Split (1)

train · 880k rows

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
carbon225/canine-s-wordseg-en	carbon225	2022-09-23T23:42:11Z	98	1	transformers	[ "transformers", "pytorch", "canine", "token-classification", "en", "license:cc0-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-22T03:58:10Z	--- license: cc0-1.0 language: en widget: - text: "thismodelcanperformwordsegmentation" - text: "sometimesitdoesntworkquitewell" - text: "expertsexchange" ---
ericntay/stbl_clinical_bert_ft_rs5	ericntay	2022-09-23T20:39:56Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-23T20:21:55Z	--- license: mit tags: - generated_from_trainer metrics: - f1 model-index: - name: stbl_clinical_bert_ft_rs5 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. --> # stbl_clinical_bert_ft_rs5 This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0936 - F1: 0.9268 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 12 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.2723 \| 1.0 \| 101 \| 0.0875 \| 0.8479 \| \| 0.066 \| 2.0 \| 202 \| 0.0688 \| 0.9002 \| \| 0.0328 \| 3.0 \| 303 \| 0.0668 \| 0.9070 \| \| 0.0179 \| 4.0 \| 404 \| 0.0689 \| 0.9129 \| \| 0.0098 \| 5.0 \| 505 \| 0.0790 \| 0.9147 \| \| 0.0069 \| 6.0 \| 606 \| 0.0805 \| 0.9205 \| \| 0.0033 \| 7.0 \| 707 \| 0.0835 \| 0.9268 \| \| 0.0022 \| 8.0 \| 808 \| 0.0904 \| 0.9262 \| \| 0.0021 \| 9.0 \| 909 \| 0.0882 \| 0.9263 \| \| 0.0015 \| 10.0 \| 1010 \| 0.0933 \| 0.9289 \| \| 0.0009 \| 11.0 \| 1111 \| 0.0921 \| 0.9311 \| \| 0.0009 \| 12.0 \| 1212 \| 0.0936 \| 0.9268 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
g30rv17ys/ddpm-geeve-drusen-1000-200ep	g30rv17ys	2022-09-23T19:12:36Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:39:11Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-drusen-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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/geevegeorge/ddpm-geeve-drusen-1000-200ep/tensorboard?#scalars)
g30rv17ys/ddpm-geeve-cnv-1000-200ep	g30rv17ys	2022-09-23T19:10:42Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:29:54Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-cnv-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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/geevegeorge/ddpm-geeve-cnv-1000-200ep/tensorboard?#scalars)
gokuls/distilbert-base-Massive-intent	gokuls	2022-09-23T19:02:42Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T18:50:57Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: distilbert-base-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8947368421052632 --- <!-- 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-Massive-intent This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.7693 - Accuracy: 0.8947 ## 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: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.4555 \| 1.0 \| 720 \| 0.5983 \| 0.8426 \| \| 0.407 \| 2.0 \| 1440 \| 0.4702 \| 0.8775 \| \| 0.2095 \| 3.0 \| 2160 \| 0.5319 \| 0.8834 \| \| 0.1172 \| 4.0 \| 2880 \| 0.5902 \| 0.8810 \| \| 0.0683 \| 5.0 \| 3600 \| 0.6555 \| 0.8810 \| \| 0.042 \| 6.0 \| 4320 \| 0.6989 \| 0.8879 \| \| 0.0253 \| 7.0 \| 5040 \| 0.6963 \| 0.8928 \| \| 0.0208 \| 8.0 \| 5760 \| 0.7313 \| 0.8908 \| \| 0.0119 \| 9.0 \| 6480 \| 0.7683 \| 0.8923 \| \| 0.0093 \| 10.0 \| 7200 \| 0.7693 \| 0.8947 \| \| 0.0071 \| 11.0 \| 7920 \| 0.7873 \| 0.8923 \| \| 0.0047 \| 12.0 \| 8640 \| 0.8275 \| 0.8893 \| \| 0.003 \| 13.0 \| 9360 \| 0.8312 \| 0.8928 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
tszocinski/bart-base-squad-question-generation	tszocinski	2022-09-23T18:43:43Z	75	0	transformers	[ "transformers", "tf", "bart", "text2text-generation", "generated_from_keras_callback", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-22T19:36:46Z	--- license: apache-2.0 tags: - generated_from_keras_callback model-index: - name: tszocinski/bart-base-squad-question-generation 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. --> # tszocinski/bart-base-squad-question-generation This model is a fine-tuned version of [tszocinski/bart-base-squad-question-generation](https://huggingface.co/tszocinski/bart-base-squad-question-generation) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 6.5656 - Validation Loss: 11.1958 - 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: {'inner_optimizer': {'class_name': 'RMSprop', 'config': {'name': 'RMSprop', 'learning_rate': 0.001, 'decay': 0.0, 'rho': 0.9, 'momentum': 0.0, 'epsilon': 1e-07, 'centered': False}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000} - training_precision: mixed_float16 ### Training results \| Train Loss \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 6.5656 \| 11.1958 \| 0 \| ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1
g30rv17ys/ddpm-geeve-normal-1000-200ep	g30rv17ys	2022-09-23T18:24:23Z	0	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-23T15:24:37Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-normal-1000-200ep ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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/geevegeorge/ddpm-geeve-normal-1000-200ep/tensorboard?#scalars)
mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli	mfreihaut	2022-09-23T18:20:23Z	23	1	transformers	[ "transformers", "pytorch", "bart", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-21T18:05:28Z	--- tags: - generated_from_trainer model-index: - name: iab_classification-finetuned-mnli-finetuned-mnli 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. --> # iab_classification-finetuned-mnli-finetuned-mnli This model is a fine-tuned version of [mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli](https://huggingface.co/mfreihaut/iab_classification-finetuned-mnli-finetuned-mnli) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8711 ## 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: 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: 50 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| No log \| 1.0 \| 250 \| 1.5956 \| \| 0.9361 \| 2.0 \| 500 \| 0.0409 \| \| 0.9361 \| 3.0 \| 750 \| 2.9853 \| \| 0.7634 \| 4.0 \| 1000 \| 0.1317 \| \| 0.7634 \| 5.0 \| 1250 \| 0.4056 \| \| 0.611 \| 6.0 \| 1500 \| 1.8038 \| \| 0.611 \| 7.0 \| 1750 \| 0.6305 \| \| 0.5627 \| 8.0 \| 2000 \| 0.6923 \| \| 0.5627 \| 9.0 \| 2250 \| 3.7410 \| \| 0.9863 \| 10.0 \| 2500 \| 2.1912 \| \| 0.9863 \| 11.0 \| 2750 \| 1.5405 \| \| 1.0197 \| 12.0 \| 3000 \| 1.9271 \| \| 1.0197 \| 13.0 \| 3250 \| 1.1741 \| \| 0.5186 \| 14.0 \| 3500 \| 1.1864 \| \| 0.5186 \| 15.0 \| 3750 \| 0.7945 \| \| 0.4042 \| 16.0 \| 4000 \| 1.0645 \| \| 0.4042 \| 17.0 \| 4250 \| 1.8826 \| \| 0.3637 \| 18.0 \| 4500 \| 0.3234 \| \| 0.3637 \| 19.0 \| 4750 \| 0.2641 \| \| 0.3464 \| 20.0 \| 5000 \| 0.8596 \| \| 0.3464 \| 21.0 \| 5250 \| 0.5601 \| \| 0.2449 \| 22.0 \| 5500 \| 0.4543 \| \| 0.2449 \| 23.0 \| 5750 \| 1.1986 \| \| 0.2595 \| 24.0 \| 6000 \| 0.3642 \| \| 0.2595 \| 25.0 \| 6250 \| 1.3606 \| \| 0.298 \| 26.0 \| 6500 \| 0.8154 \| \| 0.298 \| 27.0 \| 6750 \| 1.1105 \| \| 0.1815 \| 28.0 \| 7000 \| 0.7443 \| \| 0.1815 \| 29.0 \| 7250 \| 0.2616 \| \| 0.165 \| 30.0 \| 7500 \| 0.5318 \| \| 0.165 \| 31.0 \| 7750 \| 0.7608 \| \| 0.1435 \| 32.0 \| 8000 \| 0.9647 \| \| 0.1435 \| 33.0 \| 8250 \| 1.3749 \| \| 0.1516 \| 34.0 \| 8500 \| 0.7167 \| \| 0.1516 \| 35.0 \| 8750 \| 0.5426 \| \| 0.1359 \| 36.0 \| 9000 \| 0.7225 \| \| 0.1359 \| 37.0 \| 9250 \| 0.5453 \| \| 0.1266 \| 38.0 \| 9500 \| 0.4825 \| \| 0.1266 \| 39.0 \| 9750 \| 0.7271 \| \| 0.1153 \| 40.0 \| 10000 \| 0.9044 \| \| 0.1153 \| 41.0 \| 10250 \| 1.0363 \| \| 0.1175 \| 42.0 \| 10500 \| 0.7987 \| \| 0.1175 \| 43.0 \| 10750 \| 0.7596 \| \| 0.1089 \| 44.0 \| 11000 \| 0.8637 \| \| 0.1089 \| 45.0 \| 11250 \| 0.8327 \| \| 0.1092 \| 46.0 \| 11500 \| 0.7161 \| \| 0.1092 \| 47.0 \| 11750 \| 0.7768 \| \| 0.1068 \| 48.0 \| 12000 \| 0.9059 \| \| 0.1068 \| 49.0 \| 12250 \| 0.8829 \| \| 0.1045 \| 50.0 \| 12500 \| 0.8711 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.10.0 - Datasets 2.5.1 - Tokenizers 0.12.1
nkkodelacruz/distilbert-base-uncased-finetuned-cola	nkkodelacruz	2022-09-23T16:17:52Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:glue", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T09:07:40Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - matthews_correlation model-index: - name: distilbert-base-uncased-finetuned-cola results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue config: cola split: train args: cola metrics: - name: Matthews Correlation type: matthews_correlation value: 0.5595884617444483 --- <!-- 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.7903 - Matthews Correlation: 0.5596 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.5224 \| 1.0 \| 535 \| 0.5373 \| 0.3974 \| \| 0.3503 \| 2.0 \| 1070 \| 0.5142 \| 0.4942 \| \| 0.2328 \| 3.0 \| 1605 \| 0.5449 \| 0.5449 \| \| 0.1775 \| 4.0 \| 2140 \| 0.7457 \| 0.5487 \| \| 0.1235 \| 5.0 \| 2675 \| 0.7903 \| 0.5596 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
gokuls/distilroberta-base-Massive-intent	gokuls	2022-09-23T15:34:27Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "dataset:massive", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-23T15:23:33Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - massive metrics: - accuracy model-index: - name: distilroberta-base-Massive-intent results: - task: name: Text Classification type: text-classification dataset: name: massive type: massive config: en-US split: train args: en-US metrics: - name: Accuracy type: accuracy value: 0.8937530742744713 --- <!-- 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-Massive-intent This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the massive dataset. It achieves the following results on the evaluation set: - Loss: 0.6618 - Accuracy: 0.8938 ## 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: 16 - eval_batch_size: 16 - seed: 33 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\| \| 1.41 \| 1.0 \| 720 \| 0.6742 \| 0.8288 \| \| 0.4978 \| 2.0 \| 1440 \| 0.5150 \| 0.8751 \| \| 0.3009 \| 3.0 \| 2160 \| 0.5705 \| 0.8790 \| \| 0.1953 \| 4.0 \| 2880 \| 0.5887 \| 0.8795 \| \| 0.127 \| 5.0 \| 3600 \| 0.6123 \| 0.8810 \| \| 0.0914 \| 6.0 \| 4320 \| 0.6575 \| 0.8834 \| \| 0.0583 \| 7.0 \| 5040 \| 0.6618 \| 0.8938 \| \| 0.0355 \| 8.0 \| 5760 \| 0.7591 \| 0.8864 \| \| 0.0259 \| 9.0 \| 6480 \| 0.8087 \| 0.8780 \| \| 0.02 \| 10.0 \| 7200 \| 0.7964 \| 0.8888 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Eulering/moonlight-night	Eulering	2022-09-23T14:47:20Z	0	0	null	[ "license:bigscience-openrail-m", "region:us" ]	null	2022-09-23T14:47:20Z	--- license: bigscience-openrail-m ---
bhumikak/resultsb	bhumikak	2022-09-23T14:21:23Z	105	0	transformers	[ "transformers", "pytorch", "pegasus", "text2text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-23T13:46:43Z	--- tags: - generated_from_trainer model-index: - name: resultsb 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. --> # resultsb This model is a fine-tuned version of [bhumikak/resultsa](https://huggingface.co/bhumikak/resultsa) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 4.8957 - Rouge2 Precision: 0.2127 - Rouge2 Recall: 0.2605 - Rouge2 Fmeasure: 0.2167 ## 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: 0.0005 - train_batch_size: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adafactor - lr_scheduler_type: linear - num_epochs: 50 - label_smoothing_factor: 0.1 ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Yousef-Cot/distilbert-base-uncased-finetuned-emotion	Yousef-Cot	2022-09-23T13:21:28Z	107	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	2022-09-23T07:18:41Z	--- 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: 0.9215 - name: F1 type: f1 value: 0.9218038766645168 --- <!-- 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: 0.2201 - Accuracy: 0.9215 - F1: 0.9218 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8242 \| 1.0 \| 250 \| 0.3311 \| 0.8965 \| 0.8931 \| \| 0.254 \| 2.0 \| 500 \| 0.2201 \| 0.9215 \| 0.9218 \| ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.2 - Datasets 2.4.0 - Tokenizers 0.11.6
