface.co/north/t5_large_NCC_lm)\|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)\|[🤗](https://huggingface.co/north/t5_xxl_NCC_lm)\|\| \|North-T5‑NCC‑modern\|[🤗](https://huggingface.co/north/t5_small_NCC_modern)\|[🤗](https://huggingface.co/north/t5_base_NCC_modern)\|[🤗](https://huggingface.co/north/t5_large_NCC_modern)\|[🤗](https://huggingface.co/north/t5_xl_NCC_modern)\|\| \|North-T5‑NCC‑modern‑lm\|[🤗](https://huggingface.co/north/t5_small_NCC_modern_lm)\|[🤗](https://huggingface.co/north/t5_base_NCC_modern_lm)\|[🤗](https://huggingface.co/north/t5_large_NCC_modern_lm)\|[🤗](https://huggingface.co/north/t5_xl_NCC_modern_lm)\|\| \|North-T5‑NCC‑scand\|[🤗](https://huggingface.co/north/t5_small_NCC_scand)\|[🤗](https://huggingface.co/north/t5_base_NCC_scand)\|[🤗](https://huggingface.co/north/t5_large_NCC_scand)\|[🤗](https://huggingface.co/north/t5_xl_NCC_scand)\|\| \|North-T5‑scand\|[🤗](https://huggingface.co/north/t5_small_scand)\|[🤗](https://huggingface.co/north/t5_base_scand)\|[🤗](https://huggingface.co/north/t5_large_scand)\|\| \|North-byT5‑NCC\|[🤗](https://huggingface.co/north/byt5_small_NCC)\|[🤗](https://huggingface.co/north/byt5_base_NCC)\|[🤗](https://huggingface.co/north/byt5_large_NCC)\|\| \|North-T5‑scand3M\|✔\|[🤗](https://huggingface.co/north/t5_large_scand3M)\|[🤗](https://huggingface.co/north/t5_xl_scand3M)\|\| ## T5X Checkpoint The original T5X checkpoint is also available for this model in the [Google Cloud Bucket](gs://north-t5x/pretrained_models/base/scandinavian3k_t5x_base/). ## Performance A thorough evaluation of the North-T5 models is planned, and I strongly recommend external researchers to make their own evaluation. The main advantage with the T5-models are their flexibility. Traditionally, encoder-only models (like BERT) excels in classification tasks, while seq-2-seq models are easier to train for tasks like translation and Q&A. Despite this, here are the results from using North-T5 on the political classification task explained [here](https://arxiv.org/abs/2104.09617). \|Model: \| F1 \| \|:-----------\|:------------\| \|mT5-base\|73.2 \| \|mBERT-base\|78.4 \| \|NorBERT-base\|78.2 \| \|North-T5-small\|80.5 \| \|nb-bert-base\|81.8 \| \|North-T5-base\|85.3 \| \|North-T5-large\|86.7 \| \|North-T5-xl\|88.7 \| \|North-T5-xxl\|91.8\| These are preliminary results. The [results](https://arxiv.org/abs/2104.09617) from the BERT-models are based on the test-results from the best model after 10 runs with early stopping and a decaying learning rate. The T5-results are the average of five runs on the evaluation set. The small-model was trained for 10.000 steps, while the rest for 5.000 steps. A fixed learning rate was used (no decay), and no early stopping. Neither was the recommended rank classification used. We use a max sequence length of 512. This method simplifies the test setup and gives results that are easy to interpret. However, the results from the T5 model might actually be a bit sub-optimal. ## Sub-versions of North-T5 The following sub-versions are available. More versions will be available shorter. \|Model \| Description \| \|:-----------\|:-------\| \|North‑T5‑NCC \|This is the main version. It is trained an additonal 500.000 steps on from the mT5 checkpoint. The training corpus is based on [the Norwegian Colossal Corpus (NCC)](https://huggingface.co/datasets/NbAiLab/NCC). In addition there are added data from MC4 and English Wikipedia.\| \|North‑T5‑NCC‑lm\|The model is pretrained for an addtional 100k steps on the LM objective discussed in the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf). In a way this turns a masked language model into an autoregressive model. It also prepares the model for some tasks. When for instance doing translation and NLI, it is well documented that there is a clear benefit to do a step of unsupervised LM-training before starting the finetuning.\| \|North‑T5‑NCC‑modern\| The model is pretrained for an additional 200k steps on a blanaced Bokmål and Nynorsk corpus. While this was originally done for doing translation between Bokmål and Nynorsk, it might also give improved results on tasks where you know that the input/output is modern "standard" text. A significant part of the training corpus is newspapers and reports.\| \|North‑T5‑NCC‑modern‑lm\| Trained as above but with an additional 100k "language model"-pretraining.\| \|North‑T5‑NCC‑scand\|The model is pretrained for an additional 200k steps on a Scandinavian corpus (Bokmål, Nynorsk, Danish, Swedish and Icelandic (+ a tiny bit Faeroyish)). The model was trained for increasing the understanding of what effect such training has on various languages.\| \|North‑T5‑scand\|Pretrained for 1,700,000 steps starting with the mT5 checkpoing. The purpose of the mode is studying the difference of different training regimes for Scandinavian language model.\| \|North‑byT5‑base\| This is a vocabulary free version of T5. It is trained exactly like North-T5, but instead of the 250,112 vocabulary, this model operates directly on the raw text. The model architecture might be of particulary interest for tasks involving for instance spelling correction, OCR-cleaning, handwriting recognition etc. However, it will - by design - have amuch shorter maximum sequence length.\| ## Fine-tuned versions As explained below, the model really needs to be fine-tuned for specific tasks. This procedure is relatively simple, and the models are not very sensitive to the hyper-parameters used. Usually a decent result can be obtained by using a fixed learning rate of 1e-3. Smaller versions of the model typically needs to be trained for a longer time. It is easy to train the base-models in a Google Colab. Since some people really want to see what the models are capable of, without going through the training procedure, I provide a couple of test models. These models are by no means optimised, and are just for demonstrating how the North-T5 models can be used. * Nynorsk Translator. Translates any text from Norwegian Bokmål to Norwegian Nynorsk. Please test the [Streamlit-demo](https://huggingface.co/spaces/north/Nynorsk) and the [HuggingFace repo](https://huggingface.co/north/demo-nynorsk-base) * DeUnCaser. The model adds punctation, spaces and capitalisation back into the text. The input needs to be in Norwegian but does not have to be divided into sentences or have proper capitalisation of words. You can even remove the spaces from the text, and make the model reconstruct it. It can be tested with the [Streamlit-demo](https://huggingface.co/spaces/north/DeUnCaser) and directly on the [HuggingFace repo](https://huggingface.co/north/demo-deuncaser-base) ## Training details All models are built using the Flax-based T5X codebase, and all models are initiated with the mT5 pretrained weights. The models are trained using the T5.1.1 training regime, where they are only trained on an unsupervised masking-task. This also means that the models (contrary to the original T5) needs to be finetuned to solve specific tasks. This finetuning is however usually not very compute intensive, and in most cases it can be performed even with free online training resources. All the main model model versions are trained for 500.000 steps after the mT5 checkpoint (1.000.000 steps). They are trained mainly on a 75GB corpus, consisting of NCC, Common Crawl and some additional high quality English text (Wikipedia). The corpus is roughly 80% Norwegian text. Additional languages are added to retain some of the multilingual capabilities, making the model both more robust to new words/concepts and also more suited as a basis for translation tasks. While the huge models almost always will give the best results, they are also both more difficult and more expensive to finetune. I will strongly recommended to start with finetuning a base-models. The base-models can easily be finetuned on a standard graphic card or a free TPU through Google Colab. All models were trained on TPUs. The largest XXL model was trained on a TPU v4-64, the XL model on a TPU v4-32, the Large model on a TPU v4-16 and the rest on TPU v4-8. Since it is possible to reduce the batch size during fine-tuning, it is also possible to finetune on slightly smaller hardware. The rule of thumb is that you can go "one step down" when finetuning. The large models still rewuire access to significant hardware, even for finetuning. ## Formats All models are trained using the Flax-based T5X library. The original checkpoints are available in T5X format and can be used for both finetuning or interference. All models, except the XXL-model, are also converted to Transformers/HuggingFace. In this framework, the models can be loaded for finetuning or inference both in Flax, PyTorch and TensorFlow format. ## Future I will continue to train and release additional models to this set. What models that are added is dependent upon the feedbacki from the users ## Thanks This release would not have been possible without getting support and hardware from the [TPU Research Cloud](https://sites.research.google/trc/about/) at Google Research. Both the TPU Research Cloud Team and the T5X Team has provided extremely useful support for getting this running. Freddy Wetjen at the National Library of Norway has been of tremendous help in generating the original NCC corpus, and has also contributed to generate the collated coprus used for this training. In addition he has been a dicussion partner in the creation of these models. Also thanks to Stefan Schweter for writing the [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py) for converting these models from T5X to HuggingFace and to Javier de la Rosa for writing the dataloader for reading the HuggingFace Datasets in T5X. ## Warranty Use at your own risk. The models have not yet been thougroughly tested, and may contain both errors and biases. ## Contact/About These models were trained by Per E Kummervold. Please contact me on [email protected].
amanneo/mail-generator-mini-v2	amanneo	2022-10-20T14:49:33Z	3	0	transformers	[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-10-20T13:12:41Z	--- tags: - generated_from_keras_callback model-index: - name: amanneo/mail-generator-mini-v2 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. --> # amanneo/mail-generator-mini-v2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5212 - Train Accuracy: 0.0027 - Validation Loss: 5.5781 - Validation Accuracy: 0.0 - Epoch: 99 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Train Accuracy \| Validation Loss \| Validation Accuracy \| Epoch \| \|:----------:\|:--------------:\|:---------------:\|:-------------------:\|:-----:\| \| 2.5928 \| 0.0171 \| 5.5430 \| 0.0048 \| 0 \| \| 2.6003 \| 0.0207 \| 5.5430 \| 0.0048 \| 1 \| \| 2.5954 \| 0.0171 \| 5.5508 \| 0.0048 \| 2 \| \| 2.5775 \| 0.0190 \| 5.5508 \| 0.0024 \| 3 \| \| 2.5758 \| 0.0231 \| 5.5508 \| 0.0024 \| 4 \| \| 2.5742 \| 0.0207 \| 5.5586 \| 0.0048 \| 5 \| \| 2.5547 \| 0.0209 \| 5.5586 \| 0.0048 \| 6 \| \| 2.5566 \| 0.0188 \| 5.5586 \| 0.0048 \| 7 \| \| 2.5391 \| 0.0193 \| 5.5586 \| 0.0048 \| 8 \| \| 2.5378 \| 0.0215 \| 5.5508 \| 0.0048 \| 9 \| \| 2.5238 \| 0.0188 \| 5.5469 \| 0.0048 \| 10 \| \| 2.5150 \| 0.0160 \| 5.5508 \| 0.0048 \| 11 \| \| 2.4967 \| 0.0174 \| 5.5508 \| 0.0071 \| 12 \| \| 2.4691 \| 0.0193 \| 5.5430 \| 0.0071 \| 13 \| \| 2.4626 \| 0.0163 \| 5.5430 \| 0.0071 \| 14 \| \| 2.4417 \| 0.0231 \| 5.5352 \| 0.0048 \| 15 \| \| 2.4323 \| 0.0215 \| 5.5352 \| 0.0048 \| 16 \| \| 2.4193 \| 0.0226 \| 5.5469 \| 0.0048 \| 17 \| \| 2.4170 \| 0.0185 \| 5.5469 \| 0.0048 \| 18 \| \| 2.3743 \| 0.0193 \| 5.5312 \| 0.0048 \| 19 \| \| 2.3730 \| 0.0207 \| 5.5312 \| 0.0048 \| 20 \| \| 2.3535 \| 0.0198 \| 5.5312 \| 0.0048 \| 21 \| \| 2.3372 \| 0.0182 \| 5.5312 \| 0.0071 \| 22 \| \| 2.3324 \| 0.0177 \| 5.5312 \| 0.0048 \| 23 \| \| 2.3011 \| 0.0204 \| 5.5195 \| 0.0048 \| 24 \| \| 2.2650 \| 0.0212 \| 5.5117 \| 0.0048 \| 25 \| \| 2.2568 \| 0.0198 \| 5.5078 \| 0.0048 \| 26 \| \| 2.2331 \| 0.0196 \| 5.5156 \| 0.0048 \| 27 \| \| 2.2021 \| 0.0193 \| 5.5078 \| 0.0048 \| 28 \| \| 2.1807 \| 0.0204 \| 5.5039 \| 0.0048 \| 29 \| \| 2.1691 \| 0.0190 \| 5.5 \| 0.0 \| 30 \| \| 2.1463 \| 0.0174 \| 5.4766 \| 0.0 \| 31 \| \| 2.1097 \| 0.0196 \| 5.4844 \| 0.0 \| 32 \| \| 2.1014 \| 0.0179 \| 5.4844 \| 0.0024 \| 33 \| \| 2.0833 \| 0.0177 \| 5.4844 \| 0.0024 \| 34 \| \| 2.0423 \| 0.0201 \| 5.4844 \| 0.0 \| 35 \| \| 2.0163 \| 0.0198 \| 5.4844 \| 0.0 \| 36 \| \| 1.9909 \| 0.0168 \| 5.4883 \| 0.0 \| 37 \| \| 1.9774 \| 0.0207 \| 5.4805 \| 0.0 \| 38 \| \| 1.9414 \| 0.0207 \| 5.4844 \| 0.0 \| 39 \| \| 1.9206 \| 0.0215 \| 5.4766 \| 0.0 \| 40 \| \| 1.8849 \| 0.0182 \| 5.4805 \| 0.0 \| 41 \| \| 1.8732 \| 0.0193 \| 5.4648 \| 0.0 \| 42 \| \| 1.8460 \| 0.0160 \| 5.4609 \| 0.0 \| 43 \| \| 1.8171 \| 0.0168 \| 5.4648 \| 0.0 \| 44 \| \| 1.7791 \| 0.0201 \| 5.4531 \| 0.0 \| 45 \| \| 1.7583 \| 0.0158 \| 5.4570 \| 0.0 \| 46 \| \| 1.7360 \| 0.0171 \| 5.4570 \| 0.0 \| 47 \| \| 1.7061 \| 0.0120 \| 5.4297 \| 0.0 \| 48 \| \| 1.6802 \| 0.0155 \| 5.4258 \| 0.0 \| 49 \| \| 1.6551 \| 0.0182 \| 5.4141 \| 0.0 \| 50 \| \| 1.6289 \| 0.0130 \| 5.4219 \| 0.0 \| 51 \| \| 1.5981 \| 0.0130 \| 5.3945 \| 0.0 \| 52 \| \| 1.5656 \| 0.0128 \| 5.4297 \| 0.0 \| 53 \| \| 1.5535 \| 0.0168 \| 5.4219 \| 0.0 \| 54 \| \| 1.5184 \| 0.0141 \| 5.4102 \| 0.0 \| 55 \| \| 1.4943 \| 0.0149 \| 5.4023 \| 0.0 \| 56 \| \| 1.4616 \| 0.0122 \| 5.4062 \| 0.0 \| 57 \| \| 1.4344 \| 0.0111 \| 5.4062 \| 0.0 \| 58 \| \| 1.3965 \| 0.0111 \| 5.4141 \| 0.0 \| 59 \| \| 1.3643 \| 0.0122 \| 5.4375 \| 0.0 \| 60 \| \| 1.3309 \| 0.0087 \| 5.4453 \| 0.0 \| 61 \| \| 1.3215 \| 0.0090 \| 5.4648 \| 0.0 \| 62 \| \| 1.3058 \| 0.0084 \| 5.4727 \| 0.0 \| 63 \| \| 1.2700 \| 0.0109 \| 5.4453 \| 0.0 \| 64 \| \| 1.2396 \| 0.0079 \| 5.4609 \| 0.0 \| 65 \| \| 1.2189 \| 0.0092 \| 5.4375 \| 0.0 \| 66 \| \| 1.1855 \| 0.0079 \| 5.4375 \| 0.0 \| 67 \| \| 1.1592 \| 0.0073 \| 5.4375 \| 0.0 \| 68 \| \| 1.1219 \| 0.0071 \| 5.4648 \| 0.0 \| 69 \| \| 1.1071 \| 0.0065 \| 5.4570 \| 0.0 \| 70 \| \| 1.0848 \| 0.0060 \| 5.4375 \| 0.0 \| 71 \| \| 1.0581 \| 0.0076 \| 5.4453 \| 0.0 \| 72 \| \| 1.0316 \| 0.0090 \| 5.4570 \| 0.0 \| 73 \| \| 1.0068 \| 0.0063 \| 5.4219 \| 0.0 \| 74 \| \| 0.9832 \| 0.0060 \| 5.4570 \| 0.0 \| 75 \| \| 0.9534 \| 0.0046 \| 5.4570 \| 0.0 \| 76 \| \| 0.9378 \| 0.0057 \| 5.4648 \| 0.0 \| 77 \| \| 0.9170 \| 0.0033 \| 5.4844 \| 0.0 \| 78 \| \| 0.8941 \| 0.0041 \| 5.4883 \| 0.0 \| 79 \| \| 0.8666 \| 0.0030 \| 5.4922 \| 0.0 \| 80 \| \| 0.8419 \| 0.0054 \| 5.4375 \| 0.0 \| 81 \| \| 0.8200 \| 0.0035 \| 5.4492 \| 0.0 \| 82 \| \| 0.8020 \| 0.0022 \| 5.4648 \| 0.0 \| 83 \| \| 0.7785 \| 0.0057 \| 5.4883 \| 0.0 \| 84 \| \| 0.7607 \| 0.0052 \| 5.4648 \| 0.0 \| 85 \| \| 0.7454 \| 0.0041 \| 5.5078 \| 0.0 \| 86 \| \| 0.7208 \| 0.0024 \| 5.5078 \| 0.0 \| 87 \| \| 0.7040 \| 0.0027 \| 5.5078 \| 0.0 \| 88 \| \| 0.6799 \| 0.0041 \| 5.5156 \| 0.0 \| 89 \| \| 0.6594 \| 0.0030 \| 5.5312 \| 0.0 \| 90 \| \| 0.6397 \| 0.0030 \| 5.5312 \| 0.0 \| 91 \| \| 0.6217 \| 0.0030 \| 5.5195 \| 0.0 \| 92 \| \| 0.6112 \| 0.0033 \| 5.5195 \| 0.0 \| 93 \| \| 0.5937 \| 0.0046 \| 5.5625 \| 0.0 \| 94 \| \| 0.5745 \| 0.0035 \| 5.5625 \| 0.0 \| 95 \| \| 0.5616 \| 0.0027 \| 5.5586 \| 0.0 \| 96 \| \| 0.5468 \| 0.0043 \| 5.5742 \| 0.0 \| 97 \| \| 0.5354 \| 0.0027 \| 5.5781 \| 0.0 \| 98 \| \| 0.5212 \| 0.0027 \| 5.5781 \| 0.0 \| 99 \| ### Framework versions - Transformers 4.23.1 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.1
Mattbrenr/What	Mattbrenr	2022-10-20T14:07:37Z	0	0	null	[ "license:creativeml-openrail-m", "region:us" ]	null	2022-10-20T14:07:37Z	--- license: creativeml-openrail-m ---
auriolar/Reinformce-Pong-PLE-v0	auriolar	2022-10-20T14:07:27Z	0	0	null	[ "Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2022-10-20T14:07:14Z	--- tags: - Pong-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinformce-Pong-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pong-PLE-v0 type: Pong-PLE-v0 metrics: - type: mean_reward value: -16.00 +/- 0.00 name: mean_reward verified: false --- # Reinforce Agent playing Pong-PLE-v0 This is a trained model of a Reinforce agent playing Pong-PLE-v0 . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
jeonsworld/ddpm-butterflies-128	jeonsworld	2022-10-20T13:56:19Z	3	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:huggan/smithsonian_butterflies_subset", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-20T12:40:13Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset 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-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` 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: 16 - eval_batch_size: 16 - 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/jeonsworld/ddpm-butterflies-128/tensorboard?#scalars)
lewtun/quantized-distilbert-banking77	lewtun	2022-10-20T12:47:39Z	13	0	transformers	[ "transformers", "onnx", "text-classification", "optimum", "dataset:banking77", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-08T09:42:56Z	--- tags: - optimum datasets: - banking77 metrics: - accuracy model-index: - name: quantized-distilbert-banking77 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 metrics: - name: Accuracy type: accuracy value: 0.9244 --- # Quantized-distilbert-banking77 This model is a dynamically quantized version of [optimum/distilbert-base-uncased-finetuned-banking77](https://huggingface.co/optimum/distilbert-base-uncased-finetuned-banking77) on the `banking77` dataset. The model was created using the [dynamic-quantization](https://github.com/huggingface/workshops/tree/main/mlops-world) notebook from a workshop presented at MLOps World 2022. It achieves the following results on the evaluation set: Accuracy - Vanilla model: 92.5% - Quantized model: 92.44% > The quantized model achieves 99.93% accuracy of the FP32 model Latency Payload sequence length: 128 Instance type: AWS c6i.xlarge \| latency \| vanilla transformers \| quantized optimum model \| improvement \| \|---------\|----------------------\|-------------------------\|-------------\| \| p95 \| 63.24ms \| 37.06ms \| 1.71x \| \| avg \| 62.87ms \| 37.93ms \| 1.66x \| ## How to use ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import pipeline, AutoTokenizer model = ORTModelForSequenceClassification.from_pretrained("lewtun/quantized-distilbert-banking77") tokenizer = AutoTokenizer.from_pretrained("lewtun/quantized-distilbert-banking77") classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) classifier("What is the exchange rate like on this app?") ```
ChaosW/autohome-deberta-v2-xlarge-base	ChaosW	2022-10-20T12:21:06Z	3	0	transformers	[ "transformers", "pytorch", "deberta-v2", "fill-mask", "bert", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-20T12:19:19Z	--- language: - zh license: apache-2.0 tags: - bert inference: true widget: - text: "生活的真谛是[MASK]。" --- # Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples. ## Task Description Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248) ## Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese') text = '生活的真谛是[MASK]。' fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7) print(fillmask_pipe(text, top_k=10)) ``` ## Finetune We present the dev results on some tasks. \| Model \| OCNLI \| CMNLI \| \| ---------------------------------- \| ----- \| ------ \| \| RoBERTa-base \| 0.743 \| 0.7973 \| \| Erlangshen-Deberta-97M-Chinese \| 0.752 \| 0.807 \| ## Citation If you find the resource is useful, please cite the following website in your paper. ``` @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2022}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```
knkarthick/Action_Items	knkarthick	2022-10-20T12:10:12Z	75	7	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "seq2seq", "en", "dataset:Custom", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-20T10:45:18Z	--- language: en tags: - distilbert - seq2seq - text-classification license: apache-2.0 datasets: - Custom metrics: - Accuracy - Precision - Recall widget: - text: \|- Let's start the project as soon as possible as we are running out of deadline. model-index: - name: Action_Items results: - task: name: Action Item Classification type: text-classification dataset: name: Custom type: custom metrics: - name: Validation Accuracy type: accuracy value: - name: Validation Precision type: precision value: - name: Validation Recall type: recall value: - name: Test Accuracy type: accuracy value: - name: Test Precision type: precision value: - name: Test Recall type: recall value: --- Model obtained by Fine Tuning 'distilbert' using Custom Dataset! LABEL_0 - Not an Action Item LABEL_1 - Action Item ## Usage # Example 1 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' Customer portion will have the dependency of , you know , fifty five probably has to be on XGEVA before we can start that track , but we can at least start the enablement track for sales and CSM who are as important as customers because they're the top of our funnel , especially sales. ''' summarizer(text) ``` # Example 2 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' India, officially the Republic of India, is a country in South Asia. ''' summarizer(text) ``` # Example 3 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' We have been running the business successfully for over a decade now. ''' summarizer(text) ```
bthomas/article2keyword2.1b_barthez-orangesum-title_finetuned16_for_mlm	bthomas	2022-10-20T12:04:52Z	7	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "mlm", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-20T09:46:19Z	--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.1b_barthez-orangesum-title_finetuned16_for_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. --> # article2keyword2.1b_barthez-orangesum-title_finetuned16_for_mlm This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0525 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 0.2976 \| 1.0 \| 1353 \| 0.0543 \| \| 0.0566 \| 2.0 \| 2706 \| 0.0509 \| \| 0.0487 \| 3.0 \| 4059 \| 0.0458 \| \| 0.0433 \| 4.0 \| 5412 \| 0.0456 \| \| 0.04 \| 5.0 \| 6765 \| 0.0460 \| \| 0.0373 \| 6.0 \| 8118 \| 0.0454 \| \| 0.0355 \| 7.0 \| 9471 \| 0.0465 \| \| 0.0328 \| 8.0 \| 10824 \| 0.0474 \| \| 0.0317 \| 9.0 \| 12177 \| 0.0470 \| \| 0.03 \| 10.0 \| 13530 \| 0.0488 \| \| 0.0285 \| 11.0 \| 14883 \| 0.0489 \| \| 0.0272 \| 12.0 \| 16236 \| 0.0500 \| \| 0.0262 \| 13.0 \| 17589 \| 0.0510 \| \| 0.0258 \| 14.0 \| 18942 \| 0.0511 \| \| 0.0245 \| 15.0 \| 20295 \| 0.0522 \| \| 0.0239 \| 16.0 \| 21648 \| 0.0525 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
danielsaggau/lbert_scotus_classsification	danielsaggau	2022-10-20T11:09:25Z	4	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:lex_glue", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-20T11:02:37Z	--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - lex_glue model-index: - name: lbert_scotus_classsification 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. --> # lbert_scotus_classsification This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue 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: 3e-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: 5 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
readerbench/RoSummary-medium	readerbench	2022-10-20T10:00:04Z	5	0	transformers	[ "transformers", "pytorch", "tf", "gpt2", "text-generation", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-19T06:32:45Z	Model card for RoSummary-medium --- language: - ro --- # RoSummary This is a version of the RoGPT2 model trained on the [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews) dataset for the summarization task. There are 3 trained versions, they are available on the HuggingFace Hub: * [base](https://huggingface.co/readerbench/RoSummary-base) * [medium](https://huggingface.co/readerbench/RoSummary-medium) * [large](https://huggingface.co/readerbench/RoSummary-large) ## Evaluation on [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews) \| Model \| Decode Method \| \| BERTScore \| \| \| ROUGE \| \| \|:------:\|:--------------:\|:---------:\|:---------:\|:--------:\|:--------:\|:--------:\|:--------:\| \| \| \| Precision \| Recall \| F1-Score \| ROUGE-1 \| ROUGE-2 \| ROUGE-L \| \| \| Greedy \| 0.7335 \| 0.7399 \| 0.7358 \| 0.3360 \| 0.1862 \| 0.3333 \| \| Base \| Beam Search \| 0.7354 \| 0.7468 \| 0.7404 \| 0.3480 \| 0.1991 \| 0.3416 \| \| \| Top-p Sampling \| 0.7296 \| 0.7299 \| 0.7292 \| 0.3058 \| 0.1452 \| 0.2951 \| \| \| Greedy \| 0.7378 \| 0.7401 \| 0.7380 \| 0.3422 \| 0.1922 \| 0.3394 \| \| Medium \| Beam Search \| 0.7390 \| 0.7493\|0.7434\|0.3546\|0.2061\|0.3467\| \| \| Top-p Sampling \| 0.7315 \| 0.7285 \| 0.7294 \| 0.3042 \| 0.1400 \| 0.2921 \| \| \| Greedy \| 0.7376 \| 0.7424 \| 0.7391 \| 0.3414 \| 0.1895 \| 0.3355 \| \| Large \| Beam Search \| 0.7394\| 0.7470 \| 0.7424 \| 0.3492 \| 0.1995 \| 0.3384 \| \| \| Top-p Sampling \| 0.7311 \| 0.7301 \| 0.7299 \| 0.3051 \| 0.1418 \| 0.2931 \| ## Acknowledgments --- Research supported with [Cloud TPUs](https://cloud.google.com/tpu/) from Google's [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc)
bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm	bthomas	2022-10-20T09:36:12Z	5	0	transformers	[ "transformers", "pytorch", "bert", "fill-mask", "mlm", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-10-20T08:33:40Z	--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_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. --> # article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0673 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 2.3777 \| 1.0 \| 1353 \| 0.3168 \| \| 0.2358 \| 2.0 \| 2706 \| 0.1564 \| \| 0.1372 \| 3.0 \| 4059 \| 0.1149 \| \| 0.1046 \| 4.0 \| 5412 \| 0.0956 \| \| 0.086 \| 5.0 \| 6765 \| 0.0853 \| \| 0.0741 \| 6.0 \| 8118 \| 0.0786 \| \| 0.0653 \| 7.0 \| 9471 \| 0.0750 \| \| 0.0594 \| 8.0 \| 10824 \| 0.0726 \| \| 0.0542 \| 9.0 \| 12177 \| 0.0699 \| \| 0.0504 \| 10.0 \| 13530 \| 0.0692 \| \| 0.047 \| 11.0 \| 14883 \| 0.0684 \| \| 0.0444 \| 12.0 \| 16236 \| 0.0675 \| \| 0.0423 \| 13.0 \| 17589 \| 0.0674 \| \| 0.0404 \| 14.0 \| 18942 \| 0.0673 \| \| 0.0392 \| 15.0 \| 20295 \| 0.0672 \| \| 0.0379 \| 16.0 \| 21648 \| 0.0673 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
mprzibilla/super_large_finetune_CM01	mprzibilla	2022-10-20T09:04:35Z	4	0	transformers	[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-19T23:12:30Z	--- tags: - generated_from_trainer model-index: - name: super_large_finetune_CM01 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. --> # super_large_finetune_CM01 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.2285 - Wer: 0.7714 ## 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: 0.0001 - train_batch_size: 15 - 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: 857 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\| \| 1.0031 \| 5.0 \| 1715 \| 1.9766 \| 0.7857 \| \| 0.2107 \| 10.0 \| 3430 \| 3.8748 \| 0.8238 \| \| 0.1393 \| 15.0 \| 5145 \| 4.7403 \| 0.7952 \| \| 0.0931 \| 20.0 \| 6860 \| 3.5077 \| 0.6667 \| \| 0.0649 \| 25.0 \| 8575 \| 7.7419 \| 0.9333 \| \| 0.0592 \| 30.0 \| 10290 \| 5.6440 \| 0.7762 \| \| 0.0396 \| 35.0 \| 12005 \| 6.9629 \| 0.6810 \| \| 0.03 \| 40.0 \| 13720 \| 7.8282 \| 0.7524 \| \| 0.0191 \| 45.0 \| 15435 \| 6.4626 \| 0.7429 \| \| 0.0121 \| 50.0 \| 17150 \| 7.2285 \| 0.7714 \| ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1
jayanta/vit-base-patch16-224-FV-20epochs-finetuned-memes	jayanta	2022-10-20T08:21:22Z	43	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "generated_from_trainer", "dataset:imagefolder", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-20T07:39:54Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224-FV-20epochs-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8632148377125193 - name: Precision type: precision value: 0.8617373130509159 - name: Recall type: recall value: 0.8632148377125193 - name: F1 type: f1 value: 0.8621436376894498 --- <!-- 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-FV-20epochs-finetuned-memes This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6532 - Accuracy: 0.8632 - Precision: 0.8617 - Recall: 0.8632 - F1: 0.8621 ## 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: 0.00012 - 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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 1.1709 \| 0.99 \| 20 \| 0.9393 \| 0.6971 \| 0.6896 \| 0.6971 \| 0.6890 \| \| 0.5295 \| 1.99 \| 40 \| 0.5024 \| 0.8091 \| 0.8210 \| 0.8091 \| 0.8133 \| \| 0.2909 \| 2.99 \| 60 \| 0.4070 \| 0.8539 \| 0.8529 \| 0.8539 \| 0.8529 \| \| 0.1435 \| 3.99 \| 80 \| 0.4136 \| 0.8539 \| 0.8522 \| 0.8539 \| 0.8522 \| \| 0.0928 \| 4.99 \| 100 \| 0.4495 \| 0.8478 \| 0.8548 \| 0.8478 \| 0.8507 \| \| 0.0643 \| 5.99 \| 120 \| 0.4897 \| 0.8594 \| 0.8572 \| 0.8594 \| 0.8573 \| \| 0.061 \| 6.99 \| 140 \| 0.5040 \| 0.8423 \| 0.8490 \| 0.8423 \| 0.8453 \| \| 0.0519 \| 7.99 \| 160 \| 0.5266 \| 0.8524 \| 0.8502 \| 0.8524 \| 0.8510 \| \| 0.0546 \| 8.99 \| 180 \| 0.5200 \| 0.8586 \| 0.8632 \| 0.8586 \| 0.8605 \| \| 0.0478 \| 9.99 \| 200 \| 0.5654 \| 0.8555 \| 0.8548 \| 0.8555 \| 0.8548 \| \| 0.0509 \| 10.99 \| 220 \| 0.5774 \| 0.8609 \| 0.8626 \| 0.8609 \| 0.8616 \| \| 0.0467 \| 11.99 \| 240 \| 0.5847 \| 0.8594 \| 0.8602 \| 0.8594 \| 0.8594 \| \| 0.0468 \| 12.99 \| 260 \| 0.5909 \| 0.8601 \| 0.8597 \| 0.8601 \| 0.8596 \| \| 0.0469 \| 13.99 \| 280 \| 0.5970 \| 0.8563 \| 0.8560 \| 0.8563 \| 0.8561 \| \| 0.0438 \| 14.99 \| 300 \| 0.6234 \| 0.8594 \| 0.8583 \| 0.8594 \| 0.8586 \| \| 0.0441 \| 15.99 \| 320 \| 0.6190 \| 0.8563 \| 0.8582 \| 0.8563 \| 0.8570 \| \| 0.0431 \| 16.99 \| 340 \| 0.6419 \| 0.8570 \| 0.8584 \| 0.8570 \| 0.8574 \| \| 0.0454 \| 17.99 \| 360 \| 0.6528 \| 0.8563 \| 0.8556 \| 0.8563 \| 0.8558 \| \| 0.0417 \| 18.99 \| 380 \| 0.6688 \| 0.8578 \| 0.8575 \| 0.8578 \| 0.8574 \| \| 0.0432 \| 19.99 \| 400 \| 0.6532 \| 0.8632 \| 0.8617 \| 0.8632 \| 0.8621 \| ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1
thisisHJLee/wav2vec2-large-xls-r-300m-korean-w1	thisisHJLee	2022-10-20T08:00:16Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-10-20T05:38:51Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-w1 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-korean-w1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1406 - Cer: 0.0393 ## 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 24.537 \| 0.56 \| 800 \| 3.0461 \| 0.9274 \| \| 1.9309 \| 1.13 \| 1600 \| 0.7723 \| 0.2168 \| \| 0.7595 \| 1.69 \| 2400 \| 0.3197 \| 0.0916 \| \| 0.4338 \| 2.26 \| 3200 \| 0.2051 \| 0.0587 \| \| 0.3067 \| 2.82 \| 4000 \| 0.1406 \| 0.0393 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
nayan06/binary-classifier-conversion-intent-1.1-l12	nayan06	2022-10-20T07:05:09Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-18T11:34:15Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Setfit Classification Model ON Conversion Dataset With L12 sbert Model as Base This is a Setfit Model with the L6 model as a Base for classification. <!--- Describe your model here --> ## Usage (Setfit) ``` pip install setfit ``` Then you can use the model like this: ```python from setfit import SetFitModel model = SetFitModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.1-l12") prediction = model(['i want to buy thing']) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 2163 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2163, "warmup_steps": 217, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Dataset Used https://huggingface.co/datasets/nayan06/conversion1.0 ## Citing & Authors <!--- Describe where people can find more information -->
debbiesoon/t5-small-T5_summarise	debbiesoon	2022-10-20T06:09:39Z	6	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	2022-10-20T05:53:01Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-T5_summarise 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-T5_summarise This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0384 - Rouge1: 15.9638 - Rouge2: 9.0883 - Rougel: 13.2968 - Rougelsum: 14.5007 - Gen Len: 19.0 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 4.2781 \| 1.0 \| 2 \| 5.0384 \| 15.9638 \| 9.0883 \| 13.2968 \| 14.5007 \| 19.0 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