Vmuaddib/autotrain-gudel-department-classifier-clean-886428460	Vmuaddib	2022-09-23T13:07:21Z	132	0	transformers	[ "transformers", "pytorch", "electra", "text-classification", "autotrain", "de", "dataset:Vmuaddib/autotrain-data-gudel-department-classifier-clean", "co2_eq_emissions", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-05-19T19:51:20Z	--- tags: [autotrain] language: de widget: - text: "I love AutoTrain 🤗" datasets: - Vmuaddib/autotrain-data-gudel-department-classifier-clean co2_eq_emissions: 14.294320632050567 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 886428460 - CO2 Emissions (in grams): 14.294320632050567 ## Validation Metrics - Loss: 0.051413487643003464 - Accuracy: 0.9894490035169988 - Precision: 1.0 - Recall: 0.9862174578866769 - AUC: 0.9989318529862175 - F1: 0.9930609097918273 ## 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/Vmuaddib/autotrain-gudel-department-classifier-clean-886428460 ``` Or Python API: ``` from transformers import AutoModelForSequenceClassification, AutoTokenizer model = AutoModelForSequenceClassification.from_pretrained("Vmuaddib/autotrain-gudel-department-classifier-clean-886428460", use_auth_token=True) tokenizer = AutoTokenizer.from_pretrained("Vmuaddib/autotrain-gudel-department-classifier-clean-886428460", use_auth_token=True) inputs = tokenizer("I love AutoTrain", return_tensors="pt") outputs = model(**inputs) ```
huggingtweets/cushbomb	huggingtweets	2022-09-23T12:40:19Z	113	0	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/cushbomb/1663936814713/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/1560517790900969473/MPbfc6w2_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">matt christman</div> <div style="text-align: center; font-size: 14px;">@cushbomb</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 matt christman. \| Data \| matt christman \| \| --- \| --- \| \| Tweets downloaded \| 3230 \| \| Retweets \| 241 \| \| Short tweets \| 685 \| \| Tweets kept \| 2304 \| [Explore the data](https://wandb.ai/wandb/huggingtweets/runs/39bxpmve/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 @cushbomb's tweets. Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob) for full transparency and reproducibility. At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2gd8zqob/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/cushbomb') 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)
tkuye/reinforce-dd	tkuye	2022-09-23T10:57:05Z	108	0	transformers	[ "transformers", "pytorch", "t5", "text2text-generation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-23T09:49:10Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: reinforce-dd 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. --> # reinforce-dd This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0000 ## 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: 0.0005 - 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 0.5375 \| 1.35 \| 500 \| 0.0017 \| \| 0.0001 \| 2.7 \| 1000 \| 0.0000 \| \| 0.0 \| 4.05 \| 1500 \| 0.0000 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.0 - Datasets 2.5.1 - Tokenizers 0.12.1
rinascimento/distilbert-base-uncased-finetuned-emotion	rinascimento	2022-09-23T09:52:40Z	103	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	2022-09-23T06:15:09Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: default split: train args: default metrics: - name: Accuracy type: accuracy value: 0.924 - name: F1 type: f1 value: 0.9241401774459951 --- <!-- 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: 0.2167 - Accuracy: 0.924 - F1: 0.9241 ## 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.815 \| 1.0 \| 250 \| 0.3051 \| 0.9045 \| 0.9022 \| \| 0.2496 \| 2.0 \| 500 \| 0.2167 \| 0.924 \| 0.9241 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
bryanleeharyanto/vtt-indonesia	bryanleeharyanto	2022-09-23T06:39:29Z	3	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-20T07:59:29Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: vtt-indonesia 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. --> # vtt-indonesia This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.3472 - Wer: 0.3582 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 3.7612 \| 3.23 \| 400 \| 0.6405 \| 0.6714 \| \| 0.4143 \| 6.45 \| 800 \| 0.3772 \| 0.4974 \| \| 0.2068 \| 9.68 \| 1200 \| 0.3877 \| 0.4442 \| \| 0.1436 \| 12.9 \| 1600 \| 0.3785 \| 0.4212 \| \| 0.1133 \| 16.13 \| 2000 \| 0.3944 \| 0.4144 \| \| 0.09 \| 19.35 \| 2400 \| 0.3695 \| 0.3925 \| \| 0.0705 \| 22.58 \| 2800 \| 0.3706 \| 0.3846 \| \| 0.057 \| 25.81 \| 3200 \| 0.3720 \| 0.3725 \| \| 0.048 \| 29.03 \| 3600 \| 0.3472 \| 0.3582 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu113 - Datasets 1.18.3 - Tokenizers 0.10.3
apapiu/diffusion_model_aesthetic_keras	apapiu	2022-09-23T03:56:11Z	0	1	null	[ "license:openrail", "region:us" ]	null	2022-09-21T19:14:31Z	--- license: openrail --- A sample from the [Laion 6.5+ ](https://laion.ai/blog/laion-aesthetics/) image + text dataset. You can see some samples [here](http://captions.christoph-schuhmann.de/2B-en-6.5.html). The samples are resized + center-cropped to 64x64x3 and the .npz file also contains CLIP embeddings. TODO: add img2dataset script. The data can be used to train a basic text-to-image model.
gary109/ai-light-dance_singing5_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1	gary109	2022-09-23T03:39:36Z	77	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "gary109/AI_Light_Dance", "generated_from_trainer", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-21T14:38:44Z	--- tags: - automatic-speech-recognition - gary109/AI_Light_Dance - generated_from_trainer model-index: - name: ai-light-dance_singing5_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1 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. --> # ai-light-dance_singing5_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1 This model is a fine-tuned version of [gary109/ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1](https://huggingface.co/gary109/ai-light-dance_singing4_ft_wav2vec2-large-xlsr-53-5gram-v4-2-1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING5 dataset. It achieves the following results on the evaluation set: - Loss: 0.1732 - Wer: 0.0831 ## 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: 4e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\| \| 0.4351 \| 1.0 \| 100 \| 0.1948 \| 0.0903 \| \| 0.4381 \| 2.0 \| 200 \| 0.1961 \| 0.0930 \| \| 0.441 \| 3.0 \| 300 \| 0.1948 \| 0.0957 \| \| 0.453 \| 4.0 \| 400 \| 0.1971 \| 0.0905 \| \| 0.4324 \| 5.0 \| 500 \| 0.1823 \| 0.0879 \| \| 0.4561 \| 6.0 \| 600 \| 0.1934 \| 0.0893 \| \| 0.4231 \| 7.0 \| 700 \| 0.2088 \| 0.0977 \| \| 0.4339 \| 8.0 \| 800 \| 0.1924 \| 0.0856 \| \| 0.4195 \| 9.0 \| 900 \| 0.1835 \| 0.0846 \| \| 0.4162 \| 10.0 \| 1000 \| 0.1869 \| 0.0908 \| \| 0.411 \| 11.0 \| 1100 \| 0.1966 \| 0.0950 \| \| 0.4034 \| 12.0 \| 1200 \| 0.1890 \| 0.0879 \| \| 0.4155 \| 13.0 \| 1300 \| 0.1844 \| 0.0915 \| \| 0.4123 \| 14.0 \| 1400 \| 0.1849 \| 0.0891 \| \| 0.4002 \| 15.0 \| 1500 \| 0.1901 \| 0.0902 \| \| 0.3983 \| 16.0 \| 1600 \| 0.1879 \| 0.0865 \| \| 0.3907 \| 17.0 \| 1700 \| 0.1863 \| 0.0856 \| \| 0.3969 \| 18.0 \| 1800 \| 0.1773 \| 0.0836 \| \| 0.3721 \| 19.0 \| 1900 \| 0.1834 \| 0.0890 \| \| 0.3987 \| 20.0 \| 2000 \| 0.1817 \| 0.0852 \| \| 0.3863 \| 21.0 \| 2100 \| 0.1898 \| 0.0914 \| \| 0.4052 \| 22.0 \| 2200 \| 0.1882 \| 0.0857 \| \| 0.3811 \| 23.0 \| 2300 \| 0.1874 \| 0.0856 \| \| 0.3791 \| 24.0 \| 2400 \| 0.1932 \| 0.0885 \| \| 0.3919 \| 25.0 \| 2500 \| 0.1847 \| 0.0815 \| \| 0.3891 \| 26.0 \| 2600 \| 0.1850 \| 0.0852 \| \| 0.3719 \| 27.0 \| 2700 \| 0.1774 \| 0.0820 \| \| 0.3791 \| 28.0 \| 2800 \| 0.1756 \| 0.0825 \| \| 0.3537 \| 29.0 \| 2900 \| 0.1797 \| 0.0844 \| \| 0.361 \| 30.0 \| 3000 \| 0.1818 \| 0.0834 \| \| 0.3619 \| 31.0 \| 3100 \| 0.1747 \| 0.0838 \| \| 0.3626 \| 32.0 \| 3200 \| 0.1773 \| 0.0844 \| \| 0.3632 \| 33.0 \| 3300 \| 0.1775 \| 0.0825 \| \| 0.3666 \| 34.0 \| 3400 \| 0.1835 \| 0.0859 \| \| 0.3581 \| 35.0 \| 3500 \| 0.1859 \| 0.0868 \| \| 0.3665 \| 36.0 \| 3600 \| 0.1741 \| 0.0849 \| \| 0.3495 \| 37.0 \| 3700 \| 0.1790 \| 0.0837 \| \| 0.3509 \| 38.0 \| 3800 \| 0.1782 \| 0.0841 \| \| 0.3621 \| 39.0 \| 3900 \| 0.1759 \| 0.0841 \| \| 0.3415 \| 40.0 \| 4000 \| 0.1796 \| 0.0851 \| \| 0.3508 \| 41.0 \| 4100 \| 0.1777 \| 0.0821 \| \| 0.3493 \| 42.0 \| 4200 \| 0.1758 \| 0.0829 \| \| 0.359 \| 43.0 \| 4300 \| 0.1788 \| 0.0848 \| \| 0.3438 \| 44.0 \| 4400 \| 0.1782 \| 0.0836 \| \| 0.3642 \| 45.0 \| 4500 \| 0.1732 \| 0.0831 \| \| 0.3456 \| 46.0 \| 4600 \| 0.1768 \| 0.0823 \| \| 0.3532 \| 47.0 \| 4700 \| 0.1735 \| 0.0834 \| \| 0.3448 \| 48.0 \| 4800 \| 0.1755 \| 0.0827 \| \| 0.3487 \| 49.0 \| 4900 \| 0.1767 \| 0.0833 \| \| 0.3427 \| 50.0 \| 5000 \| 0.1774 \| 0.0836 \| ### Framework versions - Transformers 4.21.0.dev0 - Pytorch 1.9.1+cu102 - Datasets 2.3.3.dev0 - Tokenizers 0.12.1
farleyknight/patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20	farleyknight	2022-09-23T02:53:23Z	98	0	transformers	[ "transformers", "pytorch", "bigbird_pegasus", "text2text-generation", "generated_from_trainer", "dataset:farleyknight/big_patent_5_percent", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-20T21:32:32Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - farleyknight/big_patent_5_percent metrics: - rouge model-index: - name: patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20 results: - task: name: Summarization type: summarization dataset: name: farleyknight/big_patent_5_percent type: farleyknight/big_patent_5_percent config: all split: train args: all metrics: - name: Rouge1 type: rouge value: 37.3764 --- <!-- 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. --> # patent-summarization-google-bigbird-pegasus-large-arxiv-2022-09-20 This model is a fine-tuned version of [google/bigbird-pegasus-large-arxiv](https://huggingface.co/google/bigbird-pegasus-large-arxiv) on the farleyknight/big_patent_5_percent dataset. It achieves the following results on the evaluation set: - Loss: 2.2617 - Rouge1: 37.3764 - Rouge2: 13.2442 - Rougel: 26.011 - Rougelsum: 31.0145 - Gen Len: 113.8789 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:--------:\| \| 2.6121 \| 0.08 \| 5000 \| 2.5652 \| 35.0673 \| 12.0073 \| 24.5471 \| 28.9315 \| 119.9866 \| \| 2.5182 \| 0.17 \| 10000 \| 2.4797 \| 34.6909 \| 11.6432 \| 24.87 \| 28.1543 \| 119.2043 \| \| 2.5102 \| 0.25 \| 15000 \| 2.4238 \| 35.8574 \| 12.2402 \| 25.0712 \| 29.5607 \| 115.2890 \| \| 2.4292 \| 0.33 \| 20000 \| 2.3869 \| 36.0133 \| 12.2453 \| 25.4039 \| 29.483 \| 112.5920 \| \| 2.3678 \| 0.41 \| 25000 \| 2.3594 \| 35.238 \| 11.6833 \| 25.0449 \| 28.3313 \| 119.1739 \| \| 2.3511 \| 0.5 \| 30000 \| 2.3326 \| 36.7755 \| 12.8394 \| 25.7218 \| 30.2594 \| 110.5819 \| \| 2.3334 \| 0.58 \| 35000 \| 2.3125 \| 36.6317 \| 12.7493 \| 25.5388 \| 30.094 \| 115.5998 \| \| 2.3833 \| 0.66 \| 40000 \| 2.2943 \| 37.1219 \| 13.1564 \| 25.7571 \| 30.8666 \| 113.8222 \| \| 2.341 \| 0.75 \| 45000 \| 2.2813 \| 36.4962 \| 12.6225 \| 25.6904 \| 29.9741 \| 115.9845 \| \| 2.3179 \| 0.83 \| 50000 \| 2.2725 \| 37.3535 \| 13.1596 \| 25.7385 \| 31.056 \| 117.7754 \| \| 2.3164 \| 0.91 \| 55000 \| 2.2654 \| 36.9191 \| 12.9316 \| 25.7586 \| 30.4691 \| 116.1670 \| \| 2.3046 \| 0.99 \| 60000 \| 2.2618 \| 37.3992 \| 13.2731 \| 26.0327 \| 31.0338 \| 114.5195 \| ### Framework versions - Transformers 4.23.0.dev0 - Pytorch 1.12.0 - Datasets 2.4.0 - Tokenizers 0.12.1
burakyldrm/wav2vec2-burak-new-300-v2-2	burakyldrm	2022-09-23T02:05:18Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "generated_from_trainer", "dataset:common_voice", "license:apache-2.0", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2022-09-22T11:55:26Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice model-index: - name: wav2vec2-burak-new-300-v2-2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-burak-new-300-v2-2 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset. It achieves the following results on the evaluation set: - Loss: 0.6158 - Wer: 0.3094 ## 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: 32 - 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: 241 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:-----:\|:---------------:\|:------:\| \| 5.5201 \| 8.62 \| 500 \| 3.1581 \| 1.0 \| \| 2.1532 \| 17.24 \| 1000 \| 0.6883 \| 0.5979 \| \| 0.5465 \| 25.86 \| 1500 \| 0.5028 \| 0.4432 \| \| 0.3287 \| 34.48 \| 2000 \| 0.4986 \| 0.4024 \| \| 0.2571 \| 43.1 \| 2500 \| 0.4920 \| 0.3824 \| \| 0.217 \| 51.72 \| 3000 \| 0.5265 \| 0.3724 \| \| 0.1848 \| 60.34 \| 3500 \| 0.5539 \| 0.3714 \| \| 0.1605 \| 68.97 \| 4000 \| 0.5689 \| 0.3670 \| \| 0.1413 \| 77.59 \| 4500 \| 0.5962 \| 0.3501 \| \| 0.1316 \| 86.21 \| 5000 \| 0.5732 \| 0.3494 \| \| 0.1168 \| 94.83 \| 5500 \| 0.5912 \| 0.3461 \| \| 0.1193 \| 103.45 \| 6000 \| 0.5766 \| 0.3378 \| \| 0.0996 \| 112.07 \| 6500 \| 0.5818 \| 0.3403 \| \| 0.0941 \| 120.69 \| 7000 \| 0.5986 \| 0.3315 \| \| 0.0912 \| 129.31 \| 7500 \| 0.5802 \| 0.3280 \| \| 0.0865 \| 137.93 \| 8000 \| 0.5878 \| 0.3290 \| \| 0.0804 \| 146.55 \| 8500 \| 0.5784 \| 0.3228 \| \| 0.0739 \| 155.17 \| 9000 \| 0.5791 \| 0.3180 \| \| 0.0718 \| 163.79 \| 9500 \| 0.5864 \| 0.3146 \| \| 0.0681 \| 172.41 \| 10000 \| 0.6104 \| 0.3178 \| \| 0.0688 \| 181.03 \| 10500 \| 0.5983 \| 0.3160 \| \| 0.0657 \| 189.66 \| 11000 \| 0.6228 \| 0.3203 \| \| 0.0598 \| 198.28 \| 11500 \| 0.6057 \| 0.3122 \| \| 0.0597 \| 206.9 \| 12000 \| 0.6094 \| 0.3129 \| \| 0.0551 \| 215.52 \| 12500 \| 0.6114 \| 0.3127 \| \| 0.0507 \| 224.14 \| 13000 \| 0.6056 \| 0.3094 \| \| 0.0554 \| 232.76 \| 13500 \| 0.6158 \| 0.3094 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
Dallasmorningstar/Yyy	Dallasmorningstar	2022-09-22T23:34:28Z	0	0	null	[ "license:afl-3.0", "region:us" ]	null	2022-09-22T23:34:03Z	--- license: afl-3.0 --- https://huggingface.co/julien-c/DPRNNTasNet-ks16_WHAM_sepclean
wenkai-li/new_classifer_epoch10	wenkai-li	2022-09-22T23:26:38Z	117	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T21:25:20Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: new_classifer_epoch10 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. --> # new_classifer_epoch10 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0837 - Accuracy: 0.9867 ## 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: 32 - 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 \| Accuracy \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\| \| 0.0524 \| 1.0 \| 4248 \| 0.0628 \| 0.9790 \| \| 0.0251 \| 2.0 \| 8496 \| 0.0496 \| 0.9848 \| \| 0.0153 \| 3.0 \| 12744 \| 0.0857 \| 0.9837 \| \| 0.0049 \| 4.0 \| 16992 \| 0.1030 \| 0.9849 \| \| 0.0038 \| 5.0 \| 21240 \| 0.0837 \| 0.9867 \| \| 0.003 \| 6.0 \| 25488 \| 0.1165 \| 0.9856 \| \| 0.0026 \| 7.0 \| 29736 \| 0.1143 \| 0.9853 \| \| 0.0004 \| 8.0 \| 33984 \| 0.1475 \| 0.9856 \| \| 0.0004 \| 9.0 \| 38232 \| 0.1328 \| 0.9861 \| \| 0.0 \| 10.0 \| 42480 \| 0.1349 \| 0.9862 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
neelmehta00/t5-base-finetuned-eli5	neelmehta00	2022-09-22T23:16:27Z	101	0	transformers	[ "transformers", "pytorch", "tensorboard", "t5", "text2text-generation", "generated_from_trainer", "dataset:eli5", "license:apache-2.0", "model-index", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text2text-generation	2022-09-22T15:04:24Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - eli5 metrics: - rouge model-index: - name: t5-base-finetuned-eli5 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: eli5 type: eli5 config: LFQA_reddit split: train_eli5 args: LFQA_reddit metrics: - name: Rouge1 type: rouge value: 14.5658 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-base-finetuned-eli5 This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the eli5 dataset. It achieves the following results on the evaluation set: - Loss: 3.1765 - Rouge1: 14.5658 - Rouge2: 2.2777 - Rougel: 11.2826 - Rougelsum: 13.1136 - Gen Len: 18.9938 ## 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 \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 3.3398 \| 1.0 \| 17040 \| 3.1765 \| 14.5658 \| 2.2777 \| 11.2826 \| 13.1136 \| 18.9938 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
mehdidn/finetuned_bert_fa_zwnj_base_ner	mehdidn	2022-09-22T21:42:36Z	123	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "token-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-02T21:27:28Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: finetuned_parsBERT_NER_fa 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. --> # finetuned_parsBERT_NER_fa This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on the mixed NER dataset collected from ARMAN, PEYMA, and WikiANN. It achieves the following results on the evaluation set: - Loss: 0.0297 - Precision: 0.9481 - Recall: 0.9582 - F1: 0.9531 - Accuracy: 0.9942 ## 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: 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.12 \| 1.0 \| 1821 \| 0.0543 \| 0.8387 \| 0.8577 \| 0.8481 \| 0.9830 \| \| 0.0381 \| 2.0 \| 3642 \| 0.0360 \| 0.8941 \| 0.9247 \| 0.9091 \| 0.9898 \| \| 0.0168 \| 3.0 \| 5463 \| 0.0282 \| 0.9273 \| 0.9452 \| 0.9362 \| 0.9927 \| \| 0.0078 \| 4.0 \| 7284 \| 0.0284 \| 0.9391 \| 0.9551 \| 0.9470 \| 0.9938 \| \| 0.0033 \| 5.0 \| 9105 \| 0.0297 \| 0.9481 \| 0.9582 \| 0.9531 \| 0.9942 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
JJRohan/ppo-LunarLander-v2	JJRohan	2022-09-22T21:12:36Z	1	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-22T21:12:08Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 169.43 +/- 77.42 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical	nlp-guild	2022-09-22T20:06:57Z	136	4	transformers	[ "transformers", "pytorch", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T19:42:37Z	fine-tuned bert-base-chinese for intent recognition task on [dataset](https://huggingface.co/datasets/nlp-guild/intent-recognition-biomedical) # Usage ```python from transformers import AutoTokenizer, AutoModelForSequenceClassification from transformers import TextClassificationPipeline tokenizer = AutoTokenizer.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") model = AutoModelForSequenceClassification.from_pretrained("nlp-guild/bert-base-chinese-finetuned-intent_recognition-biomedical") nlp = TextClassificationPipeline(model = model, tokenizer = tokenizer) label_set = [ '定义', '病因', '预防', '临床表现(病症表现)', '相关病症', '治疗方法', '所属科室', '传染性', '治愈率', '禁忌', '化验/体检方案', '治疗时间', '其他' ] def readable_results(top_k:int, usr_query:str): raw = nlp(usr_query, top_k = top_k) def f(x): index = int(x['label'][6:]) x['label'] = label_set[index] for i in raw: f(i) return raw readable_results(3,'得了心脏病怎么办') ''' [{'label': '治疗方法', 'score': 0.9994503855705261}, {'label': '其他', 'score': 0.00018375989748165011}, {'label': '临床表现(病症表现)', 'score': 0.00010841596667887643}] ''' ```
TingChenChang/hpvqa-lcqmc-ocnli-cnsd-multi-MiniLM-v2	TingChenChang	2022-09-22T19:23:21Z	6	0	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-22T19:23:08Z	--- 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 384 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 12 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters: ``` {'scale': 20.0, 'similarity_fct': 'cos_sim'} ``` Parameters of the fit()-Method: ``` { "epochs": 10, "evaluation_steps": 1, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'torch.optim.adamw.AdamW'>", "optimizer_params": { "lr": 1e-06 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 12, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, '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}) ) ``` ## Citing & Authors <!--- Describe where people can find more information -->
jayanta/twitter-roberta-base-sentiment-sentiment-memes-30epcohs	jayanta	2022-09-22T19:04:33Z	107	0	transformers	[ "transformers", "pytorch", "tensorboard", "roberta", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T14:38:21Z	--- tags: - generated_from_trainer metrics: - accuracy - precision - recall - f1 model-index: - name: twitter-roberta-base-sentiment-sentiment-memes-30epcohs 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. --> # twitter-roberta-base-sentiment-sentiment-memes-30epcohs This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3027 - Accuracy: 0.8517 - Precision: 0.8536 - Recall: 0.8517 - F1: 0.8523 ## 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: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| F1 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:---------:\|:------:\|:------:\| \| 0.2504 \| 1.0 \| 2147 \| 0.7129 \| 0.8087 \| 0.8112 \| 0.8087 \| 0.8036 \| \| 0.2449 \| 2.0 \| 4294 \| 0.7500 \| 0.8229 \| 0.8279 \| 0.8229 \| 0.8240 \| \| 0.2652 \| 3.0 \| 6441 \| 0.7460 \| 0.8181 \| 0.8185 \| 0.8181 \| 0.8149 \| \| 0.2585 \| 4.0 \| 8588 \| 0.7906 \| 0.8155 \| 0.8152 \| 0.8155 \| 0.8153 \| \| 0.2534 \| 5.0 \| 10735 \| 0.8178 \| 0.8061 \| 0.8180 \| 0.8061 \| 0.8080 \| \| 0.2498 \| 6.0 \| 12882 \| 0.8139 \| 0.8166 \| 0.8163 \| 0.8166 \| 0.8164 \| \| 0.2825 \| 7.0 \| 15029 \| 0.7494 \| 0.8155 \| 0.8210 \| 0.8155 \| 0.8168 \| \| 0.2459 \| 8.0 \| 17176 \| 0.8870 \| 0.8061 \| 0.8122 \| 0.8061 \| 0.8075 \| \| 0.2303 \| 9.0 \| 19323 \| 0.8699 \| 0.7987 \| 0.8060 \| 0.7987 \| 0.8003 \| \| 0.2425 \| 10.0 \| 21470 \| 0.8043 \| 0.8244 \| 0.8275 \| 0.8244 \| 0.8253 \| \| 0.2143 \| 11.0 \| 23617 \| 0.9163 \| 0.8208 \| 0.8251 \| 0.8208 \| 0.8219 \| \| 0.2054 \| 12.0 \| 25764 \| 0.8330 \| 0.8239 \| 0.8258 \| 0.8239 \| 0.8245 \| \| 0.208 \| 13.0 \| 27911 \| 1.0673 \| 0.8134 \| 0.8216 \| 0.8134 \| 0.8150 \| \| 0.1668 \| 14.0 \| 30058 \| 0.9071 \| 0.8270 \| 0.8276 \| 0.8270 \| 0.8273 \| \| 0.1571 \| 15.0 \| 32205 \| 0.9294 \| 0.8339 \| 0.8352 \| 0.8339 \| 0.8344 \| \| 0.1857 \| 16.0 \| 34352 \| 0.9909 \| 0.8354 \| 0.8350 \| 0.8354 \| 0.8352 \| \| 0.1476 \| 17.0 \| 36499 \| 0.9747 \| 0.8433 \| 0.8436 \| 0.8433 \| 0.8434 \| \| 0.1341 \| 18.0 \| 38646 \| 0.9372 \| 0.8422 \| 0.8415 \| 0.8422 \| 0.8415 \| \| 0.1181 \| 19.0 \| 40793 \| 1.0301 \| 0.8433 \| 0.8443 \| 0.8433 \| 0.8437 \| \| 0.1192 \| 20.0 \| 42940 \| 1.1332 \| 0.8407 \| 0.8415 \| 0.8407 \| 0.8410 \| \| 0.0983 \| 21.0 \| 45087 \| 1.2002 \| 0.8428 \| 0.8498 \| 0.8428 \| 0.8440 \| \| 0.0951 \| 22.0 \| 47234 \| 1.2141 \| 0.8475 \| 0.8504 \| 0.8475 \| 0.8483 \| \| 0.0784 \| 23.0 \| 49381 \| 1.1652 \| 0.8407 \| 0.8453 \| 0.8407 \| 0.8417 \| \| 0.0623 \| 24.0 \| 51528 \| 1.1730 \| 0.8417 \| 0.8443 \| 0.8417 \| 0.8425 \| \| 0.054 \| 25.0 \| 53675 \| 1.2900 \| 0.8454 \| 0.8496 \| 0.8454 \| 0.8464 \| \| 0.0584 \| 26.0 \| 55822 \| 1.2831 \| 0.8480 \| 0.8497 \| 0.8480 \| 0.8486 \| \| 0.0531 \| 27.0 \| 57969 \| 1.3043 \| 0.8506 \| 0.8524 \| 0.8506 \| 0.8512 \| \| 0.0522 \| 28.0 \| 60116 \| 1.2891 \| 0.8527 \| 0.8554 \| 0.8527 \| 0.8534 \| \| 0.037 \| 29.0 \| 62263 \| 1.3077 \| 0.8538 \| 0.8559 \| 0.8538 \| 0.8544 \| \| 0.038 \| 30.0 \| 64410 \| 1.3027 \| 0.8517 \| 0.8536 \| 0.8517 \| 0.8523 \| ### Framework versions - Transformers 4.13.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 1.15.2.dev0 - Tokenizers 0.10.1
lizaboiarchuk/tiny-rubert-war-finetuned	lizaboiarchuk	2022-09-22T17:27:24Z	70	0	transformers	[ "transformers", "tf", "bert", "fill-mask", "generated_from_keras_callback", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-09-22T17:04:15Z	--- license: mit tags: - generated_from_keras_callback model-index: - name: lizaboiarchuk/tiny-rubert-war-finetuned 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. --> # lizaboiarchuk/tiny-rubert-war-finetuned This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on an unknown dataset. It achieves the following results on the evaluation set: - Train Loss: 3.7630 - Validation Loss: 3.4797 - Epoch: 4 ## 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': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -787, '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 \| Validation Loss \| Epoch \| \|:----------:\|:---------------:\|:-----:\| \| 4.1307 \| 3.7059 \| 0 \| \| 4.0402 \| 3.6937 \| 1 \| \| 3.9512 \| 3.5754 \| 2 \| \| 3.8665 \| 3.4710 \| 3 \| \| 3.7630 \| 3.4797 \| 4 \| ### Framework versions - Transformers 4.22.1 - TensorFlow 2.8.2 - Datasets 2.5.1 - Tokenizers 0.12.1