nguyenkhoa2407/bert-base-cased-NER-favsbot	nguyenkhoa2407	2022-10-20T05:11:31Z	5	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "dataset:favsbot", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-08-23T15:57:05Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - favsbot metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-NER-favsbot results: - task: name: Token Classification type: token-classification dataset: name: favsbot type: favsbot config: default split: train args: default metrics: - name: Precision type: precision value: 0.8461538461538461 - name: Recall type: recall value: 0.88 - name: F1 type: f1 value: 0.8627450980392156 - name: Accuracy type: accuracy value: 0.9444444444444444 --- <!-- 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-cased-NER-favsbot This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1680 - Precision: 0.8462 - Recall: 0.88 - F1: 0.8627 - Accuracy: 0.9444 ## 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: 1.5e-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: 20 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 7 \| 1.8761 \| 0.0 \| 0.0 \| 0.0 \| 0.5833 \| \| No log \| 2.0 \| 14 \| 1.3530 \| 0.0 \| 0.0 \| 0.0 \| 0.5972 \| \| No log \| 3.0 \| 21 \| 1.0400 \| 1.0 \| 0.12 \| 0.2143 \| 0.6389 \| \| No log \| 4.0 \| 28 \| 0.7987 \| 0.7895 \| 0.6 \| 0.6818 \| 0.8194 \| \| No log \| 5.0 \| 35 \| 0.6055 \| 0.85 \| 0.68 \| 0.7556 \| 0.875 \| \| No log \| 6.0 \| 42 \| 0.4749 \| 0.8696 \| 0.8 \| 0.8333 \| 0.9167 \| \| No log \| 7.0 \| 49 \| 0.3838 \| 0.84 \| 0.84 \| 0.8400 \| 0.9444 \| \| No log \| 8.0 \| 56 \| 0.3084 \| 0.88 \| 0.88 \| 0.88 \| 0.9583 \| \| No log \| 9.0 \| 63 \| 0.2643 \| 0.88 \| 0.88 \| 0.88 \| 0.9583 \| \| No log \| 10.0 \| 70 \| 0.2360 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 11.0 \| 77 \| 0.2168 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 12.0 \| 84 \| 0.2031 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 13.0 \| 91 \| 0.1937 \| 0.88 \| 0.88 \| 0.88 \| 0.9583 \| \| No log \| 14.0 \| 98 \| 0.1853 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 15.0 \| 105 \| 0.1791 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 16.0 \| 112 \| 0.1757 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 17.0 \| 119 \| 0.1718 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| \| No log \| 18.0 \| 126 \| 0.1698 \| 0.8148 \| 0.88 \| 0.8462 \| 0.9444 \| \| No log \| 19.0 \| 133 \| 0.1686 \| 0.8148 \| 0.88 \| 0.8462 \| 0.9444 \| \| No log \| 20.0 \| 140 \| 0.1680 \| 0.8462 \| 0.88 \| 0.8627 \| 0.9444 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
debbiesoon/summarise_v6	debbiesoon	2022-10-20T04:32:42Z	10	0	transformers	[ "transformers", "pytorch", "tensorboard", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-16T20:04:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: summarise_v6 results: [] --- # summarise_v6 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0497 - Rouge2 Precision: 0.3109 - Rouge2 Recall: 0.406 - Rouge2 Fmeasure: 0.3375 ## Model description More information needed ## Intended uses & limitations max_input_length = 3072 max_output_length = 1000 led.config.max_length = 1000 led.config.min_length = 100 ## 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: 2 - eval_batch_size: 2 - seed: 42 - 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 \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.7163 \| 0.22 \| 10 \| 1.2307 \| 0.1428 \| 0.5118 \| 0.2089 \| \| 1.632 \| 0.44 \| 20 \| 1.1337 \| 0.36 \| 0.3393 \| 0.3181 \| \| 1.0916 \| 0.67 \| 30 \| 1.0738 \| 0.2693 \| 0.3487 \| 0.2731 \| \| 1.573 \| 0.89 \| 40 \| 1.0497 \| 0.3109 \| 0.406 \| 0.3375 \| ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1
debbiesoon/summarise	debbiesoon	2022-10-20T04:12:19Z	12	0	transformers	[ "transformers", "pytorch", "tensorboard", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-16T03:34:42Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: summarise 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. --> # summarise This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0497 - Rouge2 Precision: 0.3109 - Rouge2 Recall: 0.406 - Rouge2 Fmeasure: 0.3375 ## Model description More information needed ## Intended uses & limitations max_input_length = 3072 max_output_length = 1000 led.config.max_length = 1000 led.config.min_length = 100 ## 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: 2 - eval_batch_size: 2 - seed: 42 - 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 \| Rouge2 Precision \| Rouge2 Recall \| Rouge2 Fmeasure \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:----------------:\|:-------------:\|:---------------:\| \| 1.7163 \| 0.22 \| 10 \| 1.2307 \| 0.1428 \| 0.5118 \| 0.2089 \| \| 1.632 \| 0.44 \| 20 \| 1.1337 \| 0.36 \| 0.3393 \| 0.3181 \| \| 1.0916 \| 0.67 \| 30 \| 1.0738 \| 0.2693 \| 0.3487 \| 0.2731 \| \| 1.573 \| 0.89 \| 40 \| 1.0497 \| 0.3109 \| 0.406 \| 0.3375 \| ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1
debbiesoon/longformer_summarise_large	debbiesoon	2022-10-20T03:55:16Z	10	0	transformers	[ "transformers", "pytorch", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-20T03:29:04Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: longformer_summarise_large 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. --> # longformer_summarise_large This model is a fine-tuned version of [patrickvonplaten/led-large-16384-pubmed](https://huggingface.co/patrickvonplaten/led-large-16384-pubmed) 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: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1
tomjam/bert-finetuned-ner	tomjam	2022-10-20T01:48:18Z	16	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "dataset:conll2003", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-20T00:48:15Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9352911896465903 - name: Recall type: recall value: 0.9486704813194211 - name: F1 type: f1 value: 0.9419333277633887 - name: Accuracy type: accuracy value: 0.9864455171601814 --- <!-- 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. It achieves the following results on the evaluation set: - Loss: 0.0610 - Precision: 0.9353 - Recall: 0.9487 - F1: 0.9419 - Accuracy: 0.9864 ## 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 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0907 \| 1.0 \| 1756 \| 0.0732 \| 0.9188 \| 0.9337 \| 0.9262 \| 0.9818 \| \| 0.035 \| 2.0 \| 3512 \| 0.0607 \| 0.9280 \| 0.9480 \| 0.9379 \| 0.9859 \| \| 0.0169 \| 3.0 \| 5268 \| 0.0610 \| 0.9353 \| 0.9487 \| 0.9419 \| 0.9864 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
vwxyzjn/BreakoutNoFrameskip-v4-dqn_atari-seed1	vwxyzjn	2022-10-20T00:34:56Z	0	0	null	[ "tensorboard", "BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-10-20T00:34:52Z	--- tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 2.70 +/- 4.12 name: mean_reward verified: false --- # (CleanRL) DQN Agent Playing BreakoutNoFrameskip-v4 This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'BreakoutNoFrameskip-v4', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': '', 'learning_rate': 0.0001, 'learning_starts': 80000, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'torch_deterministic': True, 'total_timesteps': 10000, 'track': False, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```
MagoMerlot/PSFs_generated	MagoMerlot	2022-10-19T23:46:03Z	3	0	diffusers	[ "diffusers", "tensorboard", "en", "license:mit", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-19T05:43:58Z	--- language: en tags: - diffusers license: mit ---
spencer-gable-cook/COVID-19_Misinformation_Detector	spencer-gable-cook	2022-10-19T22:53:16Z	20	1	transformers	[ "transformers", "pytorch", "onnx", "bert", "text-classification", "arxiv:2006.00885", "doi:10.57967/hf/3925", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-18T20:23:56Z	--- license: mit --- Welcome to the COVID-19 Misinformation Detector! There is a lot of misinformation related to the COVID-19 vaccine being posted online from unreliable sources. The COVID-19 Misinformation Detector allows you to check if the information you are reading online (e.g. from Twitter or Facebook) contains misinformation or not! Enter the text from the online post in the "Hosted inference API" text area to the right to check if it is misinformation. "LABEL_0" means that no misinformation was detected in the post, while "LABEL_1" means that the post is misinformation. The COVID-19 Misinformation Detector is a modified version of the "bert-base-uncased" transformer model, found [here](https://huggingface.co/bert-base-uncased). It is fine-tuned on two datasets containing tweets relating to the COVID-19 pandemic; each tweet is labelled as containing misinformation (1) or not (0), as verified by healthcare experts. The datasets used are: 1. [ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection](https://www.sciencedirect.com/science/article/pii/S0033350621004534) 2. [CoAID (Covid-19 HeAlthcare mIsinformation Dataset)](https://arxiv.org/abs/2006.00885) For a more detailed explanation, check out the technical report [here](https://drive.google.com/file/d/1QW9D6TN4KXX6poa6Q5L6FVgqaDQ4DxY9/view?usp=sharing), and check out my literature review on transformers [here](https://drive.google.com/file/d/1d5tK3sUwYM1WBheOuNG9A7ZYri2zxdyw/view?usp=sharing)!
AokiDaiki/distilbert-base-uncased-finetuned-emotion	AokiDaiki	2022-10-19T22:45:33Z	3	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	2022-10-18T18:31:02Z	--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9270524571534725 --- <!-- 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: 0.2174 - Accuracy: 0.927 - F1: 0.9271 ## 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: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8148 \| 1.0 \| 250 \| 0.3148 \| 0.9 \| 0.8967 \| \| 0.2487 \| 2.0 \| 500 \| 0.2174 \| 0.927 \| 0.9271 \| ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3
CavenLen/ddpm-Kaga-128	CavenLen	2022-10-19T22:03:31Z	19	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:CavenLen/Kaga", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-10-17T12:48:44Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: CavenLen/Kaga 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-Kaga-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `CavenLen/Kaga` 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: 16 - eval_batch_size: 16 - 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/CavenLen/ddpm-Kaga-128/tensorboard?#scalars)
thucdangvan020999/marian-finetuned-kde4-en-to-fr	thucdangvan020999	2022-10-19T21:12:47Z	5	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-10-19T19:27:37Z	--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.83113187001415 --- <!-- 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. --> # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8560 - Bleu: 52.8311 ## 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: 32 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
mathislucka/tat-model	mathislucka	2022-10-19T20:44:53Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-19T20:44:45Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # mathislucka/tat-model This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. <!--- Describe your model here --> ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('mathislucka/tat-model') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results <!--- Describe how your model was evaluated --> For an automated evaluation of this model, see the Sentence Embeddings Benchmark: [https://seb.sbert.net](https://seb.sbert.net?model_name=mathislucka/tat-model) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 39 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss: `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 3, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
mariolinml/deberta-v3-base_MNLI_10_19_v0	mariolinml	2022-10-19T20:07:22Z	14	0	transformers	[ "transformers", "pytorch", "tensorboard", "deberta-v2", "text-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-19T15:57:15Z	--- license: mit tags: - generated_from_trainer model-index: - name: deberta-v3-base_MNLI_10_19_v0 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. --> # deberta-v3-base_MNLI_10_19_v0 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
rajesh426/distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY	rajesh426	2022-10-19T19:38:11Z	3	0	transformers	[ "transformers", "pytorch", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-19T19:31:05Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY 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. --> # distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0863 - Accuracy: 0.7368 - F1: 0.7114 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 1.0362 \| 1.0 \| 19 \| 0.9281 \| 0.5789 \| 0.4964 \| \| 0.9725 \| 2.0 \| 38 \| 0.8906 \| 0.6316 \| 0.5707 \| \| 0.8712 \| 3.0 \| 57 \| 0.8080 \| 0.6316 \| 0.5889 \| \| 0.6402 \| 4.0 \| 76 \| 0.6386 \| 0.7895 \| 0.7474 \| \| 0.4453 \| 5.0 \| 95 \| 0.5401 \| 0.7895 \| 0.7485 \| \| 0.2658 \| 6.0 \| 114 \| 0.4999 \| 0.8421 \| 0.7990 \| \| 0.1695 \| 7.0 \| 133 \| 0.6248 \| 0.7895 \| 0.7427 \| \| 0.0822 \| 8.0 \| 152 \| 0.7391 \| 0.7368 \| 0.7114 \| \| 0.0303 \| 9.0 \| 171 \| 0.6665 \| 0.7895 \| 0.7485 \| \| 0.016 \| 10.0 \| 190 \| 0.8217 \| 0.7368 \| 0.7114 \| \| 0.0103 \| 11.0 \| 209 \| 0.8090 \| 0.7368 \| 0.7114 \| \| 0.0083 \| 12.0 \| 228 \| 0.8646 \| 0.7368 \| 0.7114 \| \| 0.0068 \| 13.0 \| 247 \| 0.9091 \| 0.7368 \| 0.7114 \| \| 0.0059 \| 14.0 \| 266 \| 0.8731 \| 0.7368 \| 0.7114 \| \| 0.0049 \| 15.0 \| 285 \| 0.9512 \| 0.7368 \| 0.7114 \| \| 0.0048 \| 16.0 \| 304 \| 0.9376 \| 0.7368 \| 0.7114 \| \| 0.004 \| 17.0 \| 323 \| 0.9507 \| 0.7368 \| 0.7114 \| \| 0.0037 \| 18.0 \| 342 \| 0.9868 \| 0.7368 \| 0.7114 \| \| 0.0033 \| 19.0 \| 361 \| 0.9862 \| 0.7368 \| 0.7114 \| \| 0.0029 \| 20.0 \| 380 \| 0.9733 \| 0.7368 \| 0.7114 \| \| 0.0029 \| 21.0 \| 399 \| 0.9747 \| 0.7368 \| 0.7114 \| \| 0.0027 \| 22.0 \| 418 \| 0.9998 \| 0.7368 \| 0.7114 \| \| 0.0024 \| 23.0 \| 437 \| 0.9984 \| 0.7368 \| 0.7114 \| \| 0.0024 \| 24.0 \| 456 \| 1.0270 \| 0.7368 \| 0.7114 \| \| 0.0024 \| 25.0 \| 475 \| 1.0083 \| 0.7368 \| 0.7114 \| \| 0.0022 \| 26.0 \| 494 \| 1.0167 \| 0.7368 \| 0.7114 \| \| 0.0021 \| 27.0 \| 513 \| 1.0273 \| 0.7368 \| 0.7114 \| \| 0.002 \| 28.0 \| 532 \| 1.0340 \| 0.7368 \| 0.7114 \| \| 0.0021 \| 29.0 \| 551 \| 1.0282 \| 0.7368 \| 0.7114 \| \| 0.002 \| 30.0 \| 570 \| 1.0372 \| 0.7368 \| 0.7114 \| \| 0.0019 \| 31.0 \| 589 \| 1.0593 \| 0.7368 \| 0.7114 \| \| 0.0017 \| 32.0 \| 608 \| 1.0841 \| 0.7368 \| 0.7114 \| \| 0.0018 \| 33.0 \| 627 \| 1.0920 \| 0.7368 \| 0.7114 \| \| 0.0019 \| 34.0 \| 646 \| 1.0943 \| 0.7368 \| 0.7114 \| \| 0.0018 \| 35.0 \| 665 \| 1.0883 \| 0.7368 \| 0.7114 \| \| 0.0017 \| 36.0 \| 684 \| 1.0864 \| 0.7368 \| 0.7114 \| \| 0.0016 \| 37.0 \| 703 \| 1.0890 \| 0.7368 \| 0.7114 \| \| 0.0017 \| 38.0 \| 722 \| 1.0894 \| 0.7368 \| 0.7114 \| \| 0.0015 \| 39.0 \| 741 \| 1.0867 \| 0.7368 \| 0.7114 \| \| 0.0016 \| 40.0 \| 760 \| 1.0863 \| 0.7368 \| 0.7114 \| ### Framework versions - Transformers 4.22.2 - Pytorch 1.10.2 - Datasets 2.5.2 - Tokenizers 0.12.1
api19750904/situaciones-turismo	api19750904	2022-10-19T17:59:42Z	40	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-19T17:59:26Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: situaciones-turismo results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9101123809814453 --- # situaciones-turismo Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### people beach ![people beach](images/people_beach.jpg) #### people party ![people party](images/people_party.jpg) #### people restaurant ![people restaurant](images/people_restaurant.jpg) #### people walking ![people walking](images/people_walking.jpg)
api19750904/comida-vgm	api19750904	2022-10-19T16:54:30Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	image-classification	2022-10-19T16:54:16Z	--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: comida-vgm results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9550561904907227 --- # comida-vgm Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for anything by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### burguer ![burguer](images/burguer.jpg) #### macarroni ![macarroni](images/macarroni.jpg) #### pizza ![pizza](images/pizza.jpg) #### spaguetti ![spaguetti](images/spaguetti.jpg)
huggingtweets/konradha_	huggingtweets	2022-10-19T16:11:00Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-19T16:09:29Z	--- language: en thumbnail: http://www.huggingtweets.com/konradha_/1666195856134/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/1540685336422088704/JDxiybNe_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Konrad</div> <div style="text-align: center; font-size: 14px;">@konradha_</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 Konrad. \| Data \| Konrad \| \| --- \| --- \| \| Tweets downloaded \| 256 \| \| Retweets \| 38 \| \| Short tweets \| 75 \| \| Tweets kept \| 143 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ox7i4yk/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 @konradha_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10k5hc9s) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10k5hc9s/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/konradha_') 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)
gabski/sbert-relative-claim-quality	gabski	2022-10-19T16:10:59Z	1	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-10-19T15:59:40Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Model This [sentence-transformers](https://www.SBERT.net) model model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth # Claim Quality Classification We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. We train this model by fine-tuning SBERT based on bert-base-cased using a siamese network structure with softmax loss. Outputs can also be used to rank multiple versions of the same claim, for example, using [SVMRank](https://github.com/ds4dm/PySVMRank) or BTL (Bradley-Terry-Luce model). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('gabski/sbert-relative-claim-quality') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('gabski/sbert-relative-claim-quality') model = AutoModel.from_pretrained('gabski/sbert-relative-claim-quality') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors ```bibtex @inproceedings{skitalinskaya-etal-2021-learning, title = "Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale", author = "Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-main.147", doi = "10.18653/v1/2021.eacl-main.147", pages = "1718--1729", } ```
gabski/bert-relative-claim-quality	gabski	2022-10-19T16:09:19Z	18	0	transformers	[ "transformers", "pytorch", "bert", "text-classification", "en", "dataset:ClaimRev", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-10-19T14:04:22Z	--- language: en license: cc-by-nc-sa-4.0 datasets: - ClaimRev --- # Model This model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth # Claim Quality Classification We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer = AutoTokenizer.from_pretrained("gabski/bert-relative-claim-quality") model = AutoModelForSequenceClassification.from_pretrained("gabski/bert-relative-claim-quality") claim_1 = 'Smoking marijuana is less harmfull then smoking cigarettes.' claim_2 = 'Smoking marijuana is less harmful than smoking cigarettes.' model_input = tokenizer(claim_1,claim_2, return_tensors='pt') model_outputs = model(**model_input) outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1) print(outputs) ```
rakamsata/anim	rakamsata	2022-10-19T15:46:31Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-10-19T15:46:31Z	--- license: bigscience-openrail-m ---
smz2122/image	smz2122	2022-10-19T15:37:37Z	0	0	null	[ "region:us" ]	null	2022-10-19T15:37:20Z	git clone https://huggingface.co/templates/text-to-image cd text-to-image git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME git push --force
enryu43/anifusion_unet	enryu43	2022-10-19T15:01:54Z	15	6	diffusers	[ "diffusers", "diffusers:LDMTextToImagePipeline", "region:us" ]	null	2022-10-11T21:01:02Z	This model is converted with https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py. However, the tokenizer in the diffuser model is wrong, for proper usage, see description at https://medium.com/@enryu9000/anifusion-diffusion-models-for-anime-pictures-138cf1af2cbe, and instructions/examples at https://github.com/enryu43/anifusion-stable-diffusion. Also, the original checkpoint in the Latent Diffusion format is available. Installation instructions for webui: https://gist.github.com/enryu43/858999bf69dc92b97fdad6137c3c45e6
bthomas/article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm	bthomas	2022-10-19T14:48:17Z	6	0	transformers	[ "transformers", "pytorch", "mbart", "text2text-generation", "mlm", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-19T14:32:22Z	--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.2_barthez-orangesum-title_finetuned_for_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. --> # article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0452 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.3187 \| 1.0 \| 1235 \| 0.0545 \| \| 0.0544 \| 2.0 \| 2470 \| 0.0491 \| \| 0.0461 \| 3.0 \| 3705 \| 0.0463 \| \| 0.042 \| 4.0 \| 4940 \| 0.0452 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1
huggingtweets/moonideograph	huggingtweets	2022-10-19T14:31:00Z	3	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "huggingtweets", "en", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-10-19T14:28:14Z	--- language: en thumbnail: http://www.huggingtweets.com/moonideograph/1666189855449/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/1581258561400848384/ktYtGqLD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">🌑 Loona the Ninth</div> <div style="text-align: center; font-size: 14px;">@moonideograph</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 🌑 Loona the Ninth. \| Data \| 🌑 Loona the Ninth \| \| --- \| --- \| \| Tweets downloaded \| 409 \| \| Retweets \| 104 \| \| Short tweets \| 22 \| \| Tweets kept \| 283 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8mujtj4v/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 @moonideograph's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21pia0le) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21pia0le/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/moonideograph') 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)
facebook/xm_transformer_unity_hk-en	facebook	2022-10-19T14:28:29Z	39	7	fairseq	[ "fairseq", "audio", "audio-to-audio", "speech-to-speech-translation", "license:cc-by-nc-4.0", "region:us" ]	audio-to-audio	2022-10-08T00:55:30Z	--- license: cc-by-nc-4.0 library_name: fairseq task: audio-to-audio tags: - fairseq - audio - audio-to-audio - speech-to-speech-translation datasets: - MuST-C - TAT - Hokkien dramas --- ## xm_transformer_unity_hk-en Speech-to-speech translation model with two-pass decoder (UnitY) from fairseq: - Hokkien-English - Trained with supervised data in TED, drama, [TAT](https://sites.google.com/speech.ntut.edu.tw/fsw/home/tat-corpus) domain, and weakly supervised data in drama domain. See [here](https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation) for training details. - Speech synthesis with [facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur](https://huggingface.co/facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur) - [Project Page](https://github.com/facebookresearch/fairseq/tree/ust/examples/hokkien) ## Usage ```python import json import os from pathlib import Path import IPython.display as ipd from fairseq import hub_utils from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub from fairseq.models.speech_to_text.hub_interface import S2THubInterface from fairseq.models.text_to_speech import CodeHiFiGANVocoder from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface from huggingface_hub import snapshot_download import torchaudio cache_dir = os.getenv("HUGGINGFACE_HUB_CACHE") models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "facebook/xm_transformer_unity_hk-en", arg_overrides={"config_yaml": "config.yaml", "task": "speech_to_text"}, cache_dir=cache_dir, ) #model = models[0].cpu() #cfg["task"].cpu = True generator = task.build_generator([model], cfg) # requires 16000Hz mono channel audio audio, _ = torchaudio.load("/path/to/an/audio/file") sample = S2THubInterface.get_model_input(task, audio) unit = S2THubInterface.get_prediction(task, model, generator, sample) # speech synthesis library_name = "fairseq" cache_dir = ( cache_dir or (Path.home() / ".cache" / library_name).as_posix() ) cache_dir = snapshot_download( f"facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur", cache_dir=cache_dir, library_name=library_name ) x = hub_utils.from_pretrained( cache_dir, "model.pt", ".", archive_map=CodeHiFiGANVocoder.hub_models(), config_yaml="config.json", fp16=False, is_vocoder=True, ) with open(f"{x['args']['data']}/config.json") as f: vocoder_cfg = json.load(f) assert ( len(x["args"]["model_path"]) == 1 ), "Too many vocoder models in the input" vocoder = CodeHiFiGANVocoder(x["args"]["model_path"][0], vocoder_cfg) tts_model = VocoderHubInterface(vocoder_cfg, vocoder) tts_sample = tts_model.get_model_input(unit) wav, sr = tts_model.get_prediction(tts_sample) ipd.Audio(wav, rate=sr) ```
mclarknc/ppo-LunarLander-v2	mclarknc	2022-10-19T14:03:08Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-19T14:02:48Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.18 +/- 23.61 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
yuk/my-gothic-waifu-diffusion	yuk	2022-10-19T13:35:02Z	0	0	null	[ "license:bigscience-bloom-rail-1.0", "region:us" ]	null	2022-10-19T13:35:02Z	--- license: bigscience-bloom-rail-1.0 ---
kjhanjee/autotrain-code_classification-1815762639	kjhanjee	2022-10-19T11:01:40Z	1	0	transformers	[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:kjhanjee/autotrain-data-code_classification", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	text-classification	2022-10-19T10:56:20Z	--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - kjhanjee/autotrain-data-code_classification co2_eq_emissions: emissions: 11.438220107218369 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1815762639 - CO2 Emissions (in grams): 11.4382 ## Validation Metrics - Loss: 0.849 - Accuracy: 0.794 - Macro F1: 0.788 - Micro F1: 0.794 - Weighted F1: 0.788 - Macro Precision: 0.797 - Micro Precision: 0.794 - Weighted Precision: 0.797 - Macro Recall: 0.794 - Micro Recall: 0.794 - Weighted Recall: 0.794 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/kjhanjee/autotrain-code_classification-1815762639 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("kjhanjee/autotrain-code_classification-1815762639", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("kjhanjee/autotrain-code_classification-1815762639", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
TestZee/t5-small-baseline_summary_zee_v1.0	TestZee	2022-10-19T10:39:31Z	9	0	transformers	[ "transformers", "tf", "tensorboard", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-19T10:25:30Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-baseline_summary_zee_v1.0 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. --> # TestZee/t5-small-baseline_summary_zee_v1.0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3722 - Validation Loss: 2.1596 - Train Rouge1: 21.6350 - Train Rouge2: 8.9453 - Train Rougel: 17.8649 - Train Rougelsum: 19.9099 - Train Gen Len: 19.0 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results \| Train Loss \| Validation Loss \| Train Rouge1 \| Train Rouge2 \| Train Rougel \| Train Rougelsum \| Train Gen Len \| Epoch \| \|:----------:\|:---------------:\|:------------:\|:------------:\|:------------:\|:---------------:\|:-------------:\|:-----:\| \| 2.3722 \| 2.1596 \| 21.6350 \| 8.9453 \| 17.8649 \| 19.9099 \| 19.0 \| 0 \| ### Framework versions - Transformers 4.23.1 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.1
gaioNL/LunarLander-v2	gaioNL	2022-10-19T09:43:37Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-10-19T09:10:05Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 280.56 +/- 28.20 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
amichailidis/greek_legal_bert_v2-finetuned-ner-V2	amichailidis	2022-10-19T09:27:25Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-11T09:10:51Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: greek_legal_bert_v2-finetuned-ner-V3 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. --> # greek_legal_bert_v2-finetuned-ner-V3 This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0907 - Precision: 0.9023 - Recall: 0.9265 - F1: 0.9142 - Accuracy: 0.9828 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.19 \| 25 \| 0.0661 \| 0.8895 \| 0.9229 \| 0.9059 \| 0.9813 \| \| No log \| 2.38 \| 50 \| 0.0820 \| 0.9091 \| 0.9319 \| 0.9204 \| 0.9838 \| \| No log \| 3.57 \| 75 \| 0.0791 \| 0.8924 \| 0.9211 \| 0.9065 \| 0.9825 \| \| No log \| 4.76 \| 100 \| 0.0824 \| 0.8950 \| 0.9319 \| 0.9131 \| 0.9841 \| \| No log \| 5.95 \| 125 \| 0.0820 \| 0.8830 \| 0.9194 \| 0.9008 \| 0.9812 \| \| No log \| 7.14 \| 150 \| 0.0862 \| 0.9059 \| 0.9319 \| 0.9187 \| 0.9817 \| \| No log \| 8.33 \| 175 \| 0.0915 \| 0.9021 \| 0.9247 \| 0.9133 \| 0.9826 \| \| No log \| 9.52 \| 200 \| 0.0905 \| 0.9023 \| 0.9265 \| 0.9142 \| 0.9828 \| ### Framework versions - Transformers 4.23.0 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1
amichailidis/greek_legal_bert_v2-finetuned-ner	amichailidis	2022-10-19T09:21:07Z	19	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-08T09:21:14Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: greek_legal_bert_v2-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. --> # greek_legal_bert_v2-finetuned-ner This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0900 - Precision: 0.8424 - Recall: 0.8638 - F1: 0.8530 - Accuracy: 0.9775 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.64 \| 250 \| 0.0839 \| 0.7859 \| 0.8539 \| 0.8185 \| 0.9737 \| \| 0.1127 \| 1.29 \| 500 \| 0.0783 \| 0.8092 \| 0.8569 \| 0.8324 \| 0.9759 \| \| 0.1127 \| 1.93 \| 750 \| 0.0743 \| 0.8284 \| 0.8446 \| 0.8364 \| 0.9766 \| \| 0.0538 \| 2.58 \| 1000 \| 0.0816 \| 0.8243 \| 0.8597 \| 0.8416 \| 0.9774 \| \| 0.0538 \| 3.22 \| 1250 \| 0.0900 \| 0.8424 \| 0.8638 \| 0.8530 \| 0.9776 \| \| 0.0346 \| 3.87 \| 1500 \| 0.0890 \| 0.8401 \| 0.8597 \| 0.8498 \| 0.9770 \| \| 0.0346 \| 4.51 \| 1750 \| 0.0964 \| 0.8342 \| 0.8576 \| 0.8457 \| 0.9768 \| \| 0.0233 \| 5.15 \| 2000 \| 0.1094 \| 0.8336 \| 0.8645 \| 0.8488 \| 0.9768 \| \| 0.0233 \| 5.8 \| 2250 \| 0.1110 \| 0.8456 \| 0.8549 \| 0.8502 \| 0.9777 \| \| 0.0161 \| 6.44 \| 2500 \| 0.1224 \| 0.8408 \| 0.8535 \| 0.8471 \| 0.9769 \| \| 0.0161 \| 7.09 \| 2750 \| 0.1281 \| 0.8347 \| 0.8624 \| 0.8483 \| 0.9770 \| \| 0.0114 \| 7.73 \| 3000 \| 0.1268 \| 0.8397 \| 0.8573 \| 0.8484 \| 0.9773 \| \| 0.0114 \| 8.38 \| 3250 \| 0.1308 \| 0.8388 \| 0.8549 \| 0.8468 \| 0.9771 \| \| 0.0088 \| 9.02 \| 3500 \| 0.1301 \| 0.8412 \| 0.8559 \| 0.8485 \| 0.9772 \| \| 0.0088 \| 9.66 \| 3750 \| 0.1368 \| 0.8396 \| 0.8604 \| 0.8499 \| 0.9772 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
pcoloc/autotrain-only-rssi-1813762559	pcoloc	2022-10-19T08:57:26Z	7	0	transformers	[ "transformers", "joblib", "autotrain", "tabular", "regression", "tabular-regression", "dataset:pcoloc/autotrain-data-only-rssi", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	tabular-regression	2022-10-19T08:55:40Z	--- tags: - autotrain - tabular - regression - tabular-regression datasets: - pcoloc/autotrain-data-only-rssi co2_eq_emissions: emissions: 1.3554114117578944 --- # Model Trained Using AutoTrain - Problem type: Single Column Regression - Model ID: 1813762559 - CO2 Emissions (in grams): 1.3554 ## Validation Metrics - Loss: 83.432 - R2: 0.312 - MSE: 6960.888 - MAE: 60.449 - RMSLE: 0.532 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```
mriggs/byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it	mriggs	2022-10-19T08:42:40Z	6	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:opus_books", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-10-19T07:20:38Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books model-index: - name: byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it 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. --> # byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 0.9848 ## 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: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 1.3771 \| 1.0 \| 1819 \| 0.9848 \| ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1
amichailidis/bert-base-greek-uncased-v1-finetuned-ner	amichailidis	2022-10-19T08:32:01Z	13	0	transformers	[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-10-19T08:00:16Z	--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-greek-uncased-v1-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-base-greek-uncased-v1-finetuned-ner This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1052 - Precision: 0.8440 - Recall: 0.8566 - F1: 0.8503 - Accuracy: 0.9768 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 0.64 \| 250 \| 0.0913 \| 0.7814 \| 0.8208 \| 0.8073 \| 0.9728 \| \| 0.1144 \| 1.29 \| 500 \| 0.0823 \| 0.7940 \| 0.8448 \| 0.8342 \| 0.9755 \| \| 0.1144 \| 1.93 \| 750 \| 0.0812 \| 0.8057 \| 0.8212 \| 0.8328 \| 0.9751 \| \| 0.0570 \| 2.58 \| 1000 \| 0.0855 \| 0.8244 \| 0.8514 \| 0.8292 \| 0.9744 \| \| 0.0570 \| 3.22 \| 1250 \| 0.0926 \| 0.8329 \| 0.8441 \| 0.8397 \| 0.9760 \| \| 0.0393 \| 3.87 \| 1500 \| 0.0869 \| 0.8256 \| 0.8633 \| 0.8440 \| 0.9774 \| \| 0.0393 \| 4.51 \| 1750 \| 0.1049 \| 0.8290 \| 0.8636 \| 0.8459 \| 0.9766 \| \| 0.026 \| 5.15 \| 2000 \| 0.1093 \| 0.8440 \| 0.8566 \| 0.8503 \| 0.9768 \| \| 0.026 \| 5.8 \| 2250 \| 0.1172 \| 0.8301 \| 0.8514 \| 0.8406 \| 0.9760 \| \| 0.0189 \| 6.44 \| 2500 \| 0.1273 \| 0.8238 \| 0.8688 \| 0.8457 \| 0.9766 \| \| 0.0189 \| 7.09 \| 2750 \| 0.1246 \| 0.8350 \| 0.8539 \| 0.8443 \| 0.9764 \| \| 0.0148 \| 7.73 \| 3000 \| 0.1262 \| 0.8333 \| 0.8608 \| 0.8468 \| 0.9764 \| \| 0.0148 \| 8.38 \| 3250 \| 0.1347 \| 0.8319 \| 0.8591 \| 0.8453 \| 0.9762 \| \| 0.0010 \| 9.02 \| 3500 \| 0.1325 \| 0.8376 \| 0.8504 \| 0.8439 \| 0.9766 \| \| 0.0010 \| 9.66 \| 3750 \| 0.1362 \| 0.8371 \| 0.8563 \| 0.8466 \| 0.9765 \| ### Framework versions - Transformers 4.22.0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
crumb/eva-model-ckpt	crumb	2022-10-19T08:11:22Z	0	2	null	[ "region:us" ]	null	2022-10-19T04:45:12Z	storage for eva models. it has intermediate low-performing models