Chemsseddine/distilbert-base-uncased-finetuned-cola	Chemsseddine	2022-09-22T15:31:00Z	103	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-07T17:23:46Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-cola 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-cola This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.0011 ## 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: 5 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 5 \| 2.1485 \| \| No log \| 2.0 \| 10 \| 2.0983 \| \| No log \| 3.0 \| 15 \| 2.0499 \| \| No log \| 4.0 \| 20 \| 2.0155 \| \| No log \| 5.0 \| 25 \| 2.0011 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
m-lin20/satellite-instrument-bert-NER	m-lin20	2022-09-22T13:32:42Z	104	2	transformers	[ "transformers", "pytorch", "bert", "token-classification", "pt", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- language: "pt" widget: - text: "Poised for launch in mid-2021, the joint NASA-USGS Landsat 9 mission will continue this important data record. In many respects Landsat 9 is a clone of Landsat-8. The Operational Land Imager-2 (OLI-2) is largely identical to Landsat 8 OLI, providing calibrated imagery covering the solar reflected wavelengths. The Thermal Infrared Sensor-2 (TIRS-2) improves upon Landsat 8 TIRS, addressing known issues including stray light incursion and a malfunction of the instrument scene select mirror. In addition, Landsat 9 adds redundancy to TIRS-2, thus upgrading the instrument to a 5-year design life commensurate with other elements of the mission. Initial performance testing of OLI-2 and TIRS-2 indicate that the instruments are of excellent quality and expected to match or improve on Landsat 8 data quality. " example_title: "example 1" - text: "Compared to its predecessor, Jason-3, the two AMR-C radiometer instruments have an external calibration system which enables higher radiometric stability accomplished by moving the secondary mirror between well-defined targets. Sentinel-6 allows continuing the study of the ocean circulation, climate change, and sea-level rise for at least another decade. Besides the external calibration for the AMR heritage radiometer (18.7, 23.8, and 34 GHz channels), the AMR-C contains a high-resolution microwave radiometer (HRMR) with radiometer channels at 90, 130, and 168 GHz. This subsystem allows for a factor of 5× higher spatial resolution at coastal transitions. This article presents a brief description of the instrument and the measured performance of the completed AMR-C-A and AMR-C-B instruments." example_title: "example 2" - text: "Landsat 9 will continue the Landsat data record into its fifth decade with a near-copy build of Landsat 8 with launch scheduled for December 2020. The two instruments on Landsat 9 are Thermal Infrared Sensor-2 (TIRS-2) and Operational Land Imager-2 (OLI-2)." example_title: "example 3" inference: parameters: aggregation_strategy: "first" --- # satellite-instrument-bert-NER For details, please visit the [GitHub link](https://github.com/THU-EarthInformationScienceLab/Satellite-Instrument-NER). ## Citation Our [paper](https://www.tandfonline.com/doi/full/10.1080/17538947.2022.2107098) has been published in the International Journal of Digital Earth : ```bibtex @article{lin2022satellite, title={Satellite and instrument entity recognition using a pre-trained language model with distant supervision}, author={Lin, Ming and Jin, Meng and Liu, Yufu and Bai, Yuqi}, journal={International Journal of Digital Earth}, volume={15}, number={1}, pages={1290--1304}, year={2022}, publisher={Taylor \& Francis} } ```
fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic	fxmarty	2022-09-22T13:28:21Z	3	0	transformers	[ "transformers", "onnx", "distilbert", "text-classification", "dataset:sst2", "dataset:glue", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-22T13:19:36Z	--- license: apache-2.0 datasets: - sst2 - glue --- This model is a fork of https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english , quantized using dynamic Post-Training Quantization (PTQ) with ONNX Runtime and 🤗 Optimum library. It achieves 0.901 on the validation set. To load this model: ```python from optimum.onnxruntime import ORTModelForSequenceClassification model = ORTModelForSequenceClassification.from_pretrained("fxmarty/distilbert-base-uncased-finetuned-sst-2-english-int8-dynamic") ```
rram12/ML-agents_pyramids	rram12	2022-09-22T12:22:53Z	15	0	ml-agents	[ "ml-agents", "tensorboard", "onnx", "unity-ml-agents", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids", "region:us" ]	reinforcement-learning	2022-09-22T12:22:48Z	--- tags: - unity-ml-agents - ml-agents - deep-reinforcement-learning - reinforcement-learning - ML-Agents-Pyramids library_name: ml-agents --- # ppo Agent playing Pyramids This is a trained model of a ppo agent playing Pyramids using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents). ## Usage (with ML-Agents) The Documentation: https://github.com/huggingface/ml-agents#get-started We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub: ### Resume the training ``` mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume ``` ### Watch your Agent play You can watch your agent playing directly in your browser:. 1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids 2. Step 1: Write your model_id: rram12/ML-agents_pyramids 3. Step 2: Select your .nn /.onnx file 4. Click on Watch the agent play 👀
sd-concepts-library/bluebey-2	sd-concepts-library	2022-09-22T12:21:34Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T12:21:30Z	--- license: mit --- ### Bluebey-2 on Stable Diffusion This is the `<bluebey>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<bluebey> 0](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/0.jpeg) ![<bluebey> 1](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/2.jpeg) ![<bluebey> 2](https://huggingface.co/sd-concepts-library/bluebey-2/resolve/main/concept_images/1.jpeg)
muhtasham/bert-small-finetuned-finer-longer10	muhtasham	2022-09-22T11:51:56Z	178	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-25T21:50:21Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finetuned-finer-longer10 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-small-finetuned-finetuned-finer-longer10 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-finer](https://huggingface.co/muhtasham/bert-small-finetuned-finer) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.3791 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 7 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.5687 \| 1.0 \| 2433 \| 1.5357 \| \| 1.5081 \| 2.0 \| 4866 \| 1.4759 \| \| 1.4813 \| 3.0 \| 7299 \| 1.4337 \| \| 1.4453 \| 4.0 \| 9732 \| 1.4084 \| \| 1.4257 \| 5.0 \| 12165 \| 1.3913 \| \| 1.4155 \| 6.0 \| 14598 \| 1.3855 \| \| 1.4057 \| 7.0 \| 17031 \| 1.3791 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-small-finetuned-parsed20	muhtasham	2022-09-22T11:34:48Z	179	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-17T13:31:40Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-parsed20 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-small-finetuned-parsed20 This model is a fine-tuned version of [google/bert_uncased_L-4_H-512_A-8](https://huggingface.co/google/bert_uncased_L-4_H-512_A-8) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.1193 ## 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: 128 - eval_batch_size: 8 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 3.0763 \| \| No log \| 2.0 \| 8 \| 2.8723 \| \| No log \| 3.0 \| 12 \| 3.5102 \| \| No log \| 4.0 \| 16 \| 2.8641 \| \| No log \| 5.0 \| 20 \| 2.7827 \| \| No log \| 6.0 \| 24 \| 2.8163 \| \| No log \| 7.0 \| 28 \| 3.2415 \| \| No log \| 8.0 \| 32 \| 3.0477 \| \| No log \| 9.0 \| 36 \| 3.5160 \| \| No log \| 10.0 \| 40 \| 3.1248 \| \| No log \| 11.0 \| 44 \| 3.2159 \| \| No log \| 12.0 \| 48 \| 3.2177 \| \| No log \| 13.0 \| 52 \| 2.9108 \| \| No log \| 14.0 \| 56 \| 3.3758 \| \| No log \| 15.0 \| 60 \| 3.1335 \| \| No log \| 16.0 \| 64 \| 2.9753 \| \| No log \| 17.0 \| 68 \| 2.9922 \| \| No log \| 18.0 \| 72 \| 3.2798 \| \| No log \| 19.0 \| 76 \| 2.7280 \| \| No log \| 20.0 \| 80 \| 3.1193 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
muhtasham/bert-small-finetuned-parsed-longer50	muhtasham	2022-09-22T11:34:27Z	179	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "fill-mask", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-08-17T13:39:01Z	--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-small-finetuned-finetuned-parsed-longer50 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-small-finetuned-finetuned-parsed-longer50 This model is a fine-tuned version of [muhtasham/bert-small-finetuned-parsed20](https://huggingface.co/muhtasham/bert-small-finetuned-parsed20) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.9278 ## 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: 128 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 30 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 4 \| 2.9807 \| \| No log \| 2.0 \| 8 \| 2.7267 \| \| No log \| 3.0 \| 12 \| 3.3484 \| \| No log \| 4.0 \| 16 \| 2.7573 \| \| No log \| 5.0 \| 20 \| 2.7063 \| \| No log \| 6.0 \| 24 \| 2.7353 \| \| No log \| 7.0 \| 28 \| 3.1290 \| \| No log \| 8.0 \| 32 \| 2.9371 \| \| No log \| 9.0 \| 36 \| 3.4265 \| \| No log \| 10.0 \| 40 \| 3.0537 \| \| No log \| 11.0 \| 44 \| 3.1382 \| \| No log \| 12.0 \| 48 \| 3.1454 \| \| No log \| 13.0 \| 52 \| 2.8379 \| \| No log \| 14.0 \| 56 \| 3.2760 \| \| No log \| 15.0 \| 60 \| 3.0504 \| \| No log \| 16.0 \| 64 \| 2.9001 \| \| No log \| 17.0 \| 68 \| 2.8892 \| \| No log \| 18.0 \| 72 \| 3.1837 \| \| No log \| 19.0 \| 76 \| 2.6404 \| \| No log \| 20.0 \| 80 \| 3.0600 \| \| No log \| 21.0 \| 84 \| 3.1432 \| \| No log \| 22.0 \| 88 \| 2.9608 \| \| No log \| 23.0 \| 92 \| 3.0513 \| \| No log \| 24.0 \| 96 \| 3.1038 \| \| No log \| 25.0 \| 100 \| 3.0975 \| \| No log \| 26.0 \| 104 \| 2.8977 \| \| No log \| 27.0 \| 108 \| 2.9416 \| \| No log \| 28.0 \| 112 \| 2.9015 \| \| No log \| 29.0 \| 116 \| 2.7947 \| \| No log \| 30.0 \| 120 \| 2.9278 \| ### Framework versions - Transformers 4.21.1 - Pytorch 1.12.1+cu113 - Datasets 2.4.0 - Tokenizers 0.12.1
sherover125/newsclassifier	sherover125	2022-09-22T10:46:34Z	118	0	transformers	[ "transformers", "pytorch", "tensorboard", "bert", "text-classification", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-06-22T17:28:45Z	--- license: apache-2.0 tags: - generated_from_trainer metrics: - matthews_correlation model-index: - name: newsclassifier 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. --> # newsclassifier This model is a fine-tuned version of [HooshvareLab/bert-fa-zwnj-base](https://huggingface.co/HooshvareLab/bert-fa-zwnj-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1405 - Matthews Correlation: 0.9731 ## 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: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Matthews Correlation \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------------------:\| \| 0.2207 \| 1.0 \| 2397 \| 0.1706 \| 0.9595 \| \| 0.0817 \| 2.0 \| 4794 \| 0.1505 \| 0.9663 \| \| 0.0235 \| 3.0 \| 7191 \| 0.1405 \| 0.9731 \| ### Framework versions - Transformers 4.20.1 - Pytorch 1.12.0+cu113 - Datasets 2.3.2 - Tokenizers 0.12.1
mahaveer/ppo-LunarLander-v2	mahaveer	2022-09-22T10:11:19Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-22T09:57:48Z	--- library_name: stable-baselines3 tags: - LunarLander-v2 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: PPO results: - metrics: - type: mean_reward value: 194.40 +/- 31.46 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: LunarLander-v2 type: LunarLander-v2 --- # 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 ... ```
GItaf/roberta-base-roberta-base-TF-weight1-epoch10	GItaf	2022-09-22T09:35:57Z	49	0	transformers	[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-22T09:34:27Z	--- license: mit tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch10 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. --> # roberta-base-roberta-base-TF-weight1-epoch10 This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-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: 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: 10 ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/roberta-base-roberta-base-TF-weight1-epoch15	GItaf	2022-09-22T09:23:00Z	6	0	transformers	[ "transformers", "pytorch", "roberta", "text-generation", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-21T15:32:11Z	--- tags: - generated_from_trainer model-index: - name: roberta-base-roberta-base-TF-weight1-epoch15 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. --> # roberta-base-roberta-base-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 4.8322 - Cls loss: 0.6900 - Lm loss: 4.1423 - Cls Accuracy: 0.5401 - Cls F1: 0.3788 - Cls Precision: 0.2917 - Cls Recall: 0.5401 - Perplexity: 62.95 ## 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: 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:--------:\|:-------:\|:------------:\|:------:\|:-------------:\|:----------:\|:----------:\| \| 5.3158 \| 1.0 \| 3470 \| 4.9858 \| 0.6910 \| 4.2949 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 73.32 \| \| 4.9772 \| 2.0 \| 6940 \| 4.8876 \| 0.6956 \| 4.1920 \| 0.4599 \| 0.2898 \| 0.2115 \| 0.4599 \| 66.15 \| \| 4.8404 \| 3.0 \| 10410 \| 4.8454 \| 0.6901 \| 4.1553 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 63.77 \| \| 4.7439 \| 4.0 \| 13880 \| 4.8177 \| 0.6904 \| 4.1274 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.02 \| \| 4.6667 \| 5.0 \| 17350 \| 4.8065 \| 0.6903 \| 4.1163 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.33 \| \| 4.6018 \| 6.0 \| 20820 \| 4.8081 \| 0.6963 \| 4.1119 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.06 \| \| 4.5447 \| 7.0 \| 24290 \| 4.8089 \| 0.6912 \| 4.1177 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.42 \| \| 4.4944 \| 8.0 \| 27760 \| 4.8128 \| 0.6900 \| 4.1228 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.73 \| \| 4.4505 \| 9.0 \| 31230 \| 4.8152 \| 0.6905 \| 4.1248 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.85 \| \| 4.4116 \| 10.0 \| 34700 \| 4.8129 \| 0.6908 \| 4.1221 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.69 \| \| 4.3787 \| 11.0 \| 38170 \| 4.8146 \| 0.6906 \| 4.1241 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 61.81 \| \| 4.3494 \| 12.0 \| 41640 \| 4.8229 \| 0.6900 \| 4.1329 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.36 \| \| 4.3253 \| 13.0 \| 45110 \| 4.8287 \| 0.6900 \| 4.1388 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.73 \| \| 4.3075 \| 14.0 \| 48580 \| 4.8247 \| 0.6900 \| 4.1347 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.47 \| \| 4.2936 \| 15.0 \| 52050 \| 4.8322 \| 0.6900 \| 4.1423 \| 0.5401 \| 0.3788 \| 0.2917 \| 0.5401 \| 62.95 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
GItaf/gpt2-gpt2-TF-weight1-epoch15	GItaf	2022-09-22T09:21:30Z	4	0	transformers	[ "transformers", "pytorch", "gpt2", "text-generation", "generated_from_trainer", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2022-09-21T15:31:41Z	--- tags: - generated_from_trainer model-index: - name: gpt2-gpt2-TF-weight1-epoch15 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. --> # gpt2-gpt2-TF-weight1-epoch15 This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.0647 - Cls loss: 2.1295 - Lm loss: 3.9339 - Cls Accuracy: 0.8375 - Cls F1: 0.8368 - Cls Precision: 0.8381 - Cls Recall: 0.8375 - Perplexity: 51.11 ## 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: 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: 15 ### Training results \| Training Loss \| Epoch \| Step \| Cls loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Lm loss \| Perplexity \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:--------:\|:------------:\|:------:\|:-------------:\|:----------:\|:-------:\|:----------:\|:---------------:\| \| 4.8702 \| 1.0 \| 3470 \| 0.6951 \| 0.7752 \| 0.7670 \| 0.7978 \| 0.7752 \| 4.0201 \| 55.71 \| 4.7157 \| \| 4.5856 \| 2.0 \| 6940 \| 0.6797 \| 0.8352 \| 0.8333 \| 0.8406 \| 0.8352 \| 3.9868 \| 53.88 \| 4.6669 \| \| 4.4147 \| 3.0 \| 10410 \| 0.6899 \| 0.8375 \| 0.8368 \| 0.8384 \| 0.8375 \| 3.9716 \| 53.07 \| 4.6619 \| \| 4.2479 \| 4.0 \| 13880 \| 0.8678 \| 0.8403 \| 0.8396 \| 0.8413 \| 0.8403 \| 3.9622 \| 52.57 \| 4.8305 \| \| 4.1281 \| 5.0 \| 17350 \| 0.9747 \| 0.8340 \| 0.8334 \| 0.8346 \| 0.8340 \| 3.9596 \| 52.44 \| 4.9349 \| \| Training Loss \| Epoch \| Step \| Validation Loss \| Cls loss \| Lm loss \| Cls Accuracy \| Cls F1 \| Cls Precision \| Cls Recall \| Perplexity\| \|:-------------:\|:-----:\|:-----:\|:--------:\|:------------:\|:------:\|:-------------:\|:----------:\|:-------:\|:----------:\|:---------------:\| \| 4.195 \| 6.0 \| 20820 \| 4.9303 \| 0.9770 \| 3.9528 \| 0.8300 \| 0.8299 \| 0.8299 \| 0.8300 \| 52.08 \| \| 4.0645 \| 7.0 \| 24290 \| 5.0425 \| 1.0979 \| 3.9440 \| 0.8317 \| 0.8313 \| 0.8317 \| 0.8317 \| 51.62 \| \| 3.9637 \| 8.0 \| 27760 \| 5.3955 \| 1.4533 \| 3.9414 \| 0.8329 \| 0.8325 \| 0.8328 \| 0.8329 \| 51.49 \| \| 3.9094 \| 9.0 \| 31230 \| 5.6029 \| 1.6645 \| 3.9375 \| 0.8231 \| 0.8233 \| 0.8277 \| 0.8231 \| 51.29 \| \| 3.8661 \| 10.0 \| 34700 \| 5.8175 \| 1.8821 \| 3.9344 \| 0.8144 \| 0.8115 \| 0.8222 \| 0.8144 \| 51.13 \| \| 3.8357 \| 11.0 \| 38170 \| 5.6824 \| 1.7494 \| 3.9319 \| 0.8340 \| 0.8336 \| 0.8342 \| 0.8340 \| 51.01 \| \| 3.8019 \| 12.0 \| 41640 \| 5.8509 \| 1.9167 \| 3.9332 \| 0.8369 \| 0.8357 \| 0.8396 \| 0.8369 \| 51.07 \| \| 3.7815 \| 13.0 \| 45110 \| 5.9044 \| 1.9686 \| 3.9346 \| 0.8409 \| 0.8407 \| 0.8408 \| 0.8409 \| 51.14 \| \| 3.7662 \| 14.0 \| 48580 \| 6.0088 \| 2.0738 \| 3.9337 \| 0.8363 \| 0.8359 \| 0.8364 \| 0.8363 \| 51.10 \| \| 3.7524 \| 15.0 \| 52050 \| 6.0647 \| 2.1295 \| 3.9339 \| 0.8375 \| 0.8368 \| 0.8381 \| 0.8375 \| 51.11 \| ### Framework versions - Transformers 4.21.2 - Pytorch 1.12.1 - Datasets 2.4.0 - Tokenizers 0.12.1
chintagunta85/electramed-small-deid2014-ner-v5-classweights	chintagunta85	2022-09-22T09:08:27Z	102	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:i2b22014", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-22T07:48:30Z	--- tags: - generated_from_trainer datasets: - i2b22014 metrics: - precision - recall - f1 - accuracy model-index: - name: electramed-small-deid2014-ner-v5-classweights results: - task: name: Token Classification type: token-classification dataset: name: i2b22014 type: i2b22014 config: i2b22014-deid split: train args: i2b22014-deid metrics: - name: Precision type: precision value: 0.8832236842105263 - name: Recall type: recall value: 0.6910561632502987 - name: F1 type: f1 value: 0.7754112732711052 - name: Accuracy type: accuracy value: 0.9883040491052534 --- <!-- 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. --> # electramed-small-deid2014-ner-v5-classweights This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. It achieves the following results on the evaluation set: - Loss: 0.0009 - Precision: 0.8832 - Recall: 0.6911 - F1: 0.7754 - Accuracy: 0.9883 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0001 \| 1.0 \| 1838 \| 0.0008 \| 0.7702 \| 0.3780 \| 0.5071 \| 0.9771 \| \| 0.0 \| 2.0 \| 3676 \| 0.0007 \| 0.8753 \| 0.5671 \| 0.6883 \| 0.9827 \| \| 0.0 \| 3.0 \| 5514 \| 0.0006 \| 0.8074 \| 0.4128 \| 0.5463 \| 0.9775 \| \| 0.0 \| 4.0 \| 7352 \| 0.0007 \| 0.8693 \| 0.6102 \| 0.7170 \| 0.9848 \| \| 0.0 \| 5.0 \| 9190 \| 0.0006 \| 0.8710 \| 0.6022 \| 0.7121 \| 0.9849 \| \| 0.0 \| 6.0 \| 11028 \| 0.0007 \| 0.8835 \| 0.6547 \| 0.7521 \| 0.9867 \| \| 0.0 \| 7.0 \| 12866 \| 0.0009 \| 0.8793 \| 0.6661 \| 0.7579 \| 0.9873 \| \| 0.0 \| 8.0 \| 14704 \| 0.0008 \| 0.8815 \| 0.6740 \| 0.7639 \| 0.9876 \| \| 0.0 \| 9.0 \| 16542 \| 0.0009 \| 0.8812 \| 0.6851 \| 0.7709 \| 0.9880 \| \| 0.0 \| 10.0 \| 18380 \| 0.0009 \| 0.8832 \| 0.6911 \| 0.7754 \| 0.9883 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
prakashkmr48/Prompt-image-inpainting	prakashkmr48	2022-09-22T08:58:57Z	0	0	null	[ "region:us" ]	null	2022-09-22T08:51:46Z	git lfs install git clone https://huggingface.co/prakashkmr48/Prompt-image-inpainting
Hoax0930/kyoto_marian	Hoax0930	2022-09-22T08:32:43Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "marian", "text2text-generation", "translation", "generated_from_trainer", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	translation	2022-09-22T07:47:04Z	--- license: apache-2.0 tags: - translation - generated_from_trainer metrics: - bleu model-index: - name: kyoto_marian 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. --> # kyoto_marian This model is a fine-tuned version of [Helsinki-NLP/opus-tatoeba-en-ja](https://huggingface.co/Helsinki-NLP/opus-tatoeba-en-ja) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 2.1941 - Bleu: 13.4500 ## 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: 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: 6 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/ghostproject-men	sd-concepts-library	2022-09-22T07:36:08Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-09-22T07:36:02Z	--- license: mit --- ### ghostproject-men on Stable Diffusion This is the `<ghostsproject-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<ghostsproject-style> 0](https://huggingface.co/sd-concepts-library/ghostproject-men/resolve/main/concept_images/0.jpeg) ![<ghostsproject-style> 1](https://huggingface.co/sd-concepts-library/ghostproject-men/resolve/main/concept_images/3.jpeg) ![<ghostsproject-style> 2](https://huggingface.co/sd-concepts-library/ghostproject-men/resolve/main/concept_images/2.jpeg) ![<ghostsproject-style> 3](https://huggingface.co/sd-concepts-library/ghostproject-men/resolve/main/concept_images/1.jpeg)
sd-concepts-library/pool-test	sd-concepts-library	2022-09-22T06:53:48Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T06:53:43Z	--- license: mit --- ### Pool test on Stable Diffusion This is the `<pool_test>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<pool_test> 0](https://huggingface.co/sd-concepts-library/pool-test/resolve/main/concept_images/0.jpeg) ![<pool_test> 1](https://huggingface.co/sd-concepts-library/pool-test/resolve/main/concept_images/3.jpeg) ![<pool_test> 2](https://huggingface.co/sd-concepts-library/pool-test/resolve/main/concept_images/2.jpeg) ![<pool_test> 3](https://huggingface.co/sd-concepts-library/pool-test/resolve/main/concept_images/1.jpeg)