ctu-aic/xlm-roberta-large-xnli-enfever_nli

ctu-aic

2022-10-21T13:52:57Z

4

0

transformers

[ "transformers", "pytorch", "tf", "xlm-roberta", "text-classification", "arxiv:2201.11115", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-21T13:47:11Z

('---\ndatasets:\n- ctu-aic/enfever_nli\nlanguages:\n- cs\nlicense: cc-by-sa-4.0\ntags:\n- natural-language-inference\n\n---',) # 🦾 xlm-roberta-large-xnli-enfever_nli Transformer model for **Natural Language Inference** in ['cs'] languages finetuned on ['ctu-aic/enfever_nli'] datasets. ## 🧰 Usage ### 👾 Using UKPLab `sentence_transformers` `CrossEncoder` The model was trained using the `CrossEncoder` API and we recommend it for its usage. ```python from sentence_transformers.cross_encoder import CrossEncoder model = CrossEncoder('ctu-aic/xlm-roberta-large-xnli-enfever_nli') scores = model.predict([["My first context.", "My first hypothesis."], ["Second context.", "Hypothesis."]]) ``` ### 🤗 Using Huggingface `transformers` ```python from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("ctu-aic/xlm-roberta-large-xnli-enfever_nli") tokenizer = AutoTokenizer.from_pretrained("ctu-aic/xlm-roberta-large-xnli-enfever_nli") ``` ## 🌳 Contributing Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. ## 👬 Authors The model was trained and uploaded by **[ullriher](https://udb.fel.cvut.cz/?uid=ullriher&sn=&givenname=&_cmd=Hledat&_reqn=1&_type=user&setlang=en)** (e-mail: [[email protected]](mailto:[email protected])) The code was codeveloped by the NLP team at Artificial Intelligence Center of CTU in Prague ([AIC](https://www.aic.fel.cvut.cz/)). ## 🔐 License [cc-by-sa-4.0](https://choosealicense.com/licenses/cc-by-sa-4.0) ## 💬 Citation If you find this repository helpful, feel free to cite our publication: ``` @article{DBLP:journals/corr/abs-2201-11115, author = {Herbert Ullrich and Jan Drchal and Martin R{'{y}}par and Hana Vincourov{'{a}} and V{'{a}}clav Moravec}, title = {CsFEVER and CTKFacts: Acquiring Czech Data for Fact Verification}, journal = {CoRR}, volume = {abs/2201.11115}, year = {2022}, url = {https://arxiv.org/abs/2201.11115}, eprinttype = {arXiv}, eprint = {2201.11115}, timestamp = {Tue, 01 Feb 2022 14:59:01 +0100}, biburl = {https://dblp.org/rec/journals/corr/abs-2201-11115.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```

manirai91/enlm-r

manirai91

2022-10-21T13:50:54Z

73

0

transformers

[ "transformers", "pytorch", "tensorboard", "xlm-roberta", "fill-mask", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-08-01T07:58:38Z

--- tags: - generated_from_trainer model-index: - name: enlm-r results: [] ---  # enlm-r This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.4837 ## 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: 0.0006 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 128 - total_train_batch_size: 8192 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06 - lr_scheduler_type: polynomial - lr_scheduler_warmup_steps: 24000 - num_epochs: 81 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 6.4 | 0.33 | 160 | 10.7903 | | 6.4 | 0.66 | 320 | 10.1431 | | 6.4 | 0.99 | 480 | 9.8708 | | 6.4 | 0.33 | 640 | 9.3884 | | 6.4 | 0.66 | 800 | 8.7352 | | 6.4 | 0.99 | 960 | 8.3341 | | 6.4 | 1.33 | 1120 | 8.0614 | | 6.4 | 1.66 | 1280 | 7.8582 | | 4.2719 | 1.99 | 1440 | 7.4879 | | 3.2 | 3.3 | 1600 | 7.2689 | | 3.2 | 3.63 | 1760 | 7.1434 | | 3.2 | 3.96 | 1920 | 7.0576 | | 3.2 | 4.29 | 2080 | 7.0030 | | 3.2 | 4.62 | 2240 | 6.9612 | | 3.2 | 4.95 | 2400 | 6.9394 | | 3.2 | 5.28 | 2560 | 6.9559 | | 3.2 | 5.61 | 2720 | 6.8964 | | 3.2 | 5.94 | 2880 | 6.8939 | | 3.2 | 6.27 | 3040 | 6.8871 | | 3.2 | 6.6 | 3200 | 6.8771 | | 3.2 | 6.93 | 3360 | 6.8617 | | 3.2 | 7.26 | 3520 | 6.8472 | | 3.2 | 7.59 | 3680 | 6.8283 | | 3.2 | 7.92 | 3840 | 6.8082 | | 3.2 | 8.25 | 4000 | 6.8119 | | 3.2 | 8.58 | 4160 | 6.7962 | | 3.2 | 8.91 | 4320 | 6.7751 | | 3.2 | 9.24 | 4480 | 6.7405 | | 3.2 | 9.57 | 4640 | 6.7412 | | 3.2 | 9.9 | 4800 | 6.7279 | | 3.2 | 10.22 | 4960 | 6.7069 | | 3.2 | 10.55 | 5120 | 6.6998 | | 3.2 | 10.88 | 5280 | 6.6875 | | 3.2 | 11.22 | 5440 | 6.6580 | | 3.2 | 11.55 | 5600 | 6.6402 | | 3.2 | 11.88 | 5760 | 6.6281 | | 3.2 | 12.21 | 5920 | 6.6181 | | 3.2 | 12.54 | 6080 | 6.5995 | | 3.2 | 12.87 | 6240 | 6.5970 | | 3.2 | 13.2 | 6400 | 6.5772 | | 3.2 | 13.53 | 6560 | 6.5594 | | 3.2 | 13.85 | 6720 | 6.5400 | | 3.2 | 14.19 | 6880 | 6.5396 | | 3.2 | 14.51 | 7040 | 6.5211 | | 3.2 | 14.84 | 7200 | 6.5140 | | 3.2 | 15.18 | 7360 | 6.4002 | | 3.2 | 15.5 | 7520 | 6.3170 | | 3.2 | 15.83 | 7680 | 6.2621 | | 3.2 | 16.16 | 7840 | 6.2253 | | 3.2 | 16.49 | 8000 | 6.1722 | | 3.2 | 16.82 | 8160 | 6.1106 | | 3.2 | 17.15 | 8320 | 6.1281 | | 3.2 | 17.48 | 8480 | 6.0019 | | 3.2 | 17.81 | 8640 | 5.9069 | | 3.2 | 18.14 | 8800 | 5.7105 | | 3.2 | 18.47 | 8960 | 5.2741 | | 3.2 | 18.8 | 9120 | 5.0369 | | 5.0352 | 19.13 | 9280 | 4.8148 | | 4.5102 | 19.26 | 9440 | 4.3175 | | 4.1247 | 19.59 | 9600 | 3.9518 | | 3.8443 | 20.12 | 9760 | 3.6712 | | 3.6334 | 20.45 | 9920 | 3.4654 | | 3.4698 | 20.78 | 10080 | 3.2994 | | 3.3267 | 21.11 | 10240 | 3.1638 | | 3.2173 | 21.44 | 10400 | 3.0672 | | 3.1255 | 21.77 | 10560 | 2.9687 | | 3.0344 | 22.1 | 10720 | 2.8865 | | 2.9645 | 22.43 | 10880 | 2.8104 | | 2.9046 | 22.76 | 11040 | 2.7497 | | 2.8707 | 23.09 | 11200 | 2.7040 | | 2.7903 | 23.42 | 11360 | 2.6416 | | 2.7339 | 23.75 | 11520 | 2.5891 | | 2.6894 | 24.08 | 11680 | 2.5370 | | 2.6461 | 24.41 | 11840 | 2.4960 | | 2.5976 | 24.74 | 12000 | 2.4508 | | 2.5592 | 25.07 | 12160 | 2.4194 | | 2.5305 | 25.4 | 12320 | 2.3790 | | 2.4993 | 25.73 | 12480 | 2.3509 | | 2.465 | 26.06 | 12640 | 2.3173 | | 2.4455 | 26.39 | 12800 | 2.2934 | | 2.4107 | 26.72 | 12960 | 2.2701 | | 2.3883 | 27.05 | 13120 | 2.2378 | | 2.3568 | 27.38 | 13280 | 2.2079 | | 2.3454 | 27.71 | 13440 | 2.1919 | | 2.3207 | 28.04 | 13600 | 2.1671 | | 2.2963 | 28.37 | 13760 | 2.1513 | | 2.2738 | 28.7 | 13920 | 2.1326 | | 2.2632 | 29.03 | 14080 | 2.1176 | | 2.2413 | 29.36 | 14240 | 2.0913 | | 2.2193 | 29.69 | 14400 | 2.0772 | | 2.2169 | 30.02 | 14560 | 2.0692 | | 2.1848 | 30.35 | 14720 | 2.0411 | | 2.1693 | 30.68 | 14880 | 2.0290 | | 2.1964 | 31.01 | 15040 | 2.0169 | | 2.1467 | 31.34 | 15200 | 2.0016 | | 2.1352 | 31.67 | 15360 | 1.9880 | | 2.1152 | 32.0 | 15520 | 1.9727 | | 2.1098 | 32.33 | 15680 | 1.9604 | | 2.0888 | 32.66 | 15840 | 1.9521 | | 2.0837 | 32.99 | 16000 | 1.9394 | | 2.0761 | 33.32 | 16160 | 1.9366 | | 2.0635 | 33.65 | 16320 | 1.9200 | | 2.0631 | 33.98 | 16480 | 1.9147 | | 2.0448 | 34.31 | 16640 | 1.9053 | | 2.0452 | 34.64 | 16800 | 1.8937 | | 2.0303 | 34.97 | 16960 | 1.8801 | | 2.0184 | 35.3 | 17120 | 1.8752 | | 2.0115 | 35.63 | 17280 | 1.8667 | | 2.0042 | 35.96 | 17440 | 1.8626 | | 2.002 | 36.29 | 17600 | 1.8565 | | 1.9918 | 36.62 | 17760 | 1.8475 | | 1.9868 | 36.95 | 17920 | 1.8420 | | 1.9796 | 37.28 | 18080 | 1.8376 | | 1.976 | 37.61 | 18240 | 1.8318 | | 1.9647 | 37.94 | 18400 | 1.8225 | | 1.9561 | 38.27 | 18560 | 1.8202 | | 1.9544 | 38.6 | 18720 | 1.8084 | | 1.9454 | 38.93 | 18880 | 1.8057 | | 1.9333 | 39.26 | 19040 | 1.8030 | | 1.9411 | 39.59 | 19200 | 1.7966 | | 1.9289 | 39.92 | 19360 | 1.7865 | | 1.9261 | 40.25 | 19520 | 1.7815 | | 1.9207 | 40.58 | 19680 | 1.7881 | | 1.9164 | 40.91 | 19840 | 1.7747 | | 1.9152 | 41.24 | 20000 | 1.7786 | | 1.914 | 41.57 | 20160 | 1.7664 | | 1.901 | 41.9 | 20320 | 1.7586 | | 1.8965 | 42.23 | 20480 | 1.7554 | | 1.8982 | 42.56 | 20640 | 1.7524 | | 1.8941 | 42.89 | 20800 | 1.7460 | | 1.8834 | 43.22 | 20960 | 1.7488 | | 1.8841 | 43.55 | 21120 | 1.7486 | | 1.8846 | 43.88 | 21280 | 1.7424 | | 1.8763 | 44.21 | 21440 | 1.7352 | | 1.8688 | 44.54 | 21600 | 1.7349 | | 1.8714 | 44.87 | 21760 | 1.7263 | | 1.8653 | 45.2 | 21920 | 1.7282 | | 1.8673 | 45.53 | 22080 | 1.7195 | | 1.8682 | 45.85 | 22240 | 1.7266 | | 1.8532 | 46.19 | 22400 | 1.7180 | | 1.8553 | 46.51 | 22560 | 1.7137 | | 1.8569 | 46.84 | 22720 | 1.7158 | | 1.8469 | 47.18 | 22880 | 1.7117 | | 1.845 | 47.5 | 23040 | 1.7031 | | 1.8475 | 47.83 | 23200 | 1.7089 | | 1.845 | 48.16 | 23360 | 1.7018 | | 1.8391 | 48.49 | 23520 | 1.6945 | | 1.8456 | 48.82 | 23680 | 1.7015 | | 1.8305 | 49.15 | 23840 | 1.6964 | | 1.8324 | 49.48 | 24000 | 1.6900 | | 1.7763 | 49.81 | 24160 | 1.6449 | | 1.7728 | 50.14 | 24320 | 1.6436 | | 1.7576 | 50.47 | 24480 | 1.6268 | | 1.7354 | 50.8 | 24640 | 1.6088 | | 1.74 | 51.13 | 24800 | 1.6156 | | 1.7251 | 51.06 | 24960 | 1.6041 | | 1.719 | 51.39 | 25120 | 1.5938 | | 1.7257 | 52.12 | 25280 | 1.5983 | | 1.7184 | 52.45 | 25440 | 1.5919 | | 1.7093 | 52.78 | 25600 | 1.5848 | | 1.7114 | 53.11 | 25760 | 1.5922 | | 1.7076 | 53.44 | 25920 | 1.5843 | | 1.7 | 53.77 | 26080 | 1.5807 | | 1.7027 | 54.1 | 26240 | 1.5811 | | 1.704 | 54.43 | 26400 | 1.5766 | | 1.6958 | 54.76 | 26560 | 1.5756 | | 1.6976 | 55.09 | 26720 | 1.5773 | | 1.6944 | 55.42 | 26880 | 1.5725 | | 1.6891 | 55.75 | 27040 | 1.5685 | | 1.6936 | 56.08 | 27200 | 1.5750 | | 1.6893 | 56.41 | 27360 | 1.5696 | | 1.6886 | 56.74 | 27520 | 1.5643 | | 1.6936 | 57.07 | 27680 | 1.5691 | | 1.6883 | 57.4 | 27840 | 1.5718 | | 1.6832 | 57.73 | 28000 | 1.5660 | | 1.9222 | 28.03 | 28160 | 1.7107 | | 1.7838 | 28.19 | 28320 | 1.6345 | | 1.7843 | 28.36 | 28480 | 1.6445 | | 1.7809 | 28.52 | 28640 | 1.6461 | | 1.783 | 28.69 | 28800 | 1.6505 | | 1.7869 | 28.85 | 28960 | 1.6364 | | 1.778 | 29.02 | 29120 | 1.6363 | | 1.775 | 29.18 | 29280 | 1.6364 | | 1.7697 | 29.34 | 29440 | 1.6345 | | 1.7719 | 29.51 | 29600 | 1.6261 | | 1.7454 | 61.16 | 29760 | 1.6099 | | 1.741 | 61.49 | 29920 | 1.6006 | | 1.7314 | 62.02 | 30080 | 1.6041 | | 1.7314 | 62.35 | 30240 | 1.5914 | | 1.7246 | 62.68 | 30400 | 1.5917 | | 1.7642 | 63.01 | 30560 | 1.5923 | | 1.7221 | 63.34 | 30720 | 1.5857 | | 1.7185 | 63.67 | 30880 | 1.5836 | | 1.7022 | 64.0 | 31040 | 1.5836 | | 1.7107 | 64.33 | 31200 | 1.5739 | | 1.7082 | 64.66 | 31360 | 1.5724 | | 1.7055 | 64.99 | 31520 | 1.5734 | | 1.7019 | 65.32 | 31680 | 1.5707 | | 1.699 | 65.65 | 31840 | 1.5649 | | 1.6963 | 65.98 | 32000 | 1.5685 | | 1.6935 | 66.31 | 32160 | 1.5673 | | 1.6899 | 66.64 | 32320 | 1.5648 | | 1.6869 | 66.97 | 32480 | 1.5620 | | 1.6867 | 67.3 | 32640 | 1.5564 | | 1.6861 | 67.63 | 32800 | 1.5552 | | 1.6831 | 67.96 | 32960 | 1.5496 | | 1.6778 | 68.29 | 33120 | 1.5479 | | 1.6742 | 68.62 | 33280 | 1.5501 | | 1.6737 | 68.95 | 33440 | 1.5441 | | 1.6725 | 69.28 | 33600 | 1.5399 | | 1.6683 | 69.61 | 33760 | 1.5398 | | 1.6689 | 69.94 | 33920 | 1.5374 | | 1.6634 | 70.27 | 34080 | 1.5385 | | 1.6638 | 70.6 | 34240 | 1.5332 | | 1.6614 | 70.93 | 34400 | 1.5329 | | 1.6544 | 71.26 | 34560 | 1.5292 | | 1.6532 | 71.59 | 34720 | 1.5268 | | 1.6511 | 71.92 | 34880 | 1.5225 | | 1.6506 | 72.25 | 35040 | 1.5219 | | 1.6496 | 72.58 | 35200 | 1.5202 | | 1.6468 | 72.91 | 35360 | 1.5199 | | 1.6424 | 73.24 | 35520 | 1.5220 | | 1.642 | 73.57 | 35680 | 1.5145 | | 1.6415 | 73.9 | 35840 | 1.5139 | | 1.6419 | 74.23 | 36000 | 1.5120 | | 1.633 | 74.56 | 36160 | 1.5113 | | 1.6354 | 74.89 | 36320 | 1.5139 | | 1.6312 | 75.22 | 36480 | 1.5068 | | 1.6298 | 75.55 | 36640 | 1.5056 | | 1.6268 | 75.88 | 36800 | 1.5000 | | 1.6277 | 76.21 | 36960 | 1.5033 | | 1.6198 | 76.54 | 37120 | 1.4988 | | 1.6246 | 76.87 | 37280 | 1.4978 | | 1.6184 | 77.2 | 37440 | 1.4966 | | 1.6187 | 77.53 | 37600 | 1.4954 | | 1.6192 | 77.85 | 37760 | 1.4951 | | 1.6134 | 78.19 | 37920 | 1.4936 | | 1.6176 | 78.51 | 38080 | 1.4908 | | 1.6103 | 78.84 | 38240 | 1.4904 | | 1.612 | 79.18 | 38400 | 1.4919 | | 1.611 | 79.5 | 38560 | 1.4891 | | 1.6082 | 79.83 | 38720 | 1.4837 | | 1.6047 | 80.16 | 38880 | 1.4859 | | 1.6058 | 80.49 | 39040 | 1.4814 | | 1.602 | 80.82 | 39200 | 1.4837 | ### Framework versions - Transformers 4.20.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

DemangeJeremy/4-sentiments-with-flaubert

DemangeJeremy

2022-10-21T13:46:12Z

13

0

transformers

[ "transformers", "pytorch", "flaubert", "text-classification", "sentiments", "french", "flaubert-large", "fr", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-03-02T23:29:04Z

--- language: fr tags: - sentiments - text-classification - flaubert - french - flaubert-large --- # Modèle de détection de 4 sentiments avec FlauBERT (mixed, negative, objective, positive) ### Comment l'utiliser ? ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import pipeline loaded_tokenizer = AutoTokenizer.from_pretrained('flaubert/flaubert_large_cased') loaded_model = AutoModelForSequenceClassification.from_pretrained("DemangeJeremy/4-sentiments-with-flaubert") nlp = pipeline('sentiment-analysis', model=loaded_model, tokenizer=loaded_tokenizer) print(nlp("Je suis plutôt confiant.")) ``` ``` [{'label': 'OBJECTIVE', 'score': 0.3320835530757904}] ``` ## Résultats de l'évaluation du modèle | Epoch | Validation Loss | Samples Per Second | |:------:|:--------------:|:------------------:| | 1 | 2.219246 | 49.476000 | | 2 | 1.883753 | 47.259000 | | 3 | 1.747969 | 44.957000 | | 4 | 1.695606 | 43.872000 | | 5 | 1.641470 | 45.726000 | ## Citation Pour toute utilisation de ce modèle, merci d'utiliser cette citation : > Jérémy Demange, Four sentiments with FlauBERT, (2021), Hugging Face repository, <https://huggingface.co/DemangeJeremy/4-sentiments-with-flaubert>

orkg/orkgnlp-predicates-clustering

orkg

2022-10-21T13:40:57Z

0

null

[ "onnx", "license:mit", "region:us" ]

null

2022-05-09T08:02:12Z

--- license: mit --- This Repository includes the files required to run the `Predicates Clustering` ORKG-NLP service. Please check [this article](https://orkg-nlp-pypi.readthedocs.io/en/latest/services/services.html) for more details about the service. The [Scikit-Learn](https://scikit-learn.org/stable/) models are converted using [skl2onnx](https://github.com/onnx/sklearn-onnx) and may not include all original scikit-learn functionalities.

asi/albert-act-base

asi

2022-10-21T13:26:29Z

10

1

transformers

[ "transformers", "pytorch", "tf", "tensorboard", "albert_act", "en", "dataset:wikipedia", "dataset:bookcorpus", "arxiv:1603.08983", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

null

2022-10-11T20:33:26Z

--- license: apache-2.0 language: en datasets: - wikipedia - bookcorpus model-index: - name: asi/albert-act-base results: - task: type: text-classification name: CoLA dataset: type: glue name: CoLA # General Language Understanding Evaluation benchmark (GLUE) split: cola metrics: - type: matthews_correlation value: 36.7 name: Matthew's Corr - task: type: text-classification name: SST-2 dataset: type: glue name: SST-2 # The Stanford Sentiment Treebank split: sst2 metrics: - type: accuracy value: 87.8 name: Accuracy - task: type: text-classification name: MRPC dataset: type: glue name: MRPC # Microsoft Research Paraphrase Corpus split: mrpc metrics: - type: accuracy value: 81.4 name: Accuracy - type: f1 value: 86.5 name: F1 - task: type: text-similarity name: STS-B dataset: type: glue name: STS-B # Semantic Textual Similarity Benchmark split: stsb metrics: - type: spearmanr value: 83.0 name: Spearman Corr - type: pearsonr value: 84.2 name: Pearson Corr - task: type: text-classification name: QQP dataset: type: glue name: QQP # Quora Question Pairs split: qqp metrics: - type: f1 value: 68.5 name: F1 - type: accuracy value: 87.7 name: Accuracy - task: type: text-classification name: MNLI-m dataset: type: glue name: MNLI-m # MultiNLI Matched split: mnli_matched metrics: - type: accuracy value: 79.9 name: Accuracy - task: type: text-classification name: MNLI-mm dataset: type: glue name: MNLI-mm # MultiNLI Matched split: mnli_mismatched metrics: - type: accuracy value: 79.2 name: Accuracy - task: type: text-classification name: QNLI dataset: type: glue name: QNLI # Question NLI split: qnli metrics: - type: accuracy value: 89.0 name: Accuracy - task: type: text-classification name: RTE dataset: type: glue name: RTE # Recognizing Textual Entailment split: rte metrics: - type: accuracy value: 63.0 name: Accuracy - task: type: text-classification name: WNLI dataset: type: glue name: WNLI # Winograd NLI split: wnli metrics: - type: accuracy value: 65.1 name: Accuracy --- # Adaptive Depth Transformers Implementation of the paper "How Many Layers and Why? An Analysis of the Model Depth in Transformers". In this study, we investigate the role of the multiple layers in deep transformer models. We design a variant of ALBERT that dynamically adapts the number of layers for each token of the input. ## Model architecture We augment a multi-layer transformer encoder with a halting mechanism, which dynamically adjusts the number of layers for each token. We directly adapted this mechanism from Graves ([2016](#graves-2016)). At each iteration, we compute a probability for each token to stop updating its state. ## Model use The architecture is not yet directly included in the Transformers library. The code used for pre-training is available in the following [github repository](https://github.com/AntoineSimoulin/adaptive-depth-transformers). So you should install the code implementation first: ```bash !pip install git+https://github.com/AntoineSimoulin/adaptive-depth-transformers$ ``` Then you can use the model directly. ```python from act import AlbertActConfig, AlbertActModel, TFAlbertActModel from transformers import AlbertTokenizer tokenizer = AlbertTokenizer.from_pretrained('asi/albert-act-base') model = AlbertActModel.from_pretrained('asi/albert-act-base') _ = model.eval() inputs = tokenizer("a lump in the middle of the monkeys stirred and then fell quiet .", return_tensors="pt") outputs = model(**inputs) outputs.updates # tensor([[[[15., 9., 10., 7., 3., 8., 5., 7., 12., 10., 6., 8., 8., 9., 5., 8.]]]]) ``` ## Citations ### BibTeX entry and citation info If you use our iterative transformer model for your scientific publication or your industrial applications, please cite the following [paper](https://aclanthology.org/2021.acl-srw.23/): ```bibtex @inproceedings{simoulin-crabbe-2021-many, title = "How Many Layers and Why? {A}n Analysis of the Model Depth in Transformers", author = "Simoulin, Antoine and Crabb{\'e}, Benoit", booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop", month = aug, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.acl-srw.23", doi = "10.18653/v1/2021.acl-srw.23", pages = "221--228", } ``` ### References ><div id="graves-2016">Alex Graves. 2016. <a href="https://arxiv.org/abs/1603.08983" target="_blank">Adaptive computation time for recurrent neural networks.</a> CoRR, abs/1603.08983.</div>

huggingtweets/tszzl

huggingtweets

2022-10-21T12:32:06Z

5

1

transformers

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

text-generation

2022-03-02T23:29:05Z

--- language: en thumbnail: http://www.huggingtweets.com/tszzl/1666355521581/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/1572784789291401216/1WrwslUF_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">roon</div> <div style="text-align: center; font-size: 14px;">@tszzl</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 roon. | Data | roon | | --- | --- | | Tweets downloaded | 3207 | | Retweets | 779 | | Short tweets | 375 | | Tweets kept | 2053 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nr9oggv1/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 @tszzl's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/12g6sck7/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/tszzl') 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)

Yinxing/ddpm-butterflies-128

Yinxing

2022-10-21T12:05:23Z

0

diffusers

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

null

2022-10-21T10:51:28Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` 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: 16 - eval_batch_size: 16 - 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/Yinxing/ddpm-butterflies-128/tensorboard?#scalars)

ashish23993/t5-small-finetuned-xsum-a

ashish23993

2022-10-21T10:48:19Z

6

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

2022-10-21T10:43:22Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: t5-small-finetuned-xsum-a results: [] ---  # t5-small-finetuned-xsum-a This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 8 | 2.2554 | 21.1449 | 9.0713 | 17.7765 | 20.1134 | 19.0 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

hezzze/a2c-AntBulletEnv-v0

hezzze

2022-10-21T09:34:26Z

2

0

stable-baselines3

[ "stable-baselines3", "AntBulletEnv-v0", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T09:33:16Z

--- library_name: stable-baselines3 tags: - AntBulletEnv-v0 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: A2C results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: AntBulletEnv-v0 type: AntBulletEnv-v0 metrics: - type: mean_reward value: 1658.74 +/- 204.55 name: mean_reward verified: false --- # **A2C** Agent playing **AntBulletEnv-v0** This is a trained model of a **A2C** agent playing **AntBulletEnv-v0** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

asapcreditrepairusa/Credit-Repair-Houston

asapcreditrepairusa

2022-10-21T09:33:48Z

0

null

[ "region:us" ]

null

2022-10-21T09:33:11Z

ASAP Credit Repair has two critical missions, 1) to provide an effective and inexpensive option for credit repair and 2) to provide the best customer service experience along the way. We hope you choose [ASAP Credit Repair](https://asapcreditrepairusa.com) for your future credit repair needs.

nicolarici/LawBERT-IT_trained

nicolarici

2022-10-21T08:00:23Z

1

0

transformers

[ "transformers", "pytorch", "bert", "pretraining", "endpoints_compatible", "region:us" ]

null

2022-10-21T07:45:05Z

**LawBERT-IT** An Italian BERT model for the legal domain. The code used for developing and training the model and the dataset used to extract the new words and continue the training of the BERT model are available on [GitHub](https://github.com/nicolarici/LawBERT-IT).

teacookies/autotrain-21102022-cert-1827562840

teacookies

2022-10-21T07:41:52Z

12

0

transformers

[ "transformers", "pytorch", "autotrain", "token-classification", "unk", "dataset:teacookies/autotrain-data-21102022-cert", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

token-classification

2022-10-21T07:29:56Z

--- tags: - autotrain - token-classification language: - unk widget: - text: "I love AutoTrain 🤗" datasets: - teacookies/autotrain-data-21102022-cert co2_eq_emissions: emissions: 19.94429730071814 --- # Model Trained Using AutoTrain - Problem type: Entity Extraction - Model ID: 1827562840 - CO2 Emissions (in grams): 19.9443 ## Validation Metrics - Loss: 0.028 - Accuracy: 0.992 - Precision: 0.820 - Recall: 0.885 - F1: 0.851 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-21102022-cert-1827562840 ``` Or Python API: ``` from transformers import AutoModelForTokenClassification, AutoTokenizer model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-21102022-cert-1827562840", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

Bugpie/dummy-model

Bugpie

2022-10-21T07:04:44Z

15

0

transformers

[ "transformers", "pytorch", "camembert", "fill-mask", "fr", "dataset:oscar", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-10-19T11:49:58Z

--- language: fr license: mit datasets: - oscar --- ## Model description CamemBERT is a state-of-the-art language model for French based on the RoBERTa model. It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains. ## Evaluation The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI). ## Limitations and bias Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). This model was pretrinaed on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the [OSCAR dataset card](https://huggingface.co/datasets/oscar), include the following: > The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages. > Constructed from Common Crawl, Personal and sensitive information might be present. ## Training data OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture. ## How to use -**Filling masks using pipeline** ```python >>> from transformers import pipeline >>> camembert_fill_mask = pipeline("fill-mask", model="camembert-base") >>> results = camembert_fill_mask("Le camembert est <mask> :)") >>> result [{'score': 0.49091097712516785, 'token': 7200, 'token_str': 'délicieux', 'sequence': 'Le camembert est délicieux :)'}, {'score': 0.1055697426199913, 'token': 2183, 'token_str': 'excellent', 'sequence': 'Le camembert est excellent :)'}, {'score': 0.03453319892287254, 'token': 26202, 'token_str': 'succulent', 'sequence': 'Le camembert est succulent :)'}, {'score': 0.03303128108382225, 'token': 528, 'token_str': 'meilleur', 'sequence': 'Le camembert est meilleur :)'}, {'score': 0.030076386407017708, 'token': 1654, 'token_str': 'parfait', 'sequence': 'Le camembert est parfait :)'}] ``` -**Extract contextual embedding features from Camembert output** ```python import torch >>> tokenized_sentence = tokenizer.tokenize("J'aime le camembert !") >>> encoded_sentence = tokenizer.encode(tokenized_sentence) # Can be done in one step : tokenize.encode("J'aime le camembert !") >>> tokenized_sentence ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] >>> encoded_sentence [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] ``` ![128791.gif](https://s3.amazonaws.com/moonup/production/uploads/1666329291279-634fe2e8cfefce6e57795f69.gif) [more about](https://youtu.be/dMTy6C4UiQ4)

nlp-waseda/roberta-large-japanese-with-auto-jumanpp

nlp-waseda

2022-10-21T06:55:27Z

1,733

3

transformers

[ "transformers", "pytorch", "roberta", "fill-mask", "ja", "dataset:wikipedia", "dataset:cc100", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-10-15T05:40:40Z

--- language: ja license: cc-by-sa-4.0 datasets: - wikipedia - cc100 mask_token: "[MASK]" widget: - text: "早稲田大学で自然言語処理を[MASK]する。" --- # nlp-waseda/roberta-large-japanese-with-auto-jumanpp ## Model description This is a Japanese RoBERTa large model pretrained on Japanese Wikipedia and the Japanese portion of CC-100. ## How to use You can use this model for masked language modeling as follows: ```python from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nlp-waseda/roberta-large-japanese-with-auto-jumanpp") model = AutoModelForMaskedLM.from_pretrained("nlp-waseda/roberta-large-japanese-with-auto-jumanpp") sentence = '早稲田大学で自然言語処理を[MASK]する。' encoding = tokenizer(sentence, return_tensors='pt') ... ``` You can fine-tune this model on downstream tasks. ## Tokenization `BertJapaneseTokenizer` now supports automatic tokenization for [Juman++](https://github.com/ku-nlp/jumanpp). However, if your dataset is large, you may take a long time since `BertJapaneseTokenizer` still does not supoort fast tokenization. You can still do the Juman++ tokenization by your self and use the old model [nlp-waseda/roberta-large-japanese](https://huggingface.co/nlp-waseda/roberta-large-japanese). Juman++ 2.0.0-rc3 was used for pretraining. Each word is tokenized into tokens by [sentencepiece](https://github.com/google/sentencepiece). ## Vocabulary The vocabulary consists of 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC)) and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece). ## Training procedure This model was trained on Japanese Wikipedia (as of 20210920) and the Japanese portion of CC-100. It took two weeks using eight NVIDIA A100 GPUs. The following hyperparameters were used during pretraining: - learning_rate: 6e-5 - per_device_train_batch_size: 103 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 5 - total_train_batch_size: 4120 - max_seq_length: 128 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-6 - lr_scheduler_type: linear - training_steps: 670000 - warmup_steps: 10000 - mixed_precision_training: Native AMP ## Performance on JGLUE See the [Baseline Scores](https://github.com/yahoojapan/JGLUE#baseline-scores) of JGLUE.

salascorp/distilroberta-base-mrpc-glue-oscar-salas2

salascorp

2022-10-21T06:40:47Z

3

0

transformers

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

text-classification

2022-10-21T06:36:59Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer model-index: - name: distilroberta-base-mrpc-glue-oscar-salas2 results: [] ---  # distilroberta-base-mrpc-glue-oscar-salas2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - Loss: 1.5094 ## 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 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cpu - Datasets 2.6.1 - Tokenizers 0.13.1

NinedayWang/PolyCoder-0.4B

NinedayWang

2022-10-21T06:03:41Z

97

4

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "arxiv:2202.13169", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-20T09:45:17Z

This is a PolyCoder model with **0.4B** parameters, presented in the paper ["A Systematic Evaluation of Large Language Models of Code"](https://arxiv.org/pdf/2202.13169.pdf) (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code). The model was trained on **249 GB** of code across **12** programming languages. **Note** - this model requires `transformers` version of at least **4.23.0**: ``` pip install transformers==4.23.0 ``` For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs) If you use this model, please cite: ``` @inproceedings{ xu2022polycoder, title={A Systematic Evaluation of Large Language Models of Code}, author={Frank F. Xu and Uri Alon and Graham Neubig and Vincent Josua Hellendoorn}, booktitle={Deep Learning for Code Workshop}, year={2022}, url={https://openreview.net/forum?id=SLcEnoObJZq} } ```

NinedayWang/PolyCoder-2.7B

NinedayWang

2022-10-21T06:03:23Z

314

50

transformers

[ "transformers", "pytorch", "gpt_neox", "text-generation", "arxiv:2202.13169", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text-generation

2022-10-20T09:47:34Z

This is a PolyCoder model with **2.7B** parameters, presented in the paper ["A Systematic Evaluation of Large Language Models of Code"](https://arxiv.org/pdf/2202.13169.pdf) (MAPS'2022 and ICLR'2022 Workshop Deep Learning 4 Code). The model was trained on **249 GB** of code across **12** programming languages. **Note** - this model requires `transformers` version of at least **4.23.0**: ``` pip install transformers==4.23.0 ``` For more information, see: [https://github.com/VHellendoorn/Code-LMs](https://github.com/VHellendoorn/Code-LMs) If you use this model, please cite: ``` @inproceedings{ xu2022polycoder, title={A Systematic Evaluation of Large Language Models of Code}, author={Frank F. Xu and Uri Alon and Graham Neubig and Vincent Josua Hellendoorn}, booktitle={Deep Learning for Code Workshop}, year={2022}, url={https://openreview.net/forum?id=SLcEnoObJZq} } ```

jo-kwsm/distilbert-base-uncased-finetuned-emotion

jo-kwsm

2022-10-21T06:02:46Z

3

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

2022-10-21T03:31:19Z

--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.9255 - name: F1 type: f1 value: 0.9253582087556043 ---  # 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: 0.2244 - Accuracy: 0.9255 - F1: 0.9254 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8602 | 1.0 | 250 | 0.3344 | 0.901 | 0.8979 | | 0.263 | 2.0 | 500 | 0.2244 | 0.9255 | 0.9254 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1+cu102 - Datasets 1.16.1 - Tokenizers 0.10.3

stanford-crfm/levanter-gpt

stanford-crfm

2022-10-21T05:33:26Z

9

0

transformers

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

text-generation

2022-10-03T03:16:18Z

--- pipeline_tag: text-generation widget: text: You could not prevent a thunderstorm, but you could use --- Levanter GPT is trained on OpenWebText2. More complete model card will be made in the future.