chintagunta85/electramed-small-deid2014-ner-v4	chintagunta85	2022-09-22T06:33:10Z	104	0	transformers	[ "transformers", "pytorch", "tensorboard", "electra", "token-classification", "generated_from_trainer", "dataset:i2b22014", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2022-09-22T05:55:58Z	--- tags: - generated_from_trainer datasets: - i2b22014 metrics: - precision - recall - f1 - accuracy model-index: - name: electramed-small-deid2014-ner-v4 results: - task: name: Token Classification type: token-classification dataset: name: i2b22014 type: i2b22014 config: i2b22014-deid split: train args: i2b22014-deid metrics: - name: Precision type: precision value: 0.7571112095702259 - name: Recall type: recall value: 0.7853663020498207 - name: F1 type: f1 value: 0.770979967514889 - name: Accuracy type: accuracy value: 0.9906153616114308 --- <!-- 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. --> # electramed-small-deid2014-ner-v4 This model is a fine-tuned version of [giacomomiolo/electramed_small_scivocab](https://huggingface.co/giacomomiolo/electramed_small_scivocab) on the i2b22014 dataset. It achieves the following results on the evaluation set: - Loss: 0.0362 - Precision: 0.7571 - Recall: 0.7854 - F1: 0.7710 - Accuracy: 0.9906 ## 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 \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.0143 \| 1.0 \| 1838 \| 0.1451 \| 0.3136 \| 0.3463 \| 0.3291 \| 0.9700 \| \| 0.0033 \| 2.0 \| 3676 \| 0.0940 \| 0.4293 \| 0.4861 \| 0.4559 \| 0.9758 \| \| 0.0014 \| 3.0 \| 5514 \| 0.0725 \| 0.4906 \| 0.5766 \| 0.5301 \| 0.9799 \| \| 0.0007 \| 4.0 \| 7352 \| 0.0568 \| 0.6824 \| 0.7022 \| 0.6921 \| 0.9860 \| \| 0.0112 \| 5.0 \| 9190 \| 0.0497 \| 0.6966 \| 0.7400 \| 0.7177 \| 0.9870 \| \| 0.0002 \| 6.0 \| 11028 \| 0.0442 \| 0.7126 \| 0.7549 \| 0.7332 \| 0.9878 \| \| 0.0002 \| 7.0 \| 12866 \| 0.0404 \| 0.7581 \| 0.7591 \| 0.7586 \| 0.9896 \| \| 0.0002 \| 8.0 \| 14704 \| 0.0376 \| 0.7540 \| 0.7804 \| 0.7670 \| 0.9904 \| \| 0.0002 \| 9.0 \| 16542 \| 0.0367 \| 0.7548 \| 0.7825 \| 0.7684 \| 0.9905 \| \| 0.0001 \| 10.0 \| 18380 \| 0.0362 \| 0.7571 \| 0.7854 \| 0.7710 \| 0.9906 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/test2	sd-concepts-library	2022-09-22T06:29:49Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T06:29:45Z	--- license: mit --- ### TEST2 on Stable Diffusion This is the `<AIOCARD>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<AIOCARD> 0](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027D.jpg) ![<AIOCARD> 1](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027C.jpg) ![<AIOCARD> 2](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100282.jpg) ![<AIOCARD> 3](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027A.jpg) ![<AIOCARD> 4](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027B.jpg) ![<AIOCARD> 5](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100281.jpg) ![<AIOCARD> 6](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100280.jpg) ![<AIOCARD> 7](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027E.jpg) ![<AIOCARD> 8](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/00100279.jpg) ![<AIOCARD> 9](https://huggingface.co/sd-concepts-library/test2/resolve/main/concept_images/0010027F.jpg)
sd-concepts-library/bee	sd-concepts-library	2022-09-22T05:01:07Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-09-22T05:00:56Z	--- license: mit --- ### BEE on Stable Diffusion This is the `<b-e-e>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<b-e-e> 0](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/0.jpeg) ![<b-e-e> 1](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/3.jpeg) ![<b-e-e> 2](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/2.jpeg) ![<b-e-e> 3](https://huggingface.co/sd-concepts-library/bee/resolve/main/concept_images/1.jpeg)
sd-concepts-library/yinit	sd-concepts-library	2022-09-22T04:58:38Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T04:58:24Z	--- license: mit --- ### yinit on Stable Diffusion This is the `yinit-dropcap` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![yinit-dropcap 0](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/4.jpeg) ![yinit-dropcap 1](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/12.jpeg) ![yinit-dropcap 2](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/8.jpeg) ![yinit-dropcap 3](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/0.jpeg) ![yinit-dropcap 4](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/6.jpeg) ![yinit-dropcap 5](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/3.jpeg) ![yinit-dropcap 6](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/20.jpeg) ![yinit-dropcap 7](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/11.jpeg) ![yinit-dropcap 8](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/19.jpeg) ![yinit-dropcap 9](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/24.jpeg) ![yinit-dropcap 10](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/17.jpeg) ![yinit-dropcap 11](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/10.jpeg) ![yinit-dropcap 12](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/7.jpeg) ![yinit-dropcap 13](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/13.jpeg) ![yinit-dropcap 14](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/16.jpeg) ![yinit-dropcap 15](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/2.jpeg) ![yinit-dropcap 16](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/25.jpeg) ![yinit-dropcap 17](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/22.jpeg) ![yinit-dropcap 18](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/9.jpeg) ![yinit-dropcap 19](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/15.jpeg) ![yinit-dropcap 20](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/21.jpeg) ![yinit-dropcap 21](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/1.jpeg) ![yinit-dropcap 22](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/14.jpeg) ![yinit-dropcap 23](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/5.jpeg) ![yinit-dropcap 24](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/18.jpeg) ![yinit-dropcap 25](https://huggingface.co/sd-concepts-library/yinit/resolve/main/concept_images/23.jpeg)
sd-concepts-library/million-live-spade-q-object-3k	sd-concepts-library	2022-09-22T04:34:40Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T04:34:30Z	--- license: mit --- ### million-live-spade-q-object-3k on Stable Diffusion This is the `<spade_q>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<spade_q> 0](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/0.png) ![<spade_q> 1](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/1.png) ![<spade_q> 2](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/2.png) ![<spade_q> 3](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/3.png) ![<spade_q> 4](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/4.png) ![<spade_q> 5](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/5.png) ![<spade_q> 6](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/6.png) ![<spade_q> 7](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/7.png) ![<spade_q> 8](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/8.png) ![<spade_q> 9](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/9.png) ![<spade_q> 10](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/10.png) ![<spade_q> 11](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/11.png) ![<spade_q> 12](https://huggingface.co/sd-concepts-library/million-live-spade-q-object-3k/resolve/main/concept_images/12.png)
sd-concepts-library/million-live-akane-shifuku-3k	sd-concepts-library	2022-09-22T03:28:33Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-22T03:28:22Z	--- license: mit --- ### million-live-akane-shifuku-3k on Stable Diffusion This is the `<akane>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<akane> 0](https://huggingface.co/sd-concepts-library/million-live-akane-shifuku-3k/resolve/main/concept_images/0.png) ![<akane> 1](https://huggingface.co/sd-concepts-library/million-live-akane-shifuku-3k/resolve/main/concept_images/1.png) ![<akane> 2](https://huggingface.co/sd-concepts-library/million-live-akane-shifuku-3k/resolve/main/concept_images/2.png) ![<akane> 3](https://huggingface.co/sd-concepts-library/million-live-akane-shifuku-3k/resolve/main/concept_images/3.png)
ashiqabdulkhader/GPT2-Poet	ashiqabdulkhader	2022-09-22T03:24:00Z	381	3	transformers	[ "transformers", "tf", "gpt2", "text-generation", "license:bigscience-bloom-rail-1.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2022-09-22T02:45:20Z	--- license: bigscience-bloom-rail-1.0 widget : - text: "I used to have a lover" example_title: "I used to have a lover" - text : "The old cupola glinted above the clouds" example_title: "The old cupola" - text : "Behind the silo, the Mother Rabbit hunches" example_title : "Behind the silo" --- # GPT2-Poet ## Model description GPT2-Poet is a GPT-2 transformer model fine Tuned on a large corpus of English Poems dataset in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way with an automatic process to generate inputs and labels from those texts. More precisely, it was trained to guess the next word in sentences. More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence, shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the predictions for the token i only uses the inputs from 1 to i but not the future tokens. This way, the model learns an inner representation of the English language that can then be used to extract features useful for downstream tasks. ## Intended uses & limitations You can use the raw model for text generation or fine-tune it to a downstream task. See the [model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you. ## Usage You can use this model for English Poem generation: ```python >>> from transformers import TFGPT2LMHeadModel, GPT2Tokenizer >>> tokenizer = GPT2Tokenizer.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> model = TFGPT2LMHeadModel.from_pretrained("ashiqabdulkhader/GPT2-Poet") >>> text = "The quick brown fox" >>> input_ids = tokenizer.encode(text, return_tensors='tf') >>> sample_outputs = model.generate( input_ids, do_sample=True, max_length=100, top_k=0, top_p=0.9, temperature=1.0, num_return_sequences=3 ) >>> print("Output:", tokenizer.decode(sample_outputs[0], skip_special_tokens=True)) ```
yuntian-deng/latex2im_ss	yuntian-deng	2022-09-22T02:20:24Z	1	0	diffusers	[ "diffusers", "en", "dataset:yuntian-deng/im2latex-100k", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-22T02:19:32Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: yuntian-deng/im2latex-100k 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. --> # latex2im_ss ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `yuntian-deng/im2latex-100k` 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: no ### Training results 📈 [TensorBoard logs](https://huggingface.co/yuntian-deng/latex2im_ss/tensorboard?#scalars)
g30rv17ys/ddpm-geeve-drusen-2000-128	g30rv17ys	2022-09-22T01:53:45Z	1	0	diffusers	[ "diffusers", "tensorboard", "en", "dataset:imagefolder", "license:apache-2.0", "diffusers:DDPMPipeline", "region:us" ]	null	2022-09-21T18:22:01Z	--- language: en license: apache-2.0 library_name: diffusers tags: [] datasets: imagefolder metrics: [] --- <!-- This model card has been generated automatically according to the information the training script had access to. You should probably proofread and complete it, then remove this comment. --> # ddpm-geeve-drusen-2000-128 ## Model description This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library on the `imagefolder` dataset. ## Intended uses & limitations #### How to use ```python # TODO: add an example code snippet for running this diffusion pipeline ``` #### Limitations and bias [TODO: provide examples of latent issues and potential remediations] ## Training data [TODO: describe the data used to train the model] ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 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/geevegeorge/ddpm-geeve-drusen-2000-128/tensorboard?#scalars)
hwangt/donut-base-sroie	hwangt	2022-09-22T01:45:38Z	45	0	transformers	[ "transformers", "pytorch", "tensorboard", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2022-09-22T01:10:38Z	--- license: mit tags: - generated_from_trainer datasets: - imagefolder model-index: - name: donut-base-sroie 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. --> # donut-base-sroie This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder 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: 2 - 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 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.0+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