api19750904/VM-Fast_Check

api19750904

2022-10-21T04:30:55Z

72

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-10-21T04:30:42Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: VM-Fast_Check results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9101123809814453 --- # VM-Fast_Check Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### person drinking ![person drinking](images/person_drinking.jpg) #### person smoking ![person smoking](images/person_smoking.jpg) #### swimsuit boy ![swimsuit boy](images/swimsuit_boy.jpg) #### swimsuit girl ![swimsuit girl](images/swimsuit_girl.jpg)

edbeeching/atari_2B_atari_yarsrevenge_2222

edbeeching

2022-10-21T04:26:27Z

3

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T04:25:25Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_yarsrevenge type: atari_yarsrevenge metrics: - type: mean_reward value: 336431.19 +/- 148269.98 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_yarsrevenge** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_2B_atari_wizardofwor_2222

edbeeching

2022-10-21T04:21:31Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T04:20:36Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_wizardofwor type: atari_wizardofwor metrics: - type: mean_reward value: 61420.00 +/- 23105.79 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_wizardofwor** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

huggingtweets/elonmusk-mar15sa-sergiorocks

huggingtweets

2022-10-21T04:07:50Z

3

0

transformers

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

text-generation

2022-10-21T04:06:32Z

--- language: en thumbnail: http://www.huggingtweets.com/elonmusk-mar15sa-sergiorocks/1666325239514/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/1580062742693699584/RJ5EI7PS_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/1142324885550751744/wVNatx7J_400x400.png')"> </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/566329118489194496/f_ALTi7v_400x400.jpeg')"> </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">Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg</div> <div style="text-align: center; font-size: 14px;">@elonmusk-mar15sa-sergiorocks</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 Elon Musk & Sergio Pereira 🚀 & Marissa Goldberg. | Data | Elon Musk | Sergio Pereira 🚀 | Marissa Goldberg | | --- | --- | --- | --- | | Tweets downloaded | 3200 | 3250 | 3248 | | Retweets | 133 | 18 | 301 | | Short tweets | 949 | 54 | 110 | | Tweets kept | 2118 | 3178 | 2837 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ahul38aq/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 @elonmusk-mar15sa-sergiorocks's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r3916r2/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/elonmusk-mar15sa-sergiorocks') 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)

edbeeching/atari_2B_atari_timepilot_2222

edbeeching

2022-10-21T03:38:54Z

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T03:37:51Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_timepilot type: atari_timepilot metrics: - type: mean_reward value: 88855.00 +/- 25100.17 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_timepilot** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_2B_atari_tennis_2222

edbeeching

2022-10-21T03:32:46Z

1

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T03:31:38Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_tennis type: atari_tennis metrics: - type: mean_reward value: 23.00 +/- 1.10 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_tennis** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

huggingtweets/levelsio

huggingtweets

2022-10-21T03:28:44Z

3

0

transformers

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

text-generation

2022-10-21T03:27:24Z

--- language: en thumbnail: http://www.huggingtweets.com/levelsio/1666322920443/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/1562107516066095106/IUccJ78Y_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">@levelsio</div> <div style="text-align: center; font-size: 14px;">@levelsio</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 @levelsio. | Data | @levelsio | | --- | --- | | Tweets downloaded | 3243 | | Retweets | 173 | | Short tweets | 535 | | Tweets kept | 2535 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/tof4zha8/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 @levelsio's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lcpeawur/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/levelsio') 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)

Adipta/setfit-model-test-2

Adipta

2022-10-21T02:39:05Z

2

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-21T02:38:54Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # {MODEL_NAME} This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('{MODEL_NAME}') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}') model = AutoModel.from_pretrained('{MODEL_NAME}') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 40 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 40, "warmup_steps": 4, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors

xxxxxxxxxxxxxxxxxxxxxx/model-y

xxxxxxxxxxxxxxxxxxxxxx

2022-10-21T01:49:43Z

0

null

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

null

2022-10-17T07:21:03Z

--- license: wtfpl --- # wwww ```typescript import React, { CSSProperties, PropsWithRef } from 'react'; import MarkdownPreview, { MarkdownPreviewProps } from '@uiw/react-markdown-preview'; import { ITextAreaProps } from './components/TextArea'; import { ICommand } from './commands'; import { ContextStore, PreviewType } from './Context'; import './index.less'; export interface IProps { prefixCls?: string; className?: string; } export interface MDEditorProps extends Omit<React.HTMLAttributes<HTMLDivElement>, 'onChange'>, IProps { /** * The Markdown value. */ value?: string; /** * Event handler for the `onChange` event. */ onChange?: (value?: string, event?: React.ChangeEvent<HTMLTextAreaElement>, state?: ContextStore) => void; /** * editor height change listener */ onHeightChange?: (value?: CSSProperties['height'], oldValue?: CSSProperties['height'], state?: ContextStore) => void; /** * Can be used to make `Markdown Editor` focus itself on initialization. Defaults to on. * it will be set to true when either the source `textarea` is focused, * or it has an `autofocus` attribute and no other element is focused. */ autoFocus?: ITextAreaProps['autoFocus']; /** * The height of the editor. * ⚠️ `Dragbar` is invalid when **`height`** parameter percentage. */ height?: CSSProperties['height']; /** * Custom toolbar heigth * @default 29px * * @deprecated toolbar height adaptive: https://github.com/uiwjs/react-md-editor/issues/427 * */ toolbarHeight?: number; /** * Show drag and drop tool. Set the height of the editor. */ visibleDragbar?: boolean; /** * @deprecated use `visibleDragbar` */ visiableDragbar?: boolean; /** * Show markdown preview. */ preview?: PreviewType; /** * Full screen display editor. */ fullscreen?: boolean; /** * Disable `fullscreen` setting body styles */ overflow?: boolean; /** * Maximum drag height. `visibleDragbar=true` */ maxHeight?: number; /** * Minimum drag height. `visibleDragbar=true` */ minHeight?: number; /** * This is reset [react-markdown](https://github.com/rexxars/react-markdown) settings. */ previewOptions?: Omit<MarkdownPreviewProps, 'source'>; /** * Set the `textarea` related props. */ textareaProps?: ITextAreaProps; /** * Use div to replace TextArea or re-render TextArea * @deprecated Please use ~~`renderTextarea`~~ -> `components` */ renderTextarea?: ITextAreaProps['renderTextarea']; /** * re-render element */ components?: { /** Use div to replace TextArea or re-render TextArea */ textarea?: ITextAreaProps['renderTextarea']; /** * Override the default command element * _`toolbar`_ < _`command[].render`_ */ toolbar?: ICommand['render']; /** Custom markdown preview */ preview?: (source: string, state: ContextStore, dispath: React.Dispatch<ContextStore>) => JSX.Element; }; /** * Disable editing area code highlighting. The value is `false`, which increases the editing speed. * @default true */ highlightEnable?: boolean; /** * The number of characters to insert when pressing tab key. * Default `2` spaces. */ tabSize?: number; /** * If `false`, the `tab` key inserts a tab character into the textarea. If `true`, the `tab` key executes default behavior e.g. focus shifts to next element. */ defaultTabEnable?: boolean; /** * You can create your own commands or reuse existing commands. */ commands?: ICommand[]; /** * Filter or modify your commands. * https://github.com/uiwjs/react-md-editor/issues/296 */ commandsFilter?: (command: ICommand, isExtra: boolean) => false | ICommand; /** * You can create your own commands or reuse existing commands. */ extraCommands?: ICommand[]; /** * Hide the tool bar */ hideToolbar?: boolean; /** Whether to enable scrolling */ enableScroll?: boolean; /** Toolbar on bottom */ toolbarBottom?: boolean; } declare type Editor = React.FC<PropsWithRef<MDEditorProps>> & { Markdown: typeof MarkdownPreview; }; declare const mdEditor: Editor; export default mdEditor; ``` ## asdjk ### lskjdflskj as d s d

edbeeching/atari_2B_atari_yarsrevenge_1111

edbeeching

2022-10-21T00:01:51Z

7

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-21T00:00:47Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_yarsrevenge type: atari_yarsrevenge metrics: - type: mean_reward value: 224390.75 +/- 197367.31 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_yarsrevenge** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

edbeeching/atari_2B_atari_videopinball_1111

edbeeching

2022-10-20T23:54:10Z

6

0

sample-factory

[ "sample-factory", "tensorboard", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-20T23:52:57Z

--- library_name: sample-factory tags: - deep-reinforcement-learning - reinforcement-learning - sample-factory model-index: - name: APPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: atari_videopinball type: atari_videopinball metrics: - type: mean_reward value: 372372.91 +/- 274249.66 name: mean_reward verified: false --- A(n) **APPO** model trained on the **atari_videopinball** environment. This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory

salascorp/distilroberta-base-mrpc-glue-oscar-salas

salascorp

2022-10-20T22:48:41Z

4

0

transformers

[ "transformers", "pytorch", "roberta", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-20T01:44:30Z

--- license: apache-2.0 tags: - text-classification - generated_from_trainer datasets: - glue model-index: - name: distilroberta-base-mrpc-glue-oscar-salas results: [] ---  # distilroberta-base-mrpc-glue-oscar-salas This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the datasetX dataset. It achieves the following results on the evaluation set: - eval_loss: 0.6456 - eval_accuracy: 0.8260 - eval_f1: 0.8795 - eval_runtime: 30.3289 - eval_samples_per_second: 13.453 - eval_steps_per_second: 1.682 - epoch: 1.09 - step: 500 ## 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 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cpu - Datasets 2.6.1 - Tokenizers 0.13.1

huggingtweets/jiswooning-the3ammusician

huggingtweets

2022-10-20T22:27:14Z

4

0

transformers

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

text-generation

2022-10-20T22:26:00Z

--- language: en thumbnail: http://www.huggingtweets.com/jiswooning-the3ammusician/1666304830215/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/1560736534143422465/3oAu6oCD_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/1521185553382883334/fHjvh84L_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </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">TOR Kate & K8 misses KARD</div> <div style="text-align: center; font-size: 14px;">@jiswooning-the3ammusician</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 TOR Kate & K8 misses KARD. | Data | TOR Kate | K8 misses KARD | | --- | --- | --- | | Tweets downloaded | 3234 | 3193 | | Retweets | 1038 | 1194 | | Short tweets | 310 | 208 | | Tweets kept | 1886 | 1791 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1vcg0753/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 @jiswooning-the3ammusician's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1plbf2ii/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/jiswooning-the3ammusician') 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)

jayanta/cvt-13-384-22k-fv-finetuned-memes

jayanta

2022-10-20T22:05:58Z

42

0

transformers

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

image-classification

2022-10-20T21:40:10Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: cvt-13-384-22k-fv-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8315301391035549 - name: Precision type: precision value: 0.8302128280229624 - name: Recall type: recall value: 0.8315301391035549 - name: F1 type: f1 value: 0.8292026505769348 ---  # cvt-13-384-22k-fv-finetuned-memes This model is a fine-tuned version of [microsoft/cvt-13-384-22k](https://huggingface.co/microsoft/cvt-13-384-22k) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.5761 - Accuracy: 0.8315 - Precision: 0.8302 - Recall: 0.8315 - F1: 0.8292 ## 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: 0.00012 - 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.3821 | 0.99 | 20 | 1.2780 | 0.4969 | 0.5083 | 0.4969 | 0.4458 | | 1.0785 | 1.99 | 40 | 0.8633 | 0.6669 | 0.6658 | 0.6669 | 0.6500 | | 0.8862 | 2.99 | 60 | 0.7110 | 0.7218 | 0.7258 | 0.7218 | 0.7013 | | 0.665 | 3.99 | 80 | 0.5515 | 0.8045 | 0.8137 | 0.8045 | 0.8050 | | 0.6056 | 4.99 | 100 | 0.5956 | 0.7960 | 0.8041 | 0.7960 | 0.7846 | | 0.4779 | 5.99 | 120 | 0.6229 | 0.7937 | 0.7945 | 0.7937 | 0.7857 | | 0.4554 | 6.99 | 140 | 0.5355 | 0.8099 | 0.8126 | 0.8099 | 0.8086 | | 0.4249 | 7.99 | 160 | 0.5447 | 0.8269 | 0.8275 | 0.8269 | 0.8236 | | 0.4313 | 8.99 | 180 | 0.5530 | 0.8153 | 0.8140 | 0.8153 | 0.8132 | | 0.423 | 9.99 | 200 | 0.5346 | 0.8238 | 0.8230 | 0.8238 | 0.8223 | | 0.3997 | 10.99 | 220 | 0.5413 | 0.8338 | 0.8347 | 0.8338 | 0.8338 | | 0.4095 | 11.99 | 240 | 0.5999 | 0.8207 | 0.8231 | 0.8207 | 0.8177 | | 0.3979 | 12.99 | 260 | 0.5632 | 0.8284 | 0.8255 | 0.8284 | 0.8250 | | 0.3408 | 13.99 | 280 | 0.5725 | 0.8207 | 0.8198 | 0.8207 | 0.8196 | | 0.3828 | 14.99 | 300 | 0.5631 | 0.8277 | 0.8258 | 0.8277 | 0.8260 | | 0.3595 | 15.99 | 320 | 0.6005 | 0.8308 | 0.8297 | 0.8308 | 0.8275 | | 0.3789 | 16.99 | 340 | 0.5840 | 0.8300 | 0.8271 | 0.8300 | 0.8273 | | 0.3545 | 17.99 | 360 | 0.5983 | 0.8246 | 0.8226 | 0.8246 | 0.8222 | | 0.3472 | 18.99 | 380 | 0.5795 | 0.8416 | 0.8382 | 0.8416 | 0.8390 | | 0.355 | 19.99 | 400 | 0.5761 | 0.8315 | 0.8302 | 0.8315 | 0.8292 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1

imodels/gpt-neo-2.7B-titles

imodels

2022-10-20T21:17:47Z

5

0

transformers

[ "transformers", "pytorch", "gpt_neo", "text-generation", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-10-17T18:36:43Z

--- license: apache-2.0 widget: - text: "2021\n\n" --- Full code and details at https://github.com/csinva/gpt-paper-title-generator **Model** - finetunes starting from the[gpt-neo-2.7B checkpoint](https://huggingface.co/EleutherAI/gpt-neo-2.7B) - for training details see [the training script](https://github.com/csinva/gpt-paper-title-generator/blob/0157f26be9b0763b4ea6480e5b149fdb8dff4626/gptneo/02_finetune_hf.py) - inference - should prepend with a year and two newlines before querying for a title, e.g. `2022\n\n` ```python from transformers import AutoModelForCausalLM, pipeline, AutoTokenizer model = AutoModelForCausalLM.from_pretrained("csinva/gpt-neo-2.7B-titles") tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B") pipe = pipeline('text-generation', model=model, tokenizer=tokenizer) pipe('2022\n\n') ``` **Data** - all [papers on arXiv](https://www.kaggle.com/datasets/Cornell-University/arxiv) in the categories cs.AI, cs.LG, stat.ML - date cutoff: only finetuned on papers with dat on or before Apr 1, 2022 - random 5% of papers also excluded - this results in 98,388 papers for finetuning - during finetuning each paper title was given starting with the prompt `<year>\n\n <title>\n` (e.g. `2022\n\n Emb-GAM: an Interpretable and Efficient Predictor using Pre-trained Language Models\n`)

jayanta/swin-large-patch4-window7-224-fv-finetuned-memes

jayanta

2022-10-20T21:16:39Z

64

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

2022-10-20T19:49:36Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: swin-large-patch4-window7-224-fv-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8601236476043277 - name: Precision type: precision value: 0.8582306285016578 - name: Recall type: recall value: 0.8601236476043277 - name: F1 type: f1 value: 0.8582797853944862 ---  # swin-large-patch4-window7-224-fv-finetuned-memes This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6502 - Accuracy: 0.8601 - Precision: 0.8582 - Recall: 0.8601 - F1: 0.8583 ## 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: 0.00012 - 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.2077 | 0.99 | 20 | 0.9499 | 0.6461 | 0.6764 | 0.6461 | 0.5863 | | 0.5687 | 1.99 | 40 | 0.5365 | 0.7975 | 0.8018 | 0.7975 | 0.7924 | | 0.3607 | 2.99 | 60 | 0.4007 | 0.8423 | 0.8419 | 0.8423 | 0.8398 | | 0.203 | 3.99 | 80 | 0.3751 | 0.8509 | 0.8502 | 0.8509 | 0.8503 | | 0.1728 | 4.99 | 100 | 0.4168 | 0.8509 | 0.8519 | 0.8509 | 0.8506 | | 0.0963 | 5.99 | 120 | 0.4351 | 0.8586 | 0.8573 | 0.8586 | 0.8555 | | 0.0956 | 6.99 | 140 | 0.4415 | 0.8547 | 0.8542 | 0.8547 | 0.8541 | | 0.079 | 7.99 | 160 | 0.5312 | 0.8501 | 0.8475 | 0.8501 | 0.8459 | | 0.0635 | 8.99 | 180 | 0.5376 | 0.8601 | 0.8578 | 0.8601 | 0.8577 | | 0.0593 | 9.99 | 200 | 0.5060 | 0.8609 | 0.8615 | 0.8609 | 0.8604 | | 0.0656 | 10.99 | 220 | 0.4997 | 0.8617 | 0.8573 | 0.8617 | 0.8587 | | 0.0561 | 11.99 | 240 | 0.5430 | 0.8586 | 0.8604 | 0.8586 | 0.8589 | | 0.0523 | 12.99 | 260 | 0.5354 | 0.8624 | 0.8643 | 0.8624 | 0.8626 | | 0.0489 | 13.99 | 280 | 0.5539 | 0.8609 | 0.8572 | 0.8609 | 0.8577 | | 0.0487 | 14.99 | 300 | 0.5785 | 0.8609 | 0.8591 | 0.8609 | 0.8591 | | 0.0485 | 15.99 | 320 | 0.6186 | 0.8601 | 0.8578 | 0.8601 | 0.8573 | | 0.0518 | 16.99 | 340 | 0.6342 | 0.8624 | 0.8612 | 0.8624 | 0.8606 | | 0.0432 | 17.99 | 360 | 0.6302 | 0.8586 | 0.8598 | 0.8586 | 0.8580 | | 0.0469 | 18.99 | 380 | 0.6323 | 0.8617 | 0.8606 | 0.8617 | 0.8604 | | 0.0426 | 19.99 | 400 | 0.6502 | 0.8601 | 0.8582 | 0.8601 | 0.8583 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1

creditgrossepointe/creditgrossepointe

creditgrossepointe

2022-10-20T21:13:37Z

0

null

[ "region:us" ]

null

2022-10-20T21:12:54Z

We are a family-owned and operated Credit Repair company, founded in 2013. Our goal is to help you achieve financial success and reach your credit goals. Follow this [link](https://grossepointepark.asapcreditrepairusa.com/)

yuik/ppo-LunarLander-v2

yuik

2022-10-20T21:09:21Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-20T21:08:56Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 254.74 +/- 20.11 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

jinhybr/layoutlm-funsd-tf

jinhybr

2022-10-20T20:48:26Z

10

0

transformers

[ "transformers", "tf", "tensorboard", "layoutlm", "token-classification", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-20T20:10:28Z

--- tags: - generated_from_keras_callback model-index: - name: layoutlm-funsd-tf results: [] ---  # layoutlm-funsd-tf This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.2509 - Validation Loss: 0.6942 - Train Overall Precision: 0.7291 - Train Overall Recall: 0.7888 - Train Overall F1: 0.7578 - Train Overall Accuracy: 0.8067 - Epoch: 7 ## 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': 'AdamWeightDecay', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: mixed_float16 ### Training results | Train Loss | Validation Loss | Train Overall Precision | Train Overall Recall | Train Overall F1 | Train Overall Accuracy | Epoch | |:----------:|:---------------:|:-----------------------:|:--------------------:|:----------------:|:----------------------:|:-----:| | 1.6886 | 1.4100 | 0.2324 | 0.2313 | 0.2318 | 0.5009 | 0 | | 1.1702 | 0.8486 | 0.5971 | 0.6618 | 0.6278 | 0.7338 | 1 | | 0.7521 | 0.7032 | 0.6561 | 0.7341 | 0.6929 | 0.7687 | 2 | | 0.5727 | 0.6268 | 0.6736 | 0.7662 | 0.7169 | 0.7957 | 3 | | 0.4586 | 0.6322 | 0.6909 | 0.7772 | 0.7315 | 0.7999 | 4 | | 0.3725 | 0.6378 | 0.7134 | 0.7782 | 0.7444 | 0.8096 | 5 | | 0.2987 | 0.6835 | 0.7270 | 0.7777 | 0.7515 | 0.8056 | 6 | | 0.2509 | 0.6942 | 0.7291 | 0.7888 | 0.7578 | 0.8067 | 7 | ### Framework versions - Transformers 4.23.1 - TensorFlow 2.6.0 - Datasets 2.6.1 - Tokenizers 0.13.1

asapcreditcolumbus/asapcreditrepaircolumbus

asapcreditcolumbus

2022-10-20T20:36:15Z

0

null

[ "region:us" ]

null

2022-10-20T20:35:14Z

Are you looking for [credit repair in Columbus](https://columbus.asapcreditrepairusa.com/)? You are at the right place. We’re not your average credit repair firm, we truly care, so we only charge for the items we pursue on your report. Not only does this make us one of the FASTEST credit restoration companies, but we’re also one of the most affordable.

Shaier/longformer_quail

Shaier

2022-10-20T19:58:53Z

4

0

transformers

[ "transformers", "pytorch", "longformer", "multiple-choice", "generated_from_trainer", "dataset:quail", "endpoints_compatible", "region:us" ]

multiple-choice

2022-10-20T15:42:17Z

--- tags: - generated_from_trainer datasets: - quail model-index: - name: longformer_quail results: [] ---  # longformer_quail This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the quail dataset. It achieves the following results on the evaluation set: - eval_loss: 1.9568 - eval_accuracy: 0.5791 - eval_runtime: 44.254 - eval_samples_per_second: 12.564 - eval_steps_per_second: 6.282 - epoch: 4.0 - step: 816 ## 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: 2 - eval_batch_size: 2 - seed: 42 - gradient_accumulation_steps: 25 - total_train_batch_size: 50 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1 - Datasets 2.5.1 - Tokenizers 0.11.0

allenai/drug_combinations_lm_pubmedbert

allenai

2022-10-20T18:25:13Z

39

2

transformers

[ "transformers", "pytorch", "bert", "fill-mask", "biomedical", "bioNLP", "en", "arxiv:2205.02289", "arxiv:2007.15779", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-10-19T11:25:49Z

--- language: - en tags: - biomedical - bioNLP --- This is a version of [PubmedBERT](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext?text=%5BMASK%5D+is+a+tumor+suppressor+gene.) which has been domain-adapted (via additional pretraining) to a set of PubMed abstracts that likely discuss multiple-drug therapies. This model was the strongest contextualized encoder in the experiments in the paper ["A Dataset for N-ary Relation Extraction of Drug Combinations"](https://arxiv.org/abs/2205.02289), when used as a component of a larger relation classification model (also hosted [here on Huggingface](https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt)). If you use this model, cite both ```latex @misc{pubmedbert, author = {Yu Gu and Robert Tinn and Hao Cheng and Michael Lucas and Naoto Usuyama and Xiaodong Liu and Tristan Naumann and Jianfeng Gao and Hoifung Poon}, title = {Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing}, year = {2020}, eprint = {arXiv:2007.15779}, } ``` and ```latex @inproceedings{Tiktinsky2022ADF, title = "A Dataset for N-ary Relation Extraction of Drug Combinations", author = "Tiktinsky, Aryeh and Viswanathan, Vijay and Niezni, Danna and Meron Azagury, Dana and Shamay, Yosi and Taub-Tabib, Hillel and Hope, Tom and Goldberg, Yoav", booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies", month = jul, year = "2022", address = "Seattle, United States", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.naacl-main.233", doi = "10.18653/v1/2022.naacl-main.233", pages = "3190--3203", } ```

allenai/drug-combo-classifier-pubmedbert-dapt

allenai

2022-10-20T18:23:30Z

23

5

transformers

[ "transformers", "pytorch", "bert", "en", "arxiv:2205.02289", "license:mit", "endpoints_compatible", "region:us" ]

null

2022-05-04T03:20:11Z

--- language: en license: mit --- This is the baseline model used in most experiments in the paper ["A Dataset for N-ary Relation Extraction of Drug Combinations"](https://arxiv.org/abs/2205.02289). *(for just the domain-adapted masked language model that we use underneath this model, see [here](https://huggingface.co/allenai/drug_combinations_lm_pubmedbert?text=Paxlovid+works+well+in+combination+with+%5BMASK%5D+for+treating+breast+cancer.))* **Steps to load this model** 1) Download accompanying code: ``` git clone https://github.com/allenai/drug-combo-extraction.git conda create --name drug_combo python=3.8.5 conda activate drug_combo ``` 2) Download model from Huggingface: ``` git lfs install git clone https://huggingface.co/allenai/drug-combo-classifier-pubmedbert-dapt ``` 3) Load model (`in Python`): ``` from modeling.model import load_model checkpoint_path = "drug-combo-classifier-pubmedbert-dapt" model, tokenizer, metadata = load_model(checkpoint_path) ```

jxm/u-PMLM-R

jxm

2022-10-20T18:05:26Z

5

2

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2004.11579", "endpoints_compatible", "region:us" ]

feature-extraction

2022-06-01T16:08:29Z

PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from [the authors' original implementation](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PMLM).

jxm/u-PMLM-A

jxm

2022-10-20T18:05:03Z

5

0

transformers

[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2004.11579", "endpoints_compatible", "region:us" ]

feature-extraction

2022-06-01T17:37:45Z

PMLM is the language model described in [Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-A" variant, adapted from [the authors' original implementation](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PMLM).

mprzibilla/super_large_finetune_M01

mprzibilla

2022-10-20T17:56:53Z

5

0

transformers

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

automatic-speech-recognition

2022-10-19T12:05:24Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: super_large_finetune_M01 results: [] ---  # super_large_finetune_M01 This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9906 - Wer: 1.0 ## 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: 0.0001 - train_batch_size: 20 - 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: 35440 - num_epochs: 200 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:------:|:---------------:|:---:| | 10.0626 | 20.0 | 70880 | 3.0307 | 1.0 | | 2.5319 | 40.0 | 141760 | 3.0316 | 1.0 | | 2.4978 | 60.0 | 212640 | 3.0123 | 1.0 | | 2.4849 | 80.0 | 283520 | 2.9923 | 1.0 | | 2.4776 | 100.0 | 354400 | 3.0092 | 1.0 | | 2.4733 | 120.0 | 425280 | 2.9964 | 1.0 | | 2.4702 | 140.0 | 496160 | 2.9968 | 1.0 | | 2.4686 | 160.0 | 567040 | 2.9937 | 1.0 | | 2.4669 | 180.0 | 637920 | 2.9908 | 1.0 | | 2.4661 | 200.0 | 708800 | 2.9906 | 1.0 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

rbroc/contrastive-user-encoder-singlepost

rbroc

2022-10-20T16:56:21Z

4

0

transformers

[ "transformers", "pytorch", "distilbert", "feature-extraction", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]

feature-extraction

2022-10-19T08:56:38Z

--- language: - en license: apache-2.0 library_name: transformers --- ### Contrastive user encoder (single post) This model is a `DistilBertModel` trained by fine-tuning `distilbert-base-uncased` on author-based triplet loss. #### Details Training and evaluation details are provided in our EMNLP Findings paper: - Rocca, R., & Yarkoni, T. (2022), Language as a fingerprint: Self-supervised learning of user encodings using transformers, to appear in *Findings of the Association for Computational Linguistics: EMNLP 2022* #### Training We fine-tuned DistilBERT on triplets consisting of: - a single Reddit submission from a given user (the "anchor") - see ```rbroc/contrastive-user-encoder-multipost``` for a model trained on aggregated embeddings of multiple anchors; - an additional post from the same user (a "positive example"); - a post from a different, randomly selected user (the "negative example") To compute the loss, we use [CLS] encoding of the anchor, positive example and negative example from the last layer of the DistilBERT encoder. We optimize for \$max(||f(a) - f(n)|| - ||f(a) - f(p)|| + \alpha,0)\$ where: - \$ f(a)\$ is the [CLS] encoding of the anchor; - \$ f(n) \$ is the [CLS] encoding of the negative example; - \$ f(p) \$ is the [CLS] encoding of the positive example; - \$ \alpha \$ is a tunable parameter called margin. Here, we tuned this to \$ \alpha = 1.0\$ #### Evaluation and usage The model yields performance advantages downstream user-based classification tasks. We encourage usage and benchmarking on tasks involving: - prediction of user traits (e.g., personality); - extraction of user-aware text encodings (e.g., style modeling); - contextualized text modeling, where standard text representations are complemented with compact user representations #### Limitations Being exclusively trained on Reddit data, our models probably overfit to linguistic markers and traits which are relevant to characterizing the Reddit user population, but less salient in the general population. Domain-specific fine-tuning may be required before deployment. Furthermore, our self-supervised approach enforces little or no control over biases, which models may actively use as part of their heuristics in contrastive and downstream tasks.

tringuyexn/ppo-LunarLander-v2

tringuyexn

2022-10-20T16:55:51Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-20T16:55:27Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 237.09 +/- 23.08 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

north/t5_base_scand3M

north

2022-10-20T16:16:52Z

4

0

transformers

[ "transformers", "pytorch", "jax", "tensorboard", "t5", "text2text-generation", "no", "nn", "sv", "da", "is", "en", "dataset:nbailab/NCC", "dataset:mc4", "dataset:wikipedia", "arxiv:2104.09617", "arxiv:1910.10683", "license:other", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-13T09:02:03Z

--- language: - no - nn - sv - da - is - en datasets: - nbailab/NCC - mc4 - wikipedia widget: - text: <extra_id_0> hver uke samles Regjeringens medlemmer til Statsråd på <extra_id_1>. Dette organet er øverste <extra_id_2> i Norge. For at møtet skal være <extra_id_3>, må over halvparten av regjeringens <extra_id_4> være til stede. - text: På <extra_id_0> kan man <extra_id_1> en bok, og man kan også <extra_id_2> seg ned og lese den. license: other --- The North-T5-models are a set of Norwegian and Scandinavian sequence-to-sequence-models. It builds upon the flexible [T5](https://github.com/google-research/text-to-text-transfer-transformer) and [T5X](https://github.com/google-research/t5x) and can be used for a variety of NLP tasks ranging from classification to translation. | |**Small** <br />_60M_|**Base** <br />_220M_|**Large** <br />_770M_|**XL** <br />_3B_|**XXL** <br />_11B_| |:-----------|:------------:|:------------:|:------------:|:------------:|:------------:| |North-T5‑NCC|[🤗](https://huggingface.co/north/t5_small_NCC)|[🤗](https://huggingface.co/north/t5_base_NCC)|[🤗](https://huggingface.co/north/t5_large_NCC)|[🤗](https://huggingface.co/north/t5_xl_NCC)|[🤗](https://huggingface.co/north/t5_xxl_NCC)|| |North-T5‑NCC‑lm|[🤗](https://huggingface.co/north/t5_small_NCC_lm)|[🤗](https://huggingface.co/north/t5_base_NCC_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_lm)|[🤗](https://huggingface.co/north/t5_xxl_NCC_lm)|| |North-T5‑NCC‑modern|[🤗](https://huggingface.co/north/t5_small_NCC_modern)|[🤗](https://huggingface.co/north/t5_base_NCC_modern)|[🤗](https://huggingface.co/north/t5_large_NCC_modern)|[🤗](https://huggingface.co/north/t5_xl_NCC_modern)|| |North-T5‑NCC‑modern‑lm|[🤗](https://huggingface.co/north/t5_small_NCC_modern_lm)|[🤗](https://huggingface.co/north/t5_base_NCC_modern_lm)|[🤗](https://huggingface.co/north/t5_large_NCC_modern_lm)|[🤗](https://huggingface.co/north/t5_xl_NCC_modern_lm)|| |North-T5‑NCC‑scand|[🤗](https://huggingface.co/north/t5_small_NCC_scand)|[🤗](https://huggingface.co/north/t5_base_NCC_scand)|[🤗](https://huggingface.co/north/t5_large_NCC_scand)|[🤗](https://huggingface.co/north/t5_xl_NCC_scand)|| |North-T5‑scand|[🤗](https://huggingface.co/north/t5_small_scand)|[🤗](https://huggingface.co/north/t5_base_scand)|[🤗](https://huggingface.co/north/t5_large_scand)|| |North-byT5‑NCC|[🤗](https://huggingface.co/north/byt5_small_NCC)|[🤗](https://huggingface.co/north/byt5_base_NCC)|[🤗](https://huggingface.co/north/byt5_large_NCC)|| |North-T5‑scand3M|✔|[🤗](https://huggingface.co/north/t5_large_scand3M)|[🤗](https://huggingface.co/north/t5_xl_scand3M)|| ## T5X Checkpoint The original T5X checkpoint is also available for this model in the [Google Cloud Bucket](gs://north-t5x/pretrained_models/base/scandinavian3k_t5x_base/). ## Performance A thorough evaluation of the North-T5 models is planned, and I strongly recommend external researchers to make their own evaluation. The main advantage with the T5-models are their flexibility. Traditionally, encoder-only models (like BERT) excels in classification tasks, while seq-2-seq models are easier to train for tasks like translation and Q&A. Despite this, here are the results from using North-T5 on the political classification task explained [here](https://arxiv.org/abs/2104.09617). |**Model:** | **F1** | |:-----------|:------------| |mT5-base|73.2 | |mBERT-base|78.4 | |NorBERT-base|78.2 | |North-T5-small|80.5 | |nb-bert-base|81.8 | |North-T5-base|85.3 | |North-T5-large|86.7 | |North-T5-xl|88.7 | |North-T5-xxl|91.8| These are preliminary results. The [results](https://arxiv.org/abs/2104.09617) from the BERT-models are based on the test-results from the best model after 10 runs with early stopping and a decaying learning rate. The T5-results are the average of five runs on the evaluation set. The small-model was trained for 10.000 steps, while the rest for 5.000 steps. A fixed learning rate was used (no decay), and no early stopping. Neither was the recommended rank classification used. We use a max sequence length of 512. This method simplifies the test setup and gives results that are easy to interpret. However, the results from the T5 model might actually be a bit sub-optimal. ## Sub-versions of North-T5 The following sub-versions are available. More versions will be available shorter. |**Model** | **Description** | |:-----------|:-------| |**North‑T5‑NCC** |This is the main version. It is trained an additonal 500.000 steps on from the mT5 checkpoint. The training corpus is based on [the Norwegian Colossal Corpus (NCC)](https://huggingface.co/datasets/NbAiLab/NCC). In addition there are added data from MC4 and English Wikipedia.| |**North‑T5‑NCC‑lm**|The model is pretrained for an addtional 100k steps on the LM objective discussed in the [T5 paper](https://arxiv.org/pdf/1910.10683.pdf). In a way this turns a masked language model into an autoregressive model. It also prepares the model for some tasks. When for instance doing translation and NLI, it is well documented that there is a clear benefit to do a step of unsupervised LM-training before starting the finetuning.| |**North‑T5‑NCC‑modern**| The model is pretrained for an additional 200k steps on a blanaced Bokmål and Nynorsk corpus. While this was originally done for doing translation between Bokmål and Nynorsk, it might also give improved results on tasks where you know that the input/output is modern "standard" text. A significant part of the training corpus is newspapers and reports.| |**North‑T5‑NCC‑modern‑lm**| Trained as above but with an additional 100k "language model"-pretraining.| |**North‑T5‑NCC‑scand**|The model is pretrained for an additional 200k steps on a Scandinavian corpus (Bokmål, Nynorsk, Danish, Swedish and Icelandic (+ a tiny bit Faeroyish)). The model was trained for increasing the understanding of what effect such training has on various languages.| |**North‑T5‑scand**|Pretrained for 1,700,000 steps starting with the mT5 checkpoing. The purpose of the mode is studying the difference of different training regimes for Scandinavian language model.| |**North‑byT5‑base**| This is a vocabulary free version of T5. It is trained exactly like North-T5, but instead of the 250,112 vocabulary, this model operates directly on the raw text. The model architecture might be of particulary interest for tasks involving for instance spelling correction, OCR-cleaning, handwriting recognition etc. However, it will - by design - have amuch shorter maximum sequence length.| ## Fine-tuned versions As explained below, the model really needs to be fine-tuned for specific tasks. This procedure is relatively simple, and the models are not very sensitive to the hyper-parameters used. Usually a decent result can be obtained by using a fixed learning rate of 1e-3. Smaller versions of the model typically needs to be trained for a longer time. It is easy to train the base-models in a Google Colab. Since some people really want to see what the models are capable of, without going through the training procedure, I provide a couple of test models. These models are by no means optimised, and are just for demonstrating how the North-T5 models can be used. * Nynorsk Translator. Translates any text from Norwegian Bokmål to Norwegian Nynorsk. Please test the [Streamlit-demo](https://huggingface.co/spaces/north/Nynorsk) and the [HuggingFace repo](https://huggingface.co/north/demo-nynorsk-base) * DeUnCaser. The model adds punctation, spaces and capitalisation back into the text. The input needs to be in Norwegian but does not have to be divided into sentences or have proper capitalisation of words. You can even remove the spaces from the text, and make the model reconstruct it. It can be tested with the [Streamlit-demo](https://huggingface.co/spaces/north/DeUnCaser) and directly on the [HuggingFace repo](https://huggingface.co/north/demo-deuncaser-base) ## Training details All models are built using the Flax-based T5X codebase, and all models are initiated with the mT5 pretrained weights. The models are trained using the T5.1.1 training regime, where they are only trained on an unsupervised masking-task. This also means that the models (contrary to the original T5) needs to be finetuned to solve specific tasks. This finetuning is however usually not very compute intensive, and in most cases it can be performed even with free online training resources. All the main model model versions are trained for 500.000 steps after the mT5 checkpoint (1.000.000 steps). They are trained mainly on a 75GB corpus, consisting of NCC, Common Crawl and some additional high quality English text (Wikipedia). The corpus is roughly 80% Norwegian text. Additional languages are added to retain some of the multilingual capabilities, making the model both more robust to new words/concepts and also more suited as a basis for translation tasks. While the huge models almost always will give the best results, they are also both more difficult and more expensive to finetune. I will strongly recommended to start with finetuning a base-models. The base-models can easily be finetuned on a standard graphic card or a free TPU through Google Colab. All models were trained on TPUs. The largest XXL model was trained on a TPU v4-64, the XL model on a TPU v4-32, the Large model on a TPU v4-16 and the rest on TPU v4-8. Since it is possible to reduce the batch size during fine-tuning, it is also possible to finetune on slightly smaller hardware. The rule of thumb is that you can go "one step down" when finetuning. The large models still rewuire access to significant hardware, even for finetuning. ## Formats All models are trained using the Flax-based T5X library. The original checkpoints are available in T5X format and can be used for both finetuning or interference. All models, except the XXL-model, are also converted to Transformers/HuggingFace. In this framework, the models can be loaded for finetuning or inference both in Flax, PyTorch and TensorFlow format. ## Future I will continue to train and release additional models to this set. What models that are added is dependent upon the feedbacki from the users ## Thanks This release would not have been possible without getting support and hardware from the [TPU Research Cloud](https://sites.research.google/trc/about/) at Google Research. Both the TPU Research Cloud Team and the T5X Team has provided extremely useful support for getting this running. Freddy Wetjen at the National Library of Norway has been of tremendous help in generating the original NCC corpus, and has also contributed to generate the collated coprus used for this training. In addition he has been a dicussion partner in the creation of these models. Also thanks to Stefan Schweter for writing the [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/t5/convert_t5x_checkpoint_to_flax.py) for converting these models from T5X to HuggingFace and to Javier de la Rosa for writing the dataloader for reading the HuggingFace Datasets in T5X. ## Warranty Use at your own risk. The models have not yet been thougroughly tested, and may contain both errors and biases. ## Contact/About These models were trained by Per E Kummervold. Please contact me on [email protected].

amanneo/mail-generator-mini-v2

amanneo

2022-10-20T14:49:33Z

3

0

transformers

[ "transformers", "tf", "gpt2", "text-generation", "generated_from_keras_callback", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2022-10-20T13:12:41Z

--- tags: - generated_from_keras_callback model-index: - name: amanneo/mail-generator-mini-v2 results: [] ---  # amanneo/mail-generator-mini-v2 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 0.5212 - Train Accuracy: 0.0027 - Validation Loss: 5.5781 - Validation Accuracy: 0.0 - Epoch: 99 ## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 5e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': -994, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results | Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch | |:----------:|:--------------:|:---------------:|:-------------------:|:-----:| | 2.5928 | 0.0171 | 5.5430 | 0.0048 | 0 | | 2.6003 | 0.0207 | 5.5430 | 0.0048 | 1 | | 2.5954 | 0.0171 | 5.5508 | 0.0048 | 2 | | 2.5775 | 0.0190 | 5.5508 | 0.0024 | 3 | | 2.5758 | 0.0231 | 5.5508 | 0.0024 | 4 | | 2.5742 | 0.0207 | 5.5586 | 0.0048 | 5 | | 2.5547 | 0.0209 | 5.5586 | 0.0048 | 6 | | 2.5566 | 0.0188 | 5.5586 | 0.0048 | 7 | | 2.5391 | 0.0193 | 5.5586 | 0.0048 | 8 | | 2.5378 | 0.0215 | 5.5508 | 0.0048 | 9 | | 2.5238 | 0.0188 | 5.5469 | 0.0048 | 10 | | 2.5150 | 0.0160 | 5.5508 | 0.0048 | 11 | | 2.4967 | 0.0174 | 5.5508 | 0.0071 | 12 | | 2.4691 | 0.0193 | 5.5430 | 0.0071 | 13 | | 2.4626 | 0.0163 | 5.5430 | 0.0071 | 14 | | 2.4417 | 0.0231 | 5.5352 | 0.0048 | 15 | | 2.4323 | 0.0215 | 5.5352 | 0.0048 | 16 | | 2.4193 | 0.0226 | 5.5469 | 0.0048 | 17 | | 2.4170 | 0.0185 | 5.5469 | 0.0048 | 18 | | 2.3743 | 0.0193 | 5.5312 | 0.0048 | 19 | | 2.3730 | 0.0207 | 5.5312 | 0.0048 | 20 | | 2.3535 | 0.0198 | 5.5312 | 0.0048 | 21 | | 2.3372 | 0.0182 | 5.5312 | 0.0071 | 22 | | 2.3324 | 0.0177 | 5.5312 | 0.0048 | 23 | | 2.3011 | 0.0204 | 5.5195 | 0.0048 | 24 | | 2.2650 | 0.0212 | 5.5117 | 0.0048 | 25 | | 2.2568 | 0.0198 | 5.5078 | 0.0048 | 26 | | 2.2331 | 0.0196 | 5.5156 | 0.0048 | 27 | | 2.2021 | 0.0193 | 5.5078 | 0.0048 | 28 | | 2.1807 | 0.0204 | 5.5039 | 0.0048 | 29 | | 2.1691 | 0.0190 | 5.5 | 0.0 | 30 | | 2.1463 | 0.0174 | 5.4766 | 0.0 | 31 | | 2.1097 | 0.0196 | 5.4844 | 0.0 | 32 | | 2.1014 | 0.0179 | 5.4844 | 0.0024 | 33 | | 2.0833 | 0.0177 | 5.4844 | 0.0024 | 34 | | 2.0423 | 0.0201 | 5.4844 | 0.0 | 35 | | 2.0163 | 0.0198 | 5.4844 | 0.0 | 36 | | 1.9909 | 0.0168 | 5.4883 | 0.0 | 37 | | 1.9774 | 0.0207 | 5.4805 | 0.0 | 38 | | 1.9414 | 0.0207 | 5.4844 | 0.0 | 39 | | 1.9206 | 0.0215 | 5.4766 | 0.0 | 40 | | 1.8849 | 0.0182 | 5.4805 | 0.0 | 41 | | 1.8732 | 0.0193 | 5.4648 | 0.0 | 42 | | 1.8460 | 0.0160 | 5.4609 | 0.0 | 43 | | 1.8171 | 0.0168 | 5.4648 | 0.0 | 44 | | 1.7791 | 0.0201 | 5.4531 | 0.0 | 45 | | 1.7583 | 0.0158 | 5.4570 | 0.0 | 46 | | 1.7360 | 0.0171 | 5.4570 | 0.0 | 47 | | 1.7061 | 0.0120 | 5.4297 | 0.0 | 48 | | 1.6802 | 0.0155 | 5.4258 | 0.0 | 49 | | 1.6551 | 0.0182 | 5.4141 | 0.0 | 50 | | 1.6289 | 0.0130 | 5.4219 | 0.0 | 51 | | 1.5981 | 0.0130 | 5.3945 | 0.0 | 52 | | 1.5656 | 0.0128 | 5.4297 | 0.0 | 53 | | 1.5535 | 0.0168 | 5.4219 | 0.0 | 54 | | 1.5184 | 0.0141 | 5.4102 | 0.0 | 55 | | 1.4943 | 0.0149 | 5.4023 | 0.0 | 56 | | 1.4616 | 0.0122 | 5.4062 | 0.0 | 57 | | 1.4344 | 0.0111 | 5.4062 | 0.0 | 58 | | 1.3965 | 0.0111 | 5.4141 | 0.0 | 59 | | 1.3643 | 0.0122 | 5.4375 | 0.0 | 60 | | 1.3309 | 0.0087 | 5.4453 | 0.0 | 61 | | 1.3215 | 0.0090 | 5.4648 | 0.0 | 62 | | 1.3058 | 0.0084 | 5.4727 | 0.0 | 63 | | 1.2700 | 0.0109 | 5.4453 | 0.0 | 64 | | 1.2396 | 0.0079 | 5.4609 | 0.0 | 65 | | 1.2189 | 0.0092 | 5.4375 | 0.0 | 66 | | 1.1855 | 0.0079 | 5.4375 | 0.0 | 67 | | 1.1592 | 0.0073 | 5.4375 | 0.0 | 68 | | 1.1219 | 0.0071 | 5.4648 | 0.0 | 69 | | 1.1071 | 0.0065 | 5.4570 | 0.0 | 70 | | 1.0848 | 0.0060 | 5.4375 | 0.0 | 71 | | 1.0581 | 0.0076 | 5.4453 | 0.0 | 72 | | 1.0316 | 0.0090 | 5.4570 | 0.0 | 73 | | 1.0068 | 0.0063 | 5.4219 | 0.0 | 74 | | 0.9832 | 0.0060 | 5.4570 | 0.0 | 75 | | 0.9534 | 0.0046 | 5.4570 | 0.0 | 76 | | 0.9378 | 0.0057 | 5.4648 | 0.0 | 77 | | 0.9170 | 0.0033 | 5.4844 | 0.0 | 78 | | 0.8941 | 0.0041 | 5.4883 | 0.0 | 79 | | 0.8666 | 0.0030 | 5.4922 | 0.0 | 80 | | 0.8419 | 0.0054 | 5.4375 | 0.0 | 81 | | 0.8200 | 0.0035 | 5.4492 | 0.0 | 82 | | 0.8020 | 0.0022 | 5.4648 | 0.0 | 83 | | 0.7785 | 0.0057 | 5.4883 | 0.0 | 84 | | 0.7607 | 0.0052 | 5.4648 | 0.0 | 85 | | 0.7454 | 0.0041 | 5.5078 | 0.0 | 86 | | 0.7208 | 0.0024 | 5.5078 | 0.0 | 87 | | 0.7040 | 0.0027 | 5.5078 | 0.0 | 88 | | 0.6799 | 0.0041 | 5.5156 | 0.0 | 89 | | 0.6594 | 0.0030 | 5.5312 | 0.0 | 90 | | 0.6397 | 0.0030 | 5.5312 | 0.0 | 91 | | 0.6217 | 0.0030 | 5.5195 | 0.0 | 92 | | 0.6112 | 0.0033 | 5.5195 | 0.0 | 93 | | 0.5937 | 0.0046 | 5.5625 | 0.0 | 94 | | 0.5745 | 0.0035 | 5.5625 | 0.0 | 95 | | 0.5616 | 0.0027 | 5.5586 | 0.0 | 96 | | 0.5468 | 0.0043 | 5.5742 | 0.0 | 97 | | 0.5354 | 0.0027 | 5.5781 | 0.0 | 98 | | 0.5212 | 0.0027 | 5.5781 | 0.0 | 99 | ### Framework versions - Transformers 4.23.1 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.1

Mattbrenr/What

Mattbrenr

2022-10-20T14:07:37Z

0

null

[ "license:creativeml-openrail-m", "region:us" ]

null

2022-10-20T14:07:37Z

--- license: creativeml-openrail-m ---

auriolar/Reinformce-Pong-PLE-v0

auriolar

2022-10-20T14:07:27Z

0

null

[ "Pong-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2022-10-20T14:07:14Z

--- tags: - Pong-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinformce-Pong-PLE-v0 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pong-PLE-v0 type: Pong-PLE-v0 metrics: - type: mean_reward value: -16.00 +/- 0.00 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pong-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pong-PLE-v0** . To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5

jeonsworld/ddpm-butterflies-128

jeonsworld

2022-10-20T13:56:19Z

3

0

diffusers

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

null

2022-10-20T12:40:13Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: huggan/smithsonian_butterflies_subset metrics: [] ---  # ddpm-butterflies-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `huggan/smithsonian_butterflies_subset` 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: 16 - eval_batch_size: 16 - 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/jeonsworld/ddpm-butterflies-128/tensorboard?#scalars)

lewtun/quantized-distilbert-banking77

lewtun

2022-10-20T12:47:39Z

13

0

transformers

[ "transformers", "onnx", "text-classification", "optimum", "dataset:banking77", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-06-08T09:42:56Z

--- tags: - optimum datasets: - banking77 metrics: - accuracy model-index: - name: quantized-distilbert-banking77 results: - task: name: Text Classification type: text-classification dataset: name: banking77 type: banking77 metrics: - name: Accuracy type: accuracy value: 0.9244 --- # Quantized-distilbert-banking77 This model is a dynamically quantized version of [optimum/distilbert-base-uncased-finetuned-banking77](https://huggingface.co/optimum/distilbert-base-uncased-finetuned-banking77) on the `banking77` dataset. The model was created using the [dynamic-quantization](https://github.com/huggingface/workshops/tree/main/mlops-world) notebook from a workshop presented at MLOps World 2022. It achieves the following results on the evaluation set: **Accuracy** - Vanilla model: 92.5% - Quantized model: 92.44% > The quantized model achieves 99.93% accuracy of the FP32 model **Latency** Payload sequence length: 128 Instance type: AWS c6i.xlarge | latency | vanilla transformers | quantized optimum model | improvement | |---------|----------------------|-------------------------|-------------| | p95 | 63.24ms | 37.06ms | 1.71x | | avg | 62.87ms | 37.93ms | 1.66x | ## How to use ```python from optimum.onnxruntime import ORTModelForSequenceClassification from transformers import pipeline, AutoTokenizer model = ORTModelForSequenceClassification.from_pretrained("lewtun/quantized-distilbert-banking77") tokenizer = AutoTokenizer.from_pretrained("lewtun/quantized-distilbert-banking77") classifier = pipeline("text-classification", model=model, tokenizer=tokenizer) classifier("What is the exchange rate like on this app?") ```

ChaosW/autohome-deberta-v2-xlarge-base

ChaosW

2022-10-20T12:21:06Z

3

0

transformers

[ "transformers", "pytorch", "deberta-v2", "fill-mask", "bert", "zh", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

fill-mask

2022-10-20T12:19:19Z

--- language: - zh license: apache-2.0 tags: - bert inference: true widget: - text: "生活的真谛是[MASK]。" --- # Erlangshen-Deberta-97M-Chinese，one model of [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM). The 97 million parameter deberta-V2 base model, using 180G Chinese data, 24 A100(40G) training for 7 days，which is a encoder-only transformer structure. Consumed totally 1B samples. ## Task Description Erlangshen-Deberta-97M-Chinese is pre-trained by bert like mask task from Deberta [paper](https://readpaper.com/paper/3033187248) ## Usage ```python from transformers import AutoModelForMaskedLM, AutoTokenizer, FillMaskPipeline import torch tokenizer=AutoTokenizer.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese', use_fast=False) model=AutoModelForMaskedLM.from_pretrained('IDEA-CCNL/Erlangshen-DeBERTa-v2-97M-Chinese') text = '生活的真谛是[MASK]。' fillmask_pipe = FillMaskPipeline(model, tokenizer, device=7) print(fillmask_pipe(text, top_k=10)) ``` ## Finetune We present the dev results on some tasks. | Model | OCNLI | CMNLI | | ---------------------------------- | ----- | ------ | | RoBERTa-base | 0.743 | 0.7973 | | **Erlangshen-Deberta-97M-Chinese** | 0.752 | 0.807 | ## Citation If you find the resource is useful, please cite the following website in your paper. ``` @misc{Fengshenbang-LM, title={Fengshenbang-LM}, author={IDEA-CCNL}, year={2022}, howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}}, } ```

knkarthick/Action_Items

knkarthick

2022-10-20T12:10:12Z

75

7

transformers

[ "transformers", "pytorch", "distilbert", "text-classification", "seq2seq", "en", "dataset:Custom", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-20T10:45:18Z