sd-concepts-library/gba-pokemon-sprites	sd-concepts-library	2022-09-22T00:48:32Z	0	30	null	[ "license:mit", "region:us" ]	null	2022-09-22T00:48:25Z	--- license: mit --- ### GBA Pokemon Sprites on Stable Diffusion This is the `<GBA-Poke-Sprites>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<GBA-Poke-Sprites> 0](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/340.jpeg) ![<GBA-Poke-Sprites> 1](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/48.jpeg) ![<GBA-Poke-Sprites> 2](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/72.jpeg) ![<GBA-Poke-Sprites> 3](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/245.jpeg) ![<GBA-Poke-Sprites> 4](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/229.jpeg) ![<GBA-Poke-Sprites> 5](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/238.jpeg) ![<GBA-Poke-Sprites> 6](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/128.jpeg) ![<GBA-Poke-Sprites> 7](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/150.jpeg) ![<GBA-Poke-Sprites> 8](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/275.jpeg) ![<GBA-Poke-Sprites> 9](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/117.jpeg) ![<GBA-Poke-Sprites> 10](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/326.jpeg) ![<GBA-Poke-Sprites> 11](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/344.jpeg) ![<GBA-Poke-Sprites> 12](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/157.jpeg) ![<GBA-Poke-Sprites> 13](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/385.jpeg) ![<GBA-Poke-Sprites> 14](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/58.jpeg) ![<GBA-Poke-Sprites> 15](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/286.jpeg) ![<GBA-Poke-Sprites> 16](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/319.jpeg) ![<GBA-Poke-Sprites> 17](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/347.jpeg) ![<GBA-Poke-Sprites> 18](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/383.jpeg) ![<GBA-Poke-Sprites> 19](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/213.jpeg) ![<GBA-Poke-Sprites> 20](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/380.jpeg) ![<GBA-Poke-Sprites> 21](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/96.jpeg) ![<GBA-Poke-Sprites> 22](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/139.jpeg) ![<GBA-Poke-Sprites> 23](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/131.jpeg) ![<GBA-Poke-Sprites> 24](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/262.jpeg) ![<GBA-Poke-Sprites> 25](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/134.jpeg) ![<GBA-Poke-Sprites> 26](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/53.jpeg) ![<GBA-Poke-Sprites> 27](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/83.jpeg) ![<GBA-Poke-Sprites> 28](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/165.jpeg) ![<GBA-Poke-Sprites> 29](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/79.jpeg) ![<GBA-Poke-Sprites> 30](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/151.jpeg) ![<GBA-Poke-Sprites> 31](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/308.jpeg) ![<GBA-Poke-Sprites> 32](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/292.jpeg) ![<GBA-Poke-Sprites> 33](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/112.jpeg) ![<GBA-Poke-Sprites> 34](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/4.jpeg) ![<GBA-Poke-Sprites> 35](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/12.jpeg) ![<GBA-Poke-Sprites> 36](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/285.jpeg) ![<GBA-Poke-Sprites> 37](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/204.jpeg) ![<GBA-Poke-Sprites> 38](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/226.jpeg) ![<GBA-Poke-Sprites> 39](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/298.jpeg) ![<GBA-Poke-Sprites> 40](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/140.jpeg) ![<GBA-Poke-Sprites> 41](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/59.jpeg) ![<GBA-Poke-Sprites> 42](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/180.jpeg) ![<GBA-Poke-Sprites> 43](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/320.jpeg) ![<GBA-Poke-Sprites> 44](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/156.jpeg) ![<GBA-Poke-Sprites> 45](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/8.jpeg) ![<GBA-Poke-Sprites> 46](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/69.jpeg) ![<GBA-Poke-Sprites> 47](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/251.jpeg) ![<GBA-Poke-Sprites> 48](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/337.jpeg) ![<GBA-Poke-Sprites> 49](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/45.jpeg) ![<GBA-Poke-Sprites> 50](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/87.jpeg) ![<GBA-Poke-Sprites> 51](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/203.jpeg) ![<GBA-Poke-Sprites> 52](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/190.jpeg) ![<GBA-Poke-Sprites> 53](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/101.jpeg) ![<GBA-Poke-Sprites> 54](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/234.jpeg) ![<GBA-Poke-Sprites> 55](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/31.jpeg) ![<GBA-Poke-Sprites> 56](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/146.jpeg) ![<GBA-Poke-Sprites> 57](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/228.jpeg) ![<GBA-Poke-Sprites> 58](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/196.jpeg) ![<GBA-Poke-Sprites> 59](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/99.jpeg) ![<GBA-Poke-Sprites> 60](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/331.jpeg) ![<GBA-Poke-Sprites> 61](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/242.jpeg) ![<GBA-Poke-Sprites> 62](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/297.jpeg) ![<GBA-Poke-Sprites> 63](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/271.jpeg) ![<GBA-Poke-Sprites> 64](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/114.jpeg) ![<GBA-Poke-Sprites> 65](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/78.jpeg) ![<GBA-Poke-Sprites> 66](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/71.jpeg) ![<GBA-Poke-Sprites> 67](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/211.jpeg) ![<GBA-Poke-Sprites> 68](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/0.jpeg) ![<GBA-Poke-Sprites> 69](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/175.jpeg) ![<GBA-Poke-Sprites> 70](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/283.jpeg) ![<GBA-Poke-Sprites> 71](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/86.jpeg) ![<GBA-Poke-Sprites> 72](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/6.jpeg) ![<GBA-Poke-Sprites> 73](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/123.jpeg) ![<GBA-Poke-Sprites> 74](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/352.jpeg) ![<GBA-Poke-Sprites> 75](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/113.jpeg) ![<GBA-Poke-Sprites> 76](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/3.jpeg) ![<GBA-Poke-Sprites> 77](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/20.jpeg) ![<GBA-Poke-Sprites> 78](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/11.jpeg) ![<GBA-Poke-Sprites> 79](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/145.jpeg) ![<GBA-Poke-Sprites> 80](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/19.jpeg) ![<GBA-Poke-Sprites> 81](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/287.jpeg) ![<GBA-Poke-Sprites> 82](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/54.jpeg) ![<GBA-Poke-Sprites> 83](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/153.jpeg) ![<GBA-Poke-Sprites> 84](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/274.jpeg) ![<GBA-Poke-Sprites> 85](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/115.jpeg) ![<GBA-Poke-Sprites> 86](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/318.jpeg) ![<GBA-Poke-Sprites> 87](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/67.jpeg) ![<GBA-Poke-Sprites> 88](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/208.jpeg) ![<GBA-Poke-Sprites> 89](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/182.jpeg) ![<GBA-Poke-Sprites> 90](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/356.jpeg) ![<GBA-Poke-Sprites> 91](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/57.jpeg) ![<GBA-Poke-Sprites> 92](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/325.jpeg) ![<GBA-Poke-Sprites> 93](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/51.jpeg) ![<GBA-Poke-Sprites> 94](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/50.jpeg) ![<GBA-Poke-Sprites> 95](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/247.jpeg) ![<GBA-Poke-Sprites> 96](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/268.jpeg) ![<GBA-Poke-Sprites> 97](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/194.jpeg) ![<GBA-Poke-Sprites> 98](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/227.jpeg) ![<GBA-Poke-Sprites> 99](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/93.jpeg) ![<GBA-Poke-Sprites> 100](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/338.jpeg) ![<GBA-Poke-Sprites> 101](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/77.jpeg) ![<GBA-Poke-Sprites> 102](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/256.jpeg) ![<GBA-Poke-Sprites> 103](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/233.jpeg) ![<GBA-Poke-Sprites> 104](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/231.jpeg) 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382](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/277.jpeg) ![<GBA-Poke-Sprites> 383](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/367.jpeg) ![<GBA-Poke-Sprites> 384](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/346.jpeg) ![<GBA-Poke-Sprites> 385](https://huggingface.co/sd-concepts-library/gba-pokemon-sprites/resolve/main/concept_images/334.jpeg)
sd-concepts-library/sherhook-painting-v2	sd-concepts-library	2022-09-22T00:30:50Z	0	4	null	[ "license:mit", "region:us" ]	null	2022-09-22T00:30:44Z	--- license: mit --- ### Sherhook Painting v2 on Stable Diffusion This is the `<sherhook>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<sherhook> 0](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/4.jpeg) ![<sherhook> 1](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/8.jpeg) ![<sherhook> 2](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/0.jpeg) ![<sherhook> 3](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/6.jpeg) ![<sherhook> 4](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/3.jpeg) ![<sherhook> 5](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/7.jpeg) ![<sherhook> 6](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/2.jpeg) ![<sherhook> 7](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/1.jpeg) ![<sherhook> 8](https://huggingface.co/sd-concepts-library/sherhook-painting-v2/resolve/main/concept_images/5.jpeg)
research-backup/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification	research-backup	2022-09-21T23:50:03Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T23:18:14Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7550595238095238 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5133689839572193 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.516320474777448 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5958866036687048 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.748 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4605263157894737 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5231481481481481 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9025161970769926 - name: F1 (macro) type: f1_macro value: 0.8979165451427438 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8328638497652581 - name: F1 (macro) type: f1_macro value: 0.6469572777603673 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6630552546045504 - name: F1 (macro) type: f1_macro value: 0.6493250582245075 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9562495652778744 - name: F1 (macro) type: f1_macro value: 0.8695137253747418 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8906298965841429 - name: F1 (macro) type: f1_macro value: 0.8885946595123109 --- # relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5133689839572193 - Accuracy on SAT: 0.516320474777448 - Accuracy on BATS: 0.5958866036687048 - Accuracy on U2: 0.4605263157894737 - Accuracy on U4: 0.5231481481481481 - Accuracy on Google: 0.748 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9025161970769926 - Micro F1 score on CogALexV: 0.8328638497652581 - Micro F1 score on EVALution: 0.6630552546045504 - Micro F1 score on K&H+N: 0.9562495652778744 - Micro F1 score on ROOT09: 0.8906298965841429 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7550595238095238 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: average_no_mask - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 30 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-no-mask-prompt-e-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