--- language: en tags: - distilbert - seq2seq - text-classification license: apache-2.0 datasets: - Custom metrics: - Accuracy - Precision - Recall widget: - text: |- Let's start the project as soon as possible as we are running out of deadline. model-index: - name: Action_Items results: - task: name: Action Item Classification type: text-classification dataset: name: Custom type: custom metrics: - name: Validation Accuracy type: accuracy value: - name: Validation Precision type: precision value: - name: Validation Recall type: recall value: - name: Test Accuracy type: accuracy value: - name: Test Precision type: precision value: - name: Test Recall type: recall value: --- Model obtained by Fine Tuning 'distilbert' using Custom Dataset! LABEL_0 - Not an Action Item LABEL_1 - Action Item ## Usage # Example 1 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' Customer portion will have the dependency of , you know , fifty five probably has to be on XGEVA before we can start that track , but we can at least start the enablement track for sales and CSM who are as important as customers because they're the top of our funnel , especially sales. ''' summarizer(text) ``` # Example 2 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' India, officially the Republic of India, is a country in South Asia. ''' summarizer(text) ``` # Example 3 ```python from transformers import pipeline summarizer = pipeline("text-classification", model="knkarthick/Action_Items") text = ''' We have been running the business successfully for over a decade now. ''' summarizer(text) ```

bthomas/article2keyword2.1b_barthez-orangesum-title_finetuned16_for_mlm

bthomas

2022-10-20T12:04:52Z

7

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "mlm", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-20T09:46:19Z

--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.1b_barthez-orangesum-title_finetuned16_for_mlm results: [] ---  # article2keyword2.1b_barthez-orangesum-title_finetuned16_for_mlm This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0525 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 0.2976 | 1.0 | 1353 | 0.0543 | | 0.0566 | 2.0 | 2706 | 0.0509 | | 0.0487 | 3.0 | 4059 | 0.0458 | | 0.0433 | 4.0 | 5412 | 0.0456 | | 0.04 | 5.0 | 6765 | 0.0460 | | 0.0373 | 6.0 | 8118 | 0.0454 | | 0.0355 | 7.0 | 9471 | 0.0465 | | 0.0328 | 8.0 | 10824 | 0.0474 | | 0.0317 | 9.0 | 12177 | 0.0470 | | 0.03 | 10.0 | 13530 | 0.0488 | | 0.0285 | 11.0 | 14883 | 0.0489 | | 0.0272 | 12.0 | 16236 | 0.0500 | | 0.0262 | 13.0 | 17589 | 0.0510 | | 0.0258 | 14.0 | 18942 | 0.0511 | | 0.0245 | 15.0 | 20295 | 0.0522 | | 0.0239 | 16.0 | 21648 | 0.0525 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

danielsaggau/lbert_scotus_classsification

danielsaggau

2022-10-20T11:09:25Z

4

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "generated_from_trainer", "dataset:lex_glue", "license:cc-by-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-20T11:02:37Z

--- license: cc-by-sa-4.0 tags: - generated_from_trainer datasets: - lex_glue model-index: - name: lbert_scotus_classsification results: [] ---  # lbert_scotus_classsification This model is a fine-tuned version of [nlpaueb/legal-bert-base-uncased](https://huggingface.co/nlpaueb/legal-bert-base-uncased) on the lex_glue 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: 3e-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: 5 ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

readerbench/RoSummary-medium

readerbench

2022-10-20T10:00:04Z

5

0

transformers

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

text-generation

2022-10-19T06:32:45Z

Model card for RoSummary-medium --- language: - ro --- # RoSummary This is a version of the RoGPT2 model trained on the [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews) dataset for the summarization task. There are 3 trained versions, they are available on the HuggingFace Hub: * [base](https://huggingface.co/readerbench/RoSummary-base) * [medium](https://huggingface.co/readerbench/RoSummary-medium) * [large](https://huggingface.co/readerbench/RoSummary-large) ## Evaluation on [AlephNews](https://huggingface.co/datasets/readerbench/AlephNews) | Model | Decode Method | | BERTScore | | | ROUGE | | |:------:|:--------------:|:---------:|:---------:|:--------:|:--------:|:--------:|:--------:| | | | Precision | Recall | F1-Score | ROUGE-1 | ROUGE-2 | ROUGE-L | | | Greedy | 0.7335 | 0.7399 | 0.7358 | 0.3360 | 0.1862 | 0.3333 | | Base | Beam Search | 0.7354 | 0.7468 | 0.7404 | 0.3480 | 0.1991 | 0.3416 | | | Top-p Sampling | 0.7296 | 0.7299 | 0.7292 | 0.3058 | 0.1452 | 0.2951 | | | Greedy | 0.7378 | 0.7401 | 0.7380 | 0.3422 | 0.1922 | 0.3394 | | Medium | Beam Search | 0.7390 | **0.7493**|**0.7434**|**0.3546**|**0.2061**|**0.3467**| | | Top-p Sampling | 0.7315 | 0.7285 | 0.7294 | 0.3042 | 0.1400 | 0.2921 | | | Greedy | 0.7376 | 0.7424 | 0.7391 | 0.3414 | 0.1895 | 0.3355 | | Large | Beam Search | **0.7394**| 0.7470 | 0.7424 | 0.3492 | 0.1995 | 0.3384 | | | Top-p Sampling | 0.7311 | 0.7301 | 0.7299 | 0.3051 | 0.1418 | 0.2931 | ## Acknowledgments --- Research supported with [Cloud TPUs](https://cloud.google.com/tpu/) from Google's [TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc)

bthomas/article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm

bthomas

2022-10-20T09:36:12Z

5

0

transformers

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

fill-mask

2022-10-20T08:33:40Z

--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm results: [] ---  # article2keyword2.1b_paraphrase-multilingual-MiniLM-L12-v2_finetuned_for_mlm This model is a fine-tuned version of [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0673 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 16 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:-----:|:---------------:| | 2.3777 | 1.0 | 1353 | 0.3168 | | 0.2358 | 2.0 | 2706 | 0.1564 | | 0.1372 | 3.0 | 4059 | 0.1149 | | 0.1046 | 4.0 | 5412 | 0.0956 | | 0.086 | 5.0 | 6765 | 0.0853 | | 0.0741 | 6.0 | 8118 | 0.0786 | | 0.0653 | 7.0 | 9471 | 0.0750 | | 0.0594 | 8.0 | 10824 | 0.0726 | | 0.0542 | 9.0 | 12177 | 0.0699 | | 0.0504 | 10.0 | 13530 | 0.0692 | | 0.047 | 11.0 | 14883 | 0.0684 | | 0.0444 | 12.0 | 16236 | 0.0675 | | 0.0423 | 13.0 | 17589 | 0.0674 | | 0.0404 | 14.0 | 18942 | 0.0673 | | 0.0392 | 15.0 | 20295 | 0.0672 | | 0.0379 | 16.0 | 21648 | 0.0673 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

mprzibilla/super_large_finetune_CM01

mprzibilla

2022-10-20T09:04:35Z

4

0

transformers

[ "transformers", "pytorch", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2022-10-19T23:12:30Z

--- tags: - generated_from_trainer model-index: - name: super_large_finetune_CM01 results: [] ---  # super_large_finetune_CM01 This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 7.2285 - Wer: 0.7714 ## 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: 0.0001 - train_batch_size: 15 - 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: 857 - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 1.0031 | 5.0 | 1715 | 1.9766 | 0.7857 | | 0.2107 | 10.0 | 3430 | 3.8748 | 0.8238 | | 0.1393 | 15.0 | 5145 | 4.7403 | 0.7952 | | 0.0931 | 20.0 | 6860 | 3.5077 | 0.6667 | | 0.0649 | 25.0 | 8575 | 7.7419 | 0.9333 | | 0.0592 | 30.0 | 10290 | 5.6440 | 0.7762 | | 0.0396 | 35.0 | 12005 | 6.9629 | 0.6810 | | 0.03 | 40.0 | 13720 | 7.8282 | 0.7524 | | 0.0191 | 45.0 | 15435 | 6.4626 | 0.7429 | | 0.0121 | 50.0 | 17150 | 7.2285 | 0.7714 | ### Framework versions - Transformers 4.18.0 - Pytorch 1.10.2+cu102 - Datasets 2.3.2 - Tokenizers 0.12.1

jayanta/vit-base-patch16-224-FV-20epochs-finetuned-memes

jayanta

2022-10-20T08:21:22Z

43

0

transformers

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

image-classification

2022-10-20T07:39:54Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - imagefolder metrics: - accuracy - precision - recall - f1 model-index: - name: vit-base-patch16-224-FV-20epochs-finetuned-memes results: - task: name: Image Classification type: image-classification dataset: name: imagefolder type: imagefolder config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.8632148377125193 - name: Precision type: precision value: 0.8617373130509159 - name: Recall type: recall value: 0.8632148377125193 - name: F1 type: f1 value: 0.8621436376894498 ---  # vit-base-patch16-224-FV-20epochs-finetuned-memes This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset. It achieves the following results on the evaluation set: - Loss: 0.6532 - Accuracy: 0.8632 - Precision: 0.8617 - Recall: 0.8632 - F1: 0.8621 ## 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: 0.00012 - 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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:| | 1.1709 | 0.99 | 20 | 0.9393 | 0.6971 | 0.6896 | 0.6971 | 0.6890 | | 0.5295 | 1.99 | 40 | 0.5024 | 0.8091 | 0.8210 | 0.8091 | 0.8133 | | 0.2909 | 2.99 | 60 | 0.4070 | 0.8539 | 0.8529 | 0.8539 | 0.8529 | | 0.1435 | 3.99 | 80 | 0.4136 | 0.8539 | 0.8522 | 0.8539 | 0.8522 | | 0.0928 | 4.99 | 100 | 0.4495 | 0.8478 | 0.8548 | 0.8478 | 0.8507 | | 0.0643 | 5.99 | 120 | 0.4897 | 0.8594 | 0.8572 | 0.8594 | 0.8573 | | 0.061 | 6.99 | 140 | 0.5040 | 0.8423 | 0.8490 | 0.8423 | 0.8453 | | 0.0519 | 7.99 | 160 | 0.5266 | 0.8524 | 0.8502 | 0.8524 | 0.8510 | | 0.0546 | 8.99 | 180 | 0.5200 | 0.8586 | 0.8632 | 0.8586 | 0.8605 | | 0.0478 | 9.99 | 200 | 0.5654 | 0.8555 | 0.8548 | 0.8555 | 0.8548 | | 0.0509 | 10.99 | 220 | 0.5774 | 0.8609 | 0.8626 | 0.8609 | 0.8616 | | 0.0467 | 11.99 | 240 | 0.5847 | 0.8594 | 0.8602 | 0.8594 | 0.8594 | | 0.0468 | 12.99 | 260 | 0.5909 | 0.8601 | 0.8597 | 0.8601 | 0.8596 | | 0.0469 | 13.99 | 280 | 0.5970 | 0.8563 | 0.8560 | 0.8563 | 0.8561 | | 0.0438 | 14.99 | 300 | 0.6234 | 0.8594 | 0.8583 | 0.8594 | 0.8586 | | 0.0441 | 15.99 | 320 | 0.6190 | 0.8563 | 0.8582 | 0.8563 | 0.8570 | | 0.0431 | 16.99 | 340 | 0.6419 | 0.8570 | 0.8584 | 0.8570 | 0.8574 | | 0.0454 | 17.99 | 360 | 0.6528 | 0.8563 | 0.8556 | 0.8563 | 0.8558 | | 0.0417 | 18.99 | 380 | 0.6688 | 0.8578 | 0.8575 | 0.8578 | 0.8574 | | 0.0432 | 19.99 | 400 | 0.6532 | 0.8632 | 0.8617 | 0.8632 | 0.8621 | ### Framework versions - Transformers 4.24.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.6.1.dev0 - Tokenizers 0.13.1

thisisHJLee/wav2vec2-large-xls-r-300m-korean-w1

thisisHJLee

2022-10-20T08:00:16Z

3

0

transformers

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

automatic-speech-recognition

2022-10-20T05:38:51Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xls-r-300m-korean-w1 results: [] ---  # wav2vec2-large-xls-r-300m-korean-w1 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1406 - Cer: 0.0393 ## 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: 0.0001 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Cer | |:-------------:|:-----:|:----:|:---------------:|:------:| | 24.537 | 0.56 | 800 | 3.0461 | 0.9274 | | 1.9309 | 1.13 | 1600 | 0.7723 | 0.2168 | | 0.7595 | 1.69 | 2400 | 0.3197 | 0.0916 | | 0.4338 | 2.26 | 3200 | 0.2051 | 0.0587 | | 0.3067 | 2.82 | 4000 | 0.1406 | 0.0393 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

nayan06/binary-classifier-conversion-intent-1.1-l12

nayan06

2022-10-20T07:05:09Z

3

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-18T11:34:15Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Setfit Classification Model ON Conversion Dataset With L12 sbert Model as Base This is a Setfit Model with the L6 model as a Base for classification.  ## Usage (Setfit) ``` pip install setfit ``` Then you can use the model like this: ```python from setfit import SetFitModel model = SetFitModel.from_pretrained("nayan06/binary-classifier-conversion-intent-1.1-l12") prediction = model(['i want to buy thing']) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME}) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 2163 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": 2163, "warmup_steps": 217, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Dataset Used https://huggingface.co/datasets/nayan06/conversion1.0 ## Citing & Authors

debbiesoon/t5-small-T5_summarise

debbiesoon

2022-10-20T06:09:39Z

6

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

2022-10-20T05:53:01Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - rouge model-index: - name: t5-small-T5_summarise results: [] ---  # t5-small-T5_summarise This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset. It achieves the following results on the evaluation set: - Loss: 5.0384 - Rouge1: 15.9638 - Rouge2: 9.0883 - Rougel: 13.2968 - Rougelsum: 14.5007 - Gen Len: 19.0 ## 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: 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: 1 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 4.2781 | 1.0 | 2 | 5.0384 | 15.9638 | 9.0883 | 13.2968 | 14.5007 | 19.0 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

nguyenkhoa2407/bert-base-cased-NER-favsbot

nguyenkhoa2407

2022-10-20T05:11:31Z

5

0

transformers

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

token-classification

2022-08-23T15:57:05Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - favsbot metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-cased-NER-favsbot results: - task: name: Token Classification type: token-classification dataset: name: favsbot type: favsbot config: default split: train args: default metrics: - name: Precision type: precision value: 0.8461538461538461 - name: Recall type: recall value: 0.88 - name: F1 type: f1 value: 0.8627450980392156 - name: Accuracy type: accuracy value: 0.9444444444444444 ---  # bert-base-cased-NER-favsbot This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the favsbot dataset. It achieves the following results on the evaluation set: - Loss: 0.1680 - Precision: 0.8462 - Recall: 0.88 - F1: 0.8627 - Accuracy: 0.9444 ## 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: 1.5e-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: 20 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 7 | 1.8761 | 0.0 | 0.0 | 0.0 | 0.5833 | | No log | 2.0 | 14 | 1.3530 | 0.0 | 0.0 | 0.0 | 0.5972 | | No log | 3.0 | 21 | 1.0400 | 1.0 | 0.12 | 0.2143 | 0.6389 | | No log | 4.0 | 28 | 0.7987 | 0.7895 | 0.6 | 0.6818 | 0.8194 | | No log | 5.0 | 35 | 0.6055 | 0.85 | 0.68 | 0.7556 | 0.875 | | No log | 6.0 | 42 | 0.4749 | 0.8696 | 0.8 | 0.8333 | 0.9167 | | No log | 7.0 | 49 | 0.3838 | 0.84 | 0.84 | 0.8400 | 0.9444 | | No log | 8.0 | 56 | 0.3084 | 0.88 | 0.88 | 0.88 | 0.9583 | | No log | 9.0 | 63 | 0.2643 | 0.88 | 0.88 | 0.88 | 0.9583 | | No log | 10.0 | 70 | 0.2360 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 11.0 | 77 | 0.2168 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 12.0 | 84 | 0.2031 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 13.0 | 91 | 0.1937 | 0.88 | 0.88 | 0.88 | 0.9583 | | No log | 14.0 | 98 | 0.1853 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 15.0 | 105 | 0.1791 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 16.0 | 112 | 0.1757 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 17.0 | 119 | 0.1718 | 0.8462 | 0.88 | 0.8627 | 0.9444 | | No log | 18.0 | 126 | 0.1698 | 0.8148 | 0.88 | 0.8462 | 0.9444 | | No log | 19.0 | 133 | 0.1686 | 0.8148 | 0.88 | 0.8462 | 0.9444 | | No log | 20.0 | 140 | 0.1680 | 0.8462 | 0.88 | 0.8627 | 0.9444 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1

debbiesoon/summarise_v6

debbiesoon

2022-10-20T04:32:42Z

10

0

transformers

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

text2text-generation

2022-10-16T20:04:04Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: summarise_v6 results: [] --- # summarise_v6 This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0497 - Rouge2 Precision: 0.3109 - Rouge2 Recall: 0.406 - Rouge2 Fmeasure: 0.3375 ## Model description More information needed ## Intended uses & limitations max_input_length = 3072 max_output_length = 1000 led.config.max_length = 1000 led.config.min_length = 100 ## 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: 2 - eval_batch_size: 2 - seed: 42 - 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.7163 | 0.22 | 10 | 1.2307 | 0.1428 | 0.5118 | 0.2089 | | 1.632 | 0.44 | 20 | 1.1337 | 0.36 | 0.3393 | 0.3181 | | 1.0916 | 0.67 | 30 | 1.0738 | 0.2693 | 0.3487 | 0.2731 | | 1.573 | 0.89 | 40 | 1.0497 | 0.3109 | 0.406 | 0.3375 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1

debbiesoon/summarise

debbiesoon

2022-10-20T04:12:19Z

12

0

transformers

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

text2text-generation

2022-10-16T03:34:42Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: summarise results: [] ---  # summarise This model is a fine-tuned version of [allenai/led-base-16384](https://huggingface.co/allenai/led-base-16384) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0497 - Rouge2 Precision: 0.3109 - Rouge2 Recall: 0.406 - Rouge2 Fmeasure: 0.3375 ## Model description More information needed ## Intended uses & limitations max_input_length = 3072 max_output_length = 1000 led.config.max_length = 1000 led.config.min_length = 100 ## 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: 2 - eval_batch_size: 2 - seed: 42 - 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 | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure | |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:| | 1.7163 | 0.22 | 10 | 1.2307 | 0.1428 | 0.5118 | 0.2089 | | 1.632 | 0.44 | 20 | 1.1337 | 0.36 | 0.3393 | 0.3181 | | 1.0916 | 0.67 | 30 | 1.0738 | 0.2693 | 0.3487 | 0.2731 | | 1.573 | 0.89 | 40 | 1.0497 | 0.3109 | 0.406 | 0.3375 | ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1

debbiesoon/longformer_summarise_large

debbiesoon

2022-10-20T03:55:16Z

10

0

transformers

[ "transformers", "pytorch", "led", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-20T03:29:04Z

--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: longformer_summarise_large results: [] ---  # longformer_summarise_large This model is a fine-tuned version of [patrickvonplaten/led-large-16384-pubmed](https://huggingface.co/patrickvonplaten/led-large-16384-pubmed) 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: 5e-05 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.21.3 - Pytorch 1.12.1+cu113 - Datasets 1.2.1 - Tokenizers 0.12.1

tomjam/bert-finetuned-ner

tomjam

2022-10-20T01:48:18Z

16

0

transformers

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

token-classification

2022-10-20T00:48:15Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - conll2003 metrics: - precision - recall - f1 - accuracy model-index: - name: bert-finetuned-ner results: - task: name: Token Classification type: token-classification dataset: name: conll2003 type: conll2003 config: conll2003 split: train args: conll2003 metrics: - name: Precision type: precision value: 0.9352911896465903 - name: Recall type: recall value: 0.9486704813194211 - name: F1 type: f1 value: 0.9419333277633887 - name: Accuracy type: accuracy value: 0.9864455171601814 ---  # bert-finetuned-ner This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset. It achieves the following results on the evaluation set: - Loss: 0.0610 - Precision: 0.9353 - Recall: 0.9487 - F1: 0.9419 - Accuracy: 0.9864 ## 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 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | 0.0907 | 1.0 | 1756 | 0.0732 | 0.9188 | 0.9337 | 0.9262 | 0.9818 | | 0.035 | 2.0 | 3512 | 0.0607 | 0.9280 | 0.9480 | 0.9379 | 0.9859 | | 0.0169 | 3.0 | 5268 | 0.0610 | 0.9353 | 0.9487 | 0.9419 | 0.9864 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

vwxyzjn/BreakoutNoFrameskip-v4-dqn_atari-seed1

vwxyzjn

2022-10-20T00:34:56Z

0

null

[ "tensorboard", "BreakoutNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]

reinforcement-learning

2022-10-20T00:34:52Z

--- tags: - BreakoutNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - custom-implementation model-index: - name: DQN results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: BreakoutNoFrameskip-v4 type: BreakoutNoFrameskip-v4 metrics: - type: mean_reward value: 2.70 +/- 4.12 name: mean_reward verified: false --- # (CleanRL) **DQN** Agent Playing **BreakoutNoFrameskip-v4** This is a trained model of a DQN agent playing BreakoutNoFrameskip-v4. The model was trained by using [CleanRL](https://github.com/vwxyzjn/cleanrl) and the training code can be found [here](https://github.com/vwxyzjn/cleanrl/blob/master/cleanrl/dqn_atari.py). # Hyperparameters ```python {'batch_size': 32, 'buffer_size': 1000000, 'capture_video': False, 'cuda': True, 'end_e': 0.01, 'env_id': 'BreakoutNoFrameskip-v4', 'exp_name': 'dqn_atari', 'exploration_fraction': 0.1, 'gamma': 0.99, 'hf_entity': '', 'learning_rate': 0.0001, 'learning_starts': 80000, 'save_model': True, 'seed': 1, 'start_e': 1, 'target_network_frequency': 1000, 'torch_deterministic': True, 'total_timesteps': 10000, 'track': False, 'train_frequency': 4, 'upload_model': True, 'wandb_entity': None, 'wandb_project_name': 'cleanRL'} ```

MagoMerlot/PSFs_generated

MagoMerlot

2022-10-19T23:46:03Z

3

0

diffusers

[ "diffusers", "tensorboard", "en", "license:mit", "diffusers:DDPMPipeline", "region:us" ]

null

2022-10-19T05:43:58Z

--- language: en tags: - diffusers license: mit ---

spencer-gable-cook/COVID-19_Misinformation_Detector

spencer-gable-cook

2022-10-19T22:53:16Z

20

1

transformers

[ "transformers", "pytorch", "onnx", "bert", "text-classification", "arxiv:2006.00885", "doi:10.57967/hf/3925", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-18T20:23:56Z

--- license: mit --- Welcome to the COVID-19 Misinformation Detector! There is a lot of misinformation related to the COVID-19 vaccine being posted online from unreliable sources. The COVID-19 Misinformation Detector allows you to check if the information you are reading online (e.g. from Twitter or Facebook) contains misinformation or not! Enter the text from the online post in the "Hosted inference API" text area to the right to check if it is misinformation. "LABEL_0" means that no misinformation was detected in the post, while "LABEL_1" means that the post is misinformation. The COVID-19 Misinformation Detector is a modified version of the "bert-base-uncased" transformer model, found [here](https://huggingface.co/bert-base-uncased). It is fine-tuned on two datasets containing tweets relating to the COVID-19 pandemic; each tweet is labelled as containing misinformation (1) or not (0), as verified by healthcare experts. The datasets used are: 1. [ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection](https://www.sciencedirect.com/science/article/pii/S0033350621004534) 2. [CoAID (Covid-19 HeAlthcare mIsinformation Dataset)](https://arxiv.org/abs/2006.00885) For a more detailed explanation, check out the technical report [here](https://drive.google.com/file/d/1QW9D6TN4KXX6poa6Q5L6FVgqaDQ4DxY9/view?usp=sharing), and check out my literature review on transformers [here](https://drive.google.com/file/d/1d5tK3sUwYM1WBheOuNG9A7ZYri2zxdyw/view?usp=sharing)!

AokiDaiki/distilbert-base-uncased-finetuned-emotion

AokiDaiki

2022-10-19T22:45:33Z

3

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

2022-10-18T18:31:02Z

--- 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 args: default metrics: - name: Accuracy type: accuracy value: 0.927 - name: F1 type: f1 value: 0.9270524571534725 ---  # 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: 0.2174 - Accuracy: 0.927 - F1: 0.9271 ## 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: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8148 | 1.0 | 250 | 0.3148 | 0.9 | 0.8967 | | 0.2487 | 2.0 | 500 | 0.2174 | 0.927 | 0.9271 | ### Framework versions - Transformers 4.13.0 - Pytorch 1.12.1+cu113 - Datasets 1.16.1 - Tokenizers 0.10.3

CavenLen/ddpm-Kaga-128

CavenLen

2022-10-19T22:03:31Z

19

0

diffusers

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

null

2022-10-17T12:48:44Z

--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: CavenLen/Kaga metrics: [] ---  # ddpm-Kaga-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `CavenLen/Kaga` 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: 16 - eval_batch_size: 16 - 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/CavenLen/ddpm-Kaga-128/tensorboard?#scalars)

thucdangvan020999/marian-finetuned-kde4-en-to-fr

thucdangvan020999

2022-10-19T21:12:47Z

5

0

transformers

[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "dataset:kde4", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

translation

2022-10-19T19:27:37Z

--- license: apache-2.0 tags: - translation - generated_from_trainer datasets: - kde4 metrics: - bleu model-index: - name: marian-finetuned-kde4-en-to-fr results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: kde4 type: kde4 config: en-fr split: train args: en-fr metrics: - name: Bleu type: bleu value: 52.83113187001415 ---  # marian-finetuned-kde4-en-to-fr This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset. It achieves the following results on the evaluation set: - Loss: 0.8560 - Bleu: 52.8311 ## 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: 32 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

mathislucka/tat-model

mathislucka

2022-10-19T20:44:53Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-19T20:44:45Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # mathislucka/tat-model This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.  ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('mathislucka/tat-model') embeddings = model.encode(sentences) print(embeddings) ``` ## Evaluation Results  For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=mathislucka/tat-model) ## Training The model was trained with the parameters: **DataLoader**: `torch.utils.data.dataloader.DataLoader` of length 39 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.MarginMSELoss.MarginMSELoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "NoneType", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 3, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 250, 'do_lower_case': False}) with Transformer model: MPNetModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) (2): Normalize() ) ``` ## Citing & Authors

mariolinml/deberta-v3-base_MNLI_10_19_v0

mariolinml

2022-10-19T20:07:22Z

14

0

transformers

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

text-classification

2022-10-19T15:57:15Z

--- license: mit tags: - generated_from_trainer model-index: - name: deberta-v3-base_MNLI_10_19_v0 results: [] ---  # deberta-v3-base_MNLI_10_19_v0 This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

rajesh426/distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY

rajesh426

2022-10-19T19:38:11Z

3

0

transformers

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

text-classification

2022-10-19T19:31:05Z

--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY results: [] ---  # distilbert-base-uncased_finetuned_SPEECH_TEXT_CH_2_DISPLAY This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.0863 - Accuracy: 0.7368 - F1: 0.7114 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 40 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 1.0362 | 1.0 | 19 | 0.9281 | 0.5789 | 0.4964 | | 0.9725 | 2.0 | 38 | 0.8906 | 0.6316 | 0.5707 | | 0.8712 | 3.0 | 57 | 0.8080 | 0.6316 | 0.5889 | | 0.6402 | 4.0 | 76 | 0.6386 | 0.7895 | 0.7474 | | 0.4453 | 5.0 | 95 | 0.5401 | 0.7895 | 0.7485 | | 0.2658 | 6.0 | 114 | 0.4999 | 0.8421 | 0.7990 | | 0.1695 | 7.0 | 133 | 0.6248 | 0.7895 | 0.7427 | | 0.0822 | 8.0 | 152 | 0.7391 | 0.7368 | 0.7114 | | 0.0303 | 9.0 | 171 | 0.6665 | 0.7895 | 0.7485 | | 0.016 | 10.0 | 190 | 0.8217 | 0.7368 | 0.7114 | | 0.0103 | 11.0 | 209 | 0.8090 | 0.7368 | 0.7114 | | 0.0083 | 12.0 | 228 | 0.8646 | 0.7368 | 0.7114 | | 0.0068 | 13.0 | 247 | 0.9091 | 0.7368 | 0.7114 | | 0.0059 | 14.0 | 266 | 0.8731 | 0.7368 | 0.7114 | | 0.0049 | 15.0 | 285 | 0.9512 | 0.7368 | 0.7114 | | 0.0048 | 16.0 | 304 | 0.9376 | 0.7368 | 0.7114 | | 0.004 | 17.0 | 323 | 0.9507 | 0.7368 | 0.7114 | | 0.0037 | 18.0 | 342 | 0.9868 | 0.7368 | 0.7114 | | 0.0033 | 19.0 | 361 | 0.9862 | 0.7368 | 0.7114 | | 0.0029 | 20.0 | 380 | 0.9733 | 0.7368 | 0.7114 | | 0.0029 | 21.0 | 399 | 0.9747 | 0.7368 | 0.7114 | | 0.0027 | 22.0 | 418 | 0.9998 | 0.7368 | 0.7114 | | 0.0024 | 23.0 | 437 | 0.9984 | 0.7368 | 0.7114 | | 0.0024 | 24.0 | 456 | 1.0270 | 0.7368 | 0.7114 | | 0.0024 | 25.0 | 475 | 1.0083 | 0.7368 | 0.7114 | | 0.0022 | 26.0 | 494 | 1.0167 | 0.7368 | 0.7114 | | 0.0021 | 27.0 | 513 | 1.0273 | 0.7368 | 0.7114 | | 0.002 | 28.0 | 532 | 1.0340 | 0.7368 | 0.7114 | | 0.0021 | 29.0 | 551 | 1.0282 | 0.7368 | 0.7114 | | 0.002 | 30.0 | 570 | 1.0372 | 0.7368 | 0.7114 | | 0.0019 | 31.0 | 589 | 1.0593 | 0.7368 | 0.7114 | | 0.0017 | 32.0 | 608 | 1.0841 | 0.7368 | 0.7114 | | 0.0018 | 33.0 | 627 | 1.0920 | 0.7368 | 0.7114 | | 0.0019 | 34.0 | 646 | 1.0943 | 0.7368 | 0.7114 | | 0.0018 | 35.0 | 665 | 1.0883 | 0.7368 | 0.7114 | | 0.0017 | 36.0 | 684 | 1.0864 | 0.7368 | 0.7114 | | 0.0016 | 37.0 | 703 | 1.0890 | 0.7368 | 0.7114 | | 0.0017 | 38.0 | 722 | 1.0894 | 0.7368 | 0.7114 | | 0.0015 | 39.0 | 741 | 1.0867 | 0.7368 | 0.7114 | | 0.0016 | 40.0 | 760 | 1.0863 | 0.7368 | 0.7114 | ### Framework versions - Transformers 4.22.2 - Pytorch 1.10.2 - Datasets 2.5.2 - Tokenizers 0.12.1

api19750904/situaciones-turismo

api19750904

2022-10-19T17:59:42Z

40

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-10-19T17:59:26Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: situaciones-turismo results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9101123809814453 --- # situaciones-turismo Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### people beach ![people beach](images/people_beach.jpg) #### people party ![people party](images/people_party.jpg) #### people restaurant ![people restaurant](images/people_restaurant.jpg) #### people walking ![people walking](images/people_walking.jpg)

api19750904/comida-vgm

api19750904

2022-10-19T16:54:30Z

107

0

transformers

[ "transformers", "pytorch", "tensorboard", "vit", "image-classification", "huggingpics", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

image-classification

2022-10-19T16:54:16Z

--- tags: - image-classification - pytorch - huggingpics metrics: - accuracy model-index: - name: comida-vgm results: - task: name: Image Classification type: image-classification metrics: - name: Accuracy type: accuracy value: 0.9550561904907227 --- # comida-vgm Autogenerated by HuggingPics🤗🖼️ Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb). Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). ## Example Images #### burguer ![burguer](images/burguer.jpg) #### macarroni ![macarroni](images/macarroni.jpg) #### pizza ![pizza](images/pizza.jpg) #### spaguetti ![spaguetti](images/spaguetti.jpg)

huggingtweets/konradha_

huggingtweets

2022-10-19T16:11:00Z

3

0

transformers

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

text-generation

2022-10-19T16:09:29Z

--- language: en thumbnail: http://www.huggingtweets.com/konradha_/1666195856134/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/1540685336422088704/JDxiybNe_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">Konrad</div> <div style="text-align: center; font-size: 14px;">@konradha_</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 Konrad. | Data | Konrad | | --- | --- | | Tweets downloaded | 256 | | Retweets | 38 | | Short tweets | 75 | | Tweets kept | 143 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3ox7i4yk/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 @konradha_'s tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/10k5hc9s) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/10k5hc9s/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/konradha_') 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)

gabski/sbert-relative-claim-quality

gabski

2022-10-19T16:10:59Z

1

0

sentence-transformers

[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]

sentence-similarity

2022-10-19T15:59:40Z

--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # Model This [sentence-transformers](https://www.SBERT.net) model model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth # Claim Quality Classification We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. We train this model by fine-tuning SBERT based on bert-base-cased using a siamese network structure with softmax loss. Outputs can also be used to rank multiple versions of the same claim, for example, using [SVMRank](https://github.com/ds4dm/PySVMRank) or BTL (Bradley-Terry-Luce model). ## Usage (Sentence-Transformers) Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed: ``` pip install -U sentence-transformers ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer sentences = ["This is an example sentence", "Each sentence is converted"] model = SentenceTransformer('gabski/sbert-relative-claim-quality') embeddings = model.encode(sentences) print(embeddings) ``` ## Usage (HuggingFace Transformers) Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings. ```python from transformers import AutoTokenizer, AutoModel import torch #Mean Pooling - Take attention mask into account for correct averaging def mean_pooling(model_output, attention_mask): token_embeddings = model_output[0] #First element of model_output contains all token embeddings input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) # Sentences we want sentence embeddings for sentences = ['This is an example sentence', 'Each sentence is converted'] # Load model from HuggingFace Hub tokenizer = AutoTokenizer.from_pretrained('gabski/sbert-relative-claim-quality') model = AutoModel.from_pretrained('gabski/sbert-relative-claim-quality') # Tokenize sentences encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') # Compute token embeddings with torch.no_grad(): model_output = model(**encoded_input) # Perform pooling. In this case, mean pooling. sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) print("Sentence embeddings:") print(sentence_embeddings) ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False}) ) ``` ## Citing & Authors ```bibtex @inproceedings{skitalinskaya-etal-2021-learning, title = "Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale", author = "Skitalinskaya, Gabriella and Klaff, Jonas and Wachsmuth, Henning", booktitle = "Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume", month = apr, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.eacl-main.147", doi = "10.18653/v1/2021.eacl-main.147", pages = "1718--1729", } ```

gabski/bert-relative-claim-quality

gabski

2022-10-19T16:09:19Z

18

0

transformers

[ "transformers", "pytorch", "bert", "text-classification", "en", "dataset:ClaimRev", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2022-10-19T14:04:22Z

--- language: en license: cc-by-nc-sa-4.0 datasets: - ClaimRev --- # Model This model was obtained by fine-tuning bert-base-cased on the ClaimRev dataset. Paper: [Learning From Revisions: Quality Assessment of Claims in Argumentation at Scale](https://aclanthology.org/2021.eacl-main.147/) Authors: Gabriella Skitalinskaya, Jonas Klaff, Henning Wachsmuth # Claim Quality Classification We cast this task as a pairwise classification task, where the objective is to compare two versions of the same claim and determine which one is better. # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification import torch tokenizer = AutoTokenizer.from_pretrained("gabski/bert-relative-claim-quality") model = AutoModelForSequenceClassification.from_pretrained("gabski/bert-relative-claim-quality") claim_1 = 'Smoking marijuana is less harmfull then smoking cigarettes.' claim_2 = 'Smoking marijuana is less harmful than smoking cigarettes.' model_input = tokenizer(claim_1,claim_2, return_tensors='pt') model_outputs = model(**model_input) outputs = torch.nn.functional.softmax(model_outputs.logits, dim = -1) print(outputs) ```

rakamsata/anim

rakamsata

2022-10-19T15:46:31Z

0

null

[ "license:bigscience-openrail-m", "region:us" ]

null

2022-10-19T15:46:31Z

--- license: bigscience-openrail-m ---

smz2122/image

smz2122

2022-10-19T15:37:37Z

0

null

[ "region:us" ]

null

2022-10-19T15:37:20Z

git clone https://huggingface.co/templates/text-to-image cd text-to-image git remote set-url origin https://huggingface.co/$YOUR_USER/$YOUR_REPO_NAME git push --force

enryu43/anifusion_unet

enryu43

2022-10-19T15:01:54Z

15

6

diffusers

[ "diffusers", "diffusers:LDMTextToImagePipeline", "region:us" ]

null

2022-10-11T21:01:02Z

This model is converted with https://github.com/huggingface/diffusers/blob/main/scripts/convert_original_stable_diffusion_to_diffusers.py. However, the tokenizer in the diffuser model is wrong, for proper usage, see description at https://medium.com/@enryu9000/anifusion-diffusion-models-for-anime-pictures-138cf1af2cbe, and instructions/examples at https://github.com/enryu43/anifusion-stable-diffusion. Also, the original checkpoint in the Latent Diffusion format is available. Installation instructions for webui: https://gist.github.com/enryu43/858999bf69dc92b97fdad6137c3c45e6

bthomas/article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm

bthomas

2022-10-19T14:48:17Z

6

0

transformers

[ "transformers", "pytorch", "mbart", "text2text-generation", "mlm", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-19T14:32:22Z

--- license: apache-2.0 tags: - mlm - generated_from_trainer model-index: - name: article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm results: [] ---  # article2keyword2.2_barthez-orangesum-title_finetuned_for_mlm This model is a fine-tuned version of [moussaKam/barthez-orangesum-title](https://huggingface.co/moussaKam/barthez-orangesum-title) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0452 ## 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 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.3187 | 1.0 | 1235 | 0.0545 | | 0.0544 | 2.0 | 2470 | 0.0491 | | 0.0461 | 3.0 | 3705 | 0.0463 | | 0.042 | 4.0 | 4940 | 0.0452 | ### Framework versions - Transformers 4.21.1 - Pytorch 1.11.0 - Datasets 2.3.2 - Tokenizers 0.12.1

huggingtweets/moonideograph

huggingtweets

2022-10-19T14:31:00Z

3

0

transformers

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

text-generation

2022-10-19T14:28:14Z

--- language: en thumbnail: http://www.huggingtweets.com/moonideograph/1666189855449/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/1581258561400848384/ktYtGqLD_400x400.jpg')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> <div style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')"> </div> </div> <div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div> <div style="text-align: center; font-size: 16px; font-weight: 800">🌑 Loona the Ninth</div> <div style="text-align: center; font-size: 14px;">@moonideograph</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 🌑 Loona the Ninth. | Data | 🌑 Loona the Ninth | | --- | --- | | Tweets downloaded | 409 | | Retweets | 104 | | Short tweets | 22 | | Tweets kept | 283 | [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/8mujtj4v/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 @moonideograph's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/21pia0le) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/21pia0le/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/moonideograph') 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)

facebook/xm_transformer_unity_hk-en

facebook

2022-10-19T14:28:29Z

39

7

fairseq

[ "fairseq", "audio", "audio-to-audio", "speech-to-speech-translation", "license:cc-by-nc-4.0", "region:us" ]

audio-to-audio

2022-10-08T00:55:30Z

--- license: cc-by-nc-4.0 library_name: fairseq task: audio-to-audio tags: - fairseq - audio - audio-to-audio - speech-to-speech-translation datasets: - MuST-C - TAT - Hokkien dramas --- ## xm_transformer_unity_hk-en Speech-to-speech translation model with two-pass decoder (UnitY) from fairseq: - Hokkien-English - Trained with supervised data in TED, drama, [TAT](https://sites.google.com/speech.ntut.edu.tw/fsw/home/tat-corpus) domain, and weakly supervised data in drama domain. See [here](https://research.facebook.com/publications/hokkien-direct-speech-to-speech-translation) for training details. - Speech synthesis with [facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur](https://huggingface.co/facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur) - [Project Page](https://github.com/facebookresearch/fairseq/tree/ust/examples/hokkien) ## Usage ```python import json import os from pathlib import Path import IPython.display as ipd from fairseq import hub_utils from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub from fairseq.models.speech_to_text.hub_interface import S2THubInterface from fairseq.models.text_to_speech import CodeHiFiGANVocoder from fairseq.models.text_to_speech.hub_interface import VocoderHubInterface from huggingface_hub import snapshot_download import torchaudio cache_dir = os.getenv("HUGGINGFACE_HUB_CACHE") models, cfg, task = load_model_ensemble_and_task_from_hf_hub( "facebook/xm_transformer_unity_hk-en", arg_overrides={"config_yaml": "config.yaml", "task": "speech_to_text"}, cache_dir=cache_dir, ) #model = models[0].cpu() #cfg["task"].cpu = True generator = task.build_generator([model], cfg) # requires 16000Hz mono channel audio audio, _ = torchaudio.load("/path/to/an/audio/file") sample = S2THubInterface.get_model_input(task, audio) unit = S2THubInterface.get_prediction(task, model, generator, sample) # speech synthesis library_name = "fairseq" cache_dir = ( cache_dir or (Path.home() / ".cache" / library_name).as_posix() ) cache_dir = snapshot_download( f"facebook/unit_hifigan_mhubert_vp_en_es_fr_it3_400k_layer11_km1000_lj_dur", cache_dir=cache_dir, library_name=library_name ) x = hub_utils.from_pretrained( cache_dir, "model.pt", ".", archive_map=CodeHiFiGANVocoder.hub_models(), config_yaml="config.json", fp16=False, is_vocoder=True, ) with open(f"{x['args']['data']}/config.json") as f: vocoder_cfg = json.load(f) assert ( len(x["args"]["model_path"]) == 1 ), "Too many vocoder models in the input" vocoder = CodeHiFiGANVocoder(x["args"]["model_path"][0], vocoder_cfg) tts_model = VocoderHubInterface(vocoder_cfg, vocoder) tts_sample = tts_model.get_model_input(unit) wav, sr = tts_model.get_prediction(tts_sample) ipd.Audio(wav, rate=sr) ```

mclarknc/ppo-LunarLander-v2

mclarknc

2022-10-19T14:03:08Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-19T14:02:48Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 273.18 +/- 23.61 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

yuk/my-gothic-waifu-diffusion

yuk

2022-10-19T13:35:02Z

0

null

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

null

2022-10-19T13:35:02Z

--- license: bigscience-bloom-rail-1.0 ---

kjhanjee/autotrain-code_classification-1815762639

kjhanjee

2022-10-19T11:01:40Z

1

0

transformers

[ "transformers", "pytorch", "autotrain", "text-classification", "en", "dataset:kjhanjee/autotrain-data-code_classification", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

text-classification

2022-10-19T10:56:20Z

--- tags: - autotrain - text-classification language: - en widget: - text: "I love AutoTrain 🤗" datasets: - kjhanjee/autotrain-data-code_classification co2_eq_emissions: emissions: 11.438220107218369 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1815762639 - CO2 Emissions (in grams): 11.4382 ## Validation Metrics - Loss: 0.849 - Accuracy: 0.794 - Macro F1: 0.788 - Micro F1: 0.794 - Weighted F1: 0.788 - Macro Precision: 0.797 - Micro Precision: 0.794 - Weighted Precision: 0.797 - Macro Recall: 0.794 - Micro Recall: 0.794 - Weighted Recall: 0.794 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/kjhanjee/autotrain-code_classification-1815762639 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("kjhanjee/autotrain-code_classification-1815762639", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("kjhanjee/autotrain-code_classification-1815762639", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```

TestZee/t5-small-baseline_summary_zee_v1.0

TestZee

2022-10-19T10:39:31Z

9

0

transformers

[ "transformers", "tf", "tensorboard", "t5", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2022-10-19T10:25:30Z

--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: TestZee/t5-small-baseline_summary_zee_v1.0 results: [] ---  # TestZee/t5-small-baseline_summary_zee_v1.0 This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 2.3722 - Validation Loss: 2.1596 - Train Rouge1: 21.6350 - Train Rouge2: 8.9453 - Train Rougel: 17.8649 - Train Rougelsum: 19.9099 - Train Gen Len: 19.0 - Epoch: 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: - optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01} - training_precision: float32 ### Training results | Train Loss | Validation Loss | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch | |:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:| | 2.3722 | 2.1596 | 21.6350 | 8.9453 | 17.8649 | 19.9099 | 19.0 | 0 | ### Framework versions - Transformers 4.23.1 - TensorFlow 2.9.2 - Datasets 2.6.1 - Tokenizers 0.13.1

gaioNL/LunarLander-v2

gaioNL

2022-10-19T09:43:37Z

0

stable-baselines3

[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]

reinforcement-learning

2022-10-19T09:10:05Z

--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 metrics: - type: mean_reward value: 280.56 +/- 28.20 name: mean_reward verified: false --- # **PPO** Agent playing **LunarLander-v2** This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```

amichailidis/greek_legal_bert_v2-finetuned-ner-V2

amichailidis

2022-10-19T09:27:25Z

6

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-11T09:10:51Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: greek_legal_bert_v2-finetuned-ner-V3 results: [] ---  # greek_legal_bert_v2-finetuned-ner-V3 This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0907 - Precision: 0.9023 - Recall: 0.9265 - F1: 0.9142 - Accuracy: 0.9828 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.19 | 25 | 0.0661 | 0.8895 | 0.9229 | 0.9059 | 0.9813 | | No log | 2.38 | 50 | 0.0820 | 0.9091 | 0.9319 | 0.9204 | 0.9838 | | No log | 3.57 | 75 | 0.0791 | 0.8924 | 0.9211 | 0.9065 | 0.9825 | | No log | 4.76 | 100 | 0.0824 | 0.8950 | 0.9319 | 0.9131 | 0.9841 | | No log | 5.95 | 125 | 0.0820 | 0.8830 | 0.9194 | 0.9008 | 0.9812 | | No log | 7.14 | 150 | 0.0862 | 0.9059 | 0.9319 | 0.9187 | 0.9817 | | No log | 8.33 | 175 | 0.0915 | 0.9021 | 0.9247 | 0.9133 | 0.9826 | | No log | 9.52 | 200 | 0.0905 | 0.9023 | 0.9265 | 0.9142 | 0.9828 | ### Framework versions - Transformers 4.23.0 - Pytorch 1.12.1+cu113 - Datasets 2.5.2 - Tokenizers 0.13.1

amichailidis/greek_legal_bert_v2-finetuned-ner

amichailidis

2022-10-19T09:21:07Z

19

0

transformers

[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-09-08T09:21:14Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: greek_legal_bert_v2-finetuned-ner results: [] ---  # greek_legal_bert_v2-finetuned-ner This model is a fine-tuned version of [alexaapo/greek_legal_bert_v2](https://huggingface.co/alexaapo/greek_legal_bert_v2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0900 - Precision: 0.8424 - Recall: 0.8638 - F1: 0.8530 - Accuracy: 0.9775 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.64 | 250 | 0.0839 | 0.7859 | 0.8539 | 0.8185 | 0.9737 | | 0.1127 | 1.29 | 500 | 0.0783 | 0.8092 | 0.8569 | 0.8324 | 0.9759 | | 0.1127 | 1.93 | 750 | 0.0743 | 0.8284 | 0.8446 | 0.8364 | 0.9766 | | 0.0538 | 2.58 | 1000 | 0.0816 | 0.8243 | 0.8597 | 0.8416 | 0.9774 | | 0.0538 | 3.22 | 1250 | 0.0900 | 0.8424 | 0.8638 | 0.8530 | 0.9776 | | 0.0346 | 3.87 | 1500 | 0.0890 | 0.8401 | 0.8597 | 0.8498 | 0.9770 | | 0.0346 | 4.51 | 1750 | 0.0964 | 0.8342 | 0.8576 | 0.8457 | 0.9768 | | 0.0233 | 5.15 | 2000 | 0.1094 | 0.8336 | 0.8645 | 0.8488 | 0.9768 | | 0.0233 | 5.8 | 2250 | 0.1110 | 0.8456 | 0.8549 | 0.8502 | 0.9777 | | 0.0161 | 6.44 | 2500 | 0.1224 | 0.8408 | 0.8535 | 0.8471 | 0.9769 | | 0.0161 | 7.09 | 2750 | 0.1281 | 0.8347 | 0.8624 | 0.8483 | 0.9770 | | 0.0114 | 7.73 | 3000 | 0.1268 | 0.8397 | 0.8573 | 0.8484 | 0.9773 | | 0.0114 | 8.38 | 3250 | 0.1308 | 0.8388 | 0.8549 | 0.8468 | 0.9771 | | 0.0088 | 9.02 | 3500 | 0.1301 | 0.8412 | 0.8559 | 0.8485 | 0.9772 | | 0.0088 | 9.66 | 3750 | 0.1368 | 0.8396 | 0.8604 | 0.8499 | 0.9772 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

pcoloc/autotrain-only-rssi-1813762559

pcoloc

2022-10-19T08:57:26Z

7

0

transformers

[ "transformers", "joblib", "autotrain", "tabular", "regression", "tabular-regression", "dataset:pcoloc/autotrain-data-only-rssi", "co2_eq_emissions", "endpoints_compatible", "region:us" ]

tabular-regression

2022-10-19T08:55:40Z

--- tags: - autotrain - tabular - regression - tabular-regression datasets: - pcoloc/autotrain-data-only-rssi co2_eq_emissions: emissions: 1.3554114117578944 --- # Model Trained Using AutoTrain - Problem type: Single Column Regression - Model ID: 1813762559 - CO2 Emissions (in grams): 1.3554 ## Validation Metrics - Loss: 83.432 - R2: 0.312 - MSE: 6960.888 - MAE: 60.449 - RMSLE: 0.532 ## Usage ```python import json import joblib import pandas as pd model = joblib.load('model.joblib') config = json.load(open('config.json')) features = config['features'] # data = pd.read_csv("data.csv") data = data[features] data.columns = ["feat_" + str(col) for col in data.columns] predictions = model.predict(data) # or model.predict_proba(data) ```

mriggs/byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it

mriggs

2022-10-19T08:42:40Z

6

0

transformers

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

text2text-generation

2022-10-19T07:20:38Z

--- license: apache-2.0 tags: - generated_from_trainer datasets: - opus_books model-index: - name: byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it results: [] ---  # byt5-small-finetuned-1epoch-batch16-opus_books-en-to-it This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the opus_books dataset. It achieves the following results on the evaluation set: - Loss: 0.9848 ## 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: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.3771 | 1.0 | 1819 | 0.9848 | ### Framework versions - Transformers 4.23.1 - Pytorch 1.12.1+cu113 - Datasets 2.6.1 - Tokenizers 0.13.1

amichailidis/bert-base-greek-uncased-v1-finetuned-ner

amichailidis

2022-10-19T08:32:01Z

13

0

transformers

[ "transformers", "pytorch", "bert", "token-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2022-10-19T08:00:16Z

--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: bert-base-greek-uncased-v1-finetuned-ner results: [] ---  # bert-base-greek-uncased-v1-finetuned-ner This model is a fine-tuned version of [nlpaueb/bert-base-greek-uncased-v1](https://huggingface.co/nlpaueb/bert-base-greek-uncased-v1) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1052 - Precision: 0.8440 - Recall: 0.8566 - F1: 0.8503 - Accuracy: 0.9768 ## 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: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 0.64 | 250 | 0.0913 | 0.7814 | 0.8208 | 0.8073 | 0.9728 | | 0.1144 | 1.29 | 500 | 0.0823 | 0.7940 | 0.8448 | 0.8342 | 0.9755 | | 0.1144 | 1.93 | 750 | 0.0812 | 0.8057 | 0.8212 | 0.8328 | 0.9751 | | 0.0570 | 2.58 | 1000 | 0.0855 | 0.8244 | 0.8514 | 0.8292 | 0.9744 | | 0.0570 | 3.22 | 1250 | 0.0926 | 0.8329 | 0.8441 | 0.8397 | 0.9760 | | 0.0393 | 3.87 | 1500 | 0.0869 | 0.8256 | 0.8633 | 0.8440 | 0.9774 | | 0.0393 | 4.51 | 1750 | 0.1049 | 0.8290 | 0.8636 | 0.8459 | 0.9766 | | 0.026 | 5.15 | 2000 | 0.1093 | 0.8440 | 0.8566 | 0.8503 | 0.9768 | | 0.026 | 5.8 | 2250 | 0.1172 | 0.8301 | 0.8514 | 0.8406 | 0.9760 | | 0.0189 | 6.44 | 2500 | 0.1273 | 0.8238 | 0.8688 | 0.8457 | 0.9766 | | 0.0189 | 7.09 | 2750 | 0.1246 | 0.8350 | 0.8539 | 0.8443 | 0.9764 | | 0.0148 | 7.73 | 3000 | 0.1262 | 0.8333 | 0.8608 | 0.8468 | 0.9764 | | 0.0148 | 8.38 | 3250 | 0.1347 | 0.8319 | 0.8591 | 0.8453 | 0.9762 | | 0.0010 | 9.02 | 3500 | 0.1325 | 0.8376 | 0.8504 | 0.8439 | 0.9766 | | 0.0010 | 9.66 | 3750 | 0.1362 | 0.8371 | 0.8563 | 0.8466 | 0.9765 | ### Framework versions - Transformers 4.22.0 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1

crumb/eva-model-ckpt

crumb

2022-10-19T08:11:22Z

0

2

null

[ "region:us" ]

null

2022-10-19T04:45:12Z

storage for eva models. it has intermediate low-performing models