facebook/spar-marco-unicoil-lexmodel-context-encoder	facebook	2022-09-21T23:44:07Z	101	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2110.06918", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T23:26:34Z	--- tags: - feature-extraction pipeline_tag: feature-extraction --- This model is the context encoder of the MS MARCO UniCOIL Lexical Model (Λ) from the SPAR paper: [Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918) <br> Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih <br> Meta AI The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar This model is a BERT-base sized dense retriever trained on the MS MARCO corpus to imitate the behavior of [UniCOIL](https://github.com/castorini/pyserini/blob/master/docs/experiments-unicoil.md), a sparse retriever. The following models are also available: Pretrained Model \| Corpus \| Teacher \| Architecture \| Query Encoder Path \| Context Encoder Path \|---\|---\|---\|---\|---\|--- Wiki BM25 Λ \| Wikipedia \| BM25 \| BERT-base \| facebook/spar-wiki-bm25-lexmodel-query-encoder \| facebook/spar-wiki-bm25-lexmodel-context-encoder PAQ BM25 Λ \| PAQ \| BM25 \| BERT-base \| facebook/spar-paq-bm25-lexmodel-query-encoder \| facebook/spar-paq-bm25-lexmodel-context-encoder MARCO BM25 Λ \| MS MARCO \| BM25 \| BERT-base \| facebook/spar-marco-bm25-lexmodel-query-encoder \| facebook/spar-marco-bm25-lexmodel-context-encoder MARCO UniCOIL Λ \| MS MARCO \| UniCOIL \| BERT-base \| facebook/spar-marco-unicoil-lexmodel-query-encoder \| facebook/spar-marco-unicoil-lexmodel-context-encoder # Using the Lexical Model (Λ) Alone This model should be used together with the associated query encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model. ``` import torch from transformers import AutoTokenizer, AutoModel # The tokenizer is the same for the query and context encoder tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Apply tokenizer query_input = tokenizer(query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') # Compute embeddings: take the last-layer hidden state of the [CLS] token query_emb = query_encoder(query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :] # Compute similarity scores using dot product score1 = query_emb @ ctx_emb[0] # 341.3268 score2 = query_emb @ ctx_emb[1] # 340.1626 ``` # Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching. In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ. ``` import torch from transformers import AutoTokenizer, AutoModel from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer from transformers import DPRContextEncoder, DPRContextEncoderTokenizer # DPR model dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") # Wiki BM25 Λ model lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Compute DPR embeddings dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids'] dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') dpr_ctx_emb = dpr_ctx_encoder(dpr_ctx_input).pooler_output # Compute Λ embeddings lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt') lexmodel_query_emb = lexmodel_query_encoder(query_input).last_hidden_state[:, 0, :] lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') lexmodel_ctx_emb = lexmodel_context_encoder(*ctx_input).last_hidden_state[:, 0, :] # Form SPAR embeddings via concatenation # The concatenation weight is only applied to query embeddings # Refer to the SPAR paper for details concat_weight = 0.7 spar_query_emb = torch.cat( [dpr_query_emb, concat_weight lexmodel_query_emb], dim=-1, ) spar_ctx_emb = torch.cat( [dpr_ctx_emb, lexmodel_ctx_emb], dim=-1, ) # Compute similarity scores score1 = spar_query_emb @ spar_ctx_emb[0] # 317.6931 score2 = spar_query_emb @ spar_ctx_emb[1] # 314.6144 ```
rram12/dqn-SpaceInvadersNoFrameskip-v4	rram12	2022-09-21T23:34:19Z	2	0	stable-baselines3	[ "stable-baselines3", "SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2022-09-21T23:33:50Z	--- library_name: stable-baselines3 tags: - SpaceInvadersNoFrameskip-v4 - deep-reinforcement-learning - reinforcement-learning - stable-baselines3 model-index: - name: DQN results: - metrics: - type: mean_reward value: 567.00 +/- 231.15 name: mean_reward task: type: reinforcement-learning name: reinforcement-learning dataset: name: SpaceInvadersNoFrameskip-v4 type: SpaceInvadersNoFrameskip-v4 --- # DQN Agent playing SpaceInvadersNoFrameskip-v4 This is a trained model of a DQN agent playing SpaceInvadersNoFrameskip-v4 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3) and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo). The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. ## Usage (with SB3 RL Zoo) RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/> SB3: https://github.com/DLR-RM/stable-baselines3<br/> SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib ``` # Download model and save it into the logs/ folder python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga rram12 -f logs/ python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ ``` ## Training (with the RL Zoo) ``` python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ # Upload the model and generate video (when possible) python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga rram12 ``` ## Hyperparameters ```python OrderedDict([('batch_size', 32), ('buffer_size', 100000), ('env_wrapper', ['stable_baselines3.common.atari_wrappers.AtariWrapper']), ('exploration_final_eps', 0.01), ('exploration_fraction', 0.1), ('frame_stack', 4), ('gradient_steps', 1), ('learning_rate', 0.0001), ('learning_starts', 100000), ('n_timesteps', 1000000.0), ('optimize_memory_usage', False), ('policy', 'CnnPolicy'), ('target_update_interval', 1000), ('train_freq', 4), ('normalize', False)]) ```
espnet/jiyangtang_magicdata_asr_conformer_lm_transformer	espnet	2022-09-21T23:17:26Z	0	0	espnet	[ "espnet", "audio", "automatic-speech-recognition", "zh", "dataset:magicdata", "arxiv:1804.00015", "license:cc-by-4.0", "region:us" ]	automatic-speech-recognition	2022-09-21T23:15:28Z	--- tags: - espnet - audio - automatic-speech-recognition language: zh datasets: - magicdata license: cc-by-4.0 --- ## ESPnet2 ASR model ### `espnet/jiyangtang_magicdata_asr_conformer_lm_transformer` This model was trained by Jiyang Tang using magicdata recipe in [espnet](https://github.com/espnet/espnet/). ### Demo: How to use in ESPnet2 Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html) if you haven't done that already. ```bash cd espnet git checkout 9d0f3b3e1be6650d38cc5008518f445308fe06d9 pip install -e . cd egs2/magicdata/asr1 ./run.sh --skip_data_prep false --skip_train true --download_model espnet/jiyangtang_magicdata_asr_conformer_lm_transformer ``` <!-- Generated by scripts/utils/show_asr_result.sh --> # RESULTS ## Environments - date: `Wed Sep 21 01:11:58 EDT 2022` - python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]` - espnet version: `espnet 202207` - pytorch version: `pytorch 1.8.1+cu102` - Git hash: `9d0f3b3e1be6650d38cc5008518f445308fe06d9` - Commit date: `Mon Sep 19 20:27:41 2022 -0400` ## asr_train_asr_raw_zh_char_sp ### WER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test\|24279\|24286\|84.4\|15.6\|0.0\|0.0\|15.6\|15.6\| ### CER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| \|decode_asr_rnn_lm_lm_train_lm_transformer_zh_char_valid.loss.ave_asr_model_valid.acc.ave/test\|24279\|243325\|96.4\|1.7\|2.0\|0.1\|3.7\|15.6\| ### TER \|dataset\|Snt\|Wrd\|Corr\|Sub\|Del\|Ins\|Err\|S.Err\| \|---\|---\|---\|---\|---\|---\|---\|---\|---\| ## ASR config <details><summary>expand</summary> ``` config: conf/train_asr.yaml print_config: false log_level: INFO dry_run: false iterator_type: sequence output_dir: exp/asr_train_asr_raw_zh_char_sp ngpu: 0 seed: 0 num_workers: 4 num_att_plot: 3 dist_backend: nccl dist_init_method: env:// dist_world_size: null dist_rank: null local_rank: null dist_master_addr: null dist_master_port: null dist_launcher: null multiprocessing_distributed: false unused_parameters: false sharded_ddp: false cudnn_enabled: true cudnn_benchmark: false cudnn_deterministic: true collect_stats: false write_collected_feats: false max_epoch: 20 patience: null val_scheduler_criterion: - valid - acc early_stopping_criterion: - valid - loss - min best_model_criterion: - - valid - acc - max keep_nbest_models: 10 nbest_averaging_interval: 0 grad_clip: 5 grad_clip_type: 2.0 grad_noise: false accum_grad: 4 no_forward_run: false resume: true train_dtype: float32 use_amp: false log_interval: null use_matplotlib: true use_tensorboard: true create_graph_in_tensorboard: false use_wandb: false wandb_project: null wandb_id: null wandb_entity: null wandb_name: null wandb_model_log_interval: -1 detect_anomaly: false pretrain_path: null init_param: [] ignore_init_mismatch: false freeze_param: [] num_iters_per_epoch: null batch_size: 20 valid_batch_size: null batch_bins: 20000000 valid_batch_bins: null train_shape_file: - exp/asr_stats_raw_zh_char_sp/train/speech_shape - exp/asr_stats_raw_zh_char_sp/train/text_shape.char valid_shape_file: - exp/asr_stats_raw_zh_char_sp/valid/speech_shape - exp/asr_stats_raw_zh_char_sp/valid/text_shape.char batch_type: numel valid_batch_type: null fold_length: - 80000 - 150 sort_in_batch: descending sort_batch: descending multiple_iterator: false chunk_length: 500 chunk_shift_ratio: 0.5 num_cache_chunks: 1024 train_data_path_and_name_and_type: - - dump/raw/train_noeng_sp/wav.scp - speech - sound - - dump/raw/train_noeng_sp/text - text - text valid_data_path_and_name_and_type: - - dump/raw/dev/wav.scp - speech - sound - - dump/raw/dev/text - text - text allow_variable_data_keys: false max_cache_size: 0.0 max_cache_fd: 32 valid_max_cache_size: null optim: adam optim_conf: lr: 0.0005 scheduler: warmuplr scheduler_conf: warmup_steps: 30000 token_list: - <blank> - <unk> - 的 - 我 - 一 - 歌 - 你 - 天 - 不 - 了 - 放 - 来 - 播 - 下 - 个 - 是 - 有 - 给 - 首 - 好 - 请 - 在 - 听 - 么 - 气 - 要 - 想 - 曲 - 上 - 吗 - 去 - 到 - 这 - 啊 - 点 - 那 - 没 - 就 - 说 - 大 - 唱 - 人 - 最 - 第 - 看 - 会 - 明 - 集 - 吧 - 音 - 还 - 乐 - 今 - 电 - 开 - 能 - 度 - 哪 - 里 - 多 - 打 - 十 - 可 - 怎 - 道 - 什 - 新 - 雨 - 以 - 家 - 回 - 话 - 儿 - 他 - 时 - 小 - 温 - 样 - 爱 - 都 - 吃 - 呢 - 知 - 谁 - 为 - 子 - 们 - 也 - 过 - 老 - 很 - 出 - 中 - 现 - 冷 - 和 - 情 - 行 - 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卅 - 箅 - 氨 - 浠 - 蠡 - 募 - 肛 - 岀 - 瞑 - 蛆 - 舀 - 蚝 - 歙 - 涔 - 诘 - 、 - 垡 - 涠 - 嘢 - 糸 - 胤 - 绊 - 柒 - 沓 - 粼 - 菖 - 犒 - 呒 - 唑 - 莘 - 莪 - 宸 - 睨 - \ - 鲶 - 蛐 - 溏 - 菈 - 蹩 - 焙 - 釆 - 瑗 - 睾 - 槐 - 榉 - 杷 - 鄢 - 僕 - 诽 - 嗲 - 蜃 - 戆 - 蘼 - 糜 - 霁 - 坻 - 硼 - 槛 - 枞 - 麸 - 谒 - 荀 - 邋 - 遢 - 锴 - 啶 - 粪 - 驭 - 筵 - 砌 - 莩 - 蹼 - 吔 - 缳 - 埭 - 隗 - 厶 - 丶 - "\x14" - "\x17" - 稼 - 铖 - 涣 - 亳 - 幢 - 沭 - 驮 - 奚 - 藐 - 颅 - 埤 - 愘 - 镲 - 窒 - 暄 - 诃 - 噘 - 歼 - 隅 - 爻 - 蘅 - 锹 - 锇 - 椎 - 琨 - 烩 - 枢 - 觧 - 萁 - 镂 - 龈 - 怠 - 阐 - 藉 - 凛 - 冽 - 珣 - 泘 - 抉 - 锭 - 蕃 - 蠃 - 毓 - 啐 - 栩 - 骷 - 髅 - 耷 - 寥 - 杵 - 蚬 - 窖 - 孛 - 舆 - 皿 - 柸 - 粳 - 钣 - 趸 - 叄 - 腚 - 杖 - 鸸 - 犲 - 浗 - 缮 - 哓 - 箧 - 攘 - 冇 - 钛 - 郗 - 囡 - 酆 - 姌 - 雉 - 胯 - 椭 - 埏 - 钵 - 绌 - 蝾 - 坼 - 濂 - ｗ - ｏ - ｒ - ｄ - 袒 - 峦 - 鹫 - 炯 - 悱 - 漕 - 莦 - 蔑 - 樽 - 牒 - 濡 - 嫯 - 陖 - 疸 - 桅 - 辖 - 僢 - 《 - 》 - 酣 - 遨 - 邬 - ':' - 嫲 - 哌 - 锚 - 淙 - Ｑ - 濑 - 熨 - 谴 - 筛 - 薹 - 磬 - 熠 - 腓 - 阉 - 钴 - 恂 - 溉 - 陨 - 螳 - 孵 - 瘠 - 嫡 - 哝 - 狙 - 怼 - 斟 - 甫 - 渌 - 卒 - 翕 - 沏 - 旮 - 旯 - 菡 - 變 - 狈 - 鳜 - 嵋 - 仞 - 鳕 - 噩 - 踟 - 躇 - 蛀 - 瘸 - 篡 - 锊 - 団 - 斐 - 蹍 - 冗 - "\uFEFF" - 歆 - 圴 - 泯 - 伥 - 愎 - 坌 - 碘 - 赉 - 骧 - 矩 - 綽 - 秭 - 怵 - 麝 - 贩 - 溥 - 捆 - 腩 - 溴 - 卉 - 痦 - 荻 - 缇 - 秸 - 秆 - 捍 - 炀 - 阆 - 泞 - 懊 - 啕 - 蚶 - 衩 - 桜 - 旖 - 贬 - 酵 - 滟 - 纥 - 倭 - 赝 - 呶 - 哧 - 煸 - 劢 - 炝 - 僚 - 豇 - 阂 - 涝 - 骡 - 霭 - 窨 - 殴 - 竣 - 醇 - 擂 - 怦 - 怩 - 臾 - 搔 - 伱 - 啉 - 嫖 - 囝 - 糠 - 胥 - 酰 - 镫 - 蟒 - 荞 - 醪 - 颦 - 吏 - 颛 - 赳 - 贿 - 赂 - 痩 - 仂 - 颍 - 罔 - 猕 - 嚒 - 蘸 - 熹 - 捺 - 坜 - 郜 - 鉄 - 蒌 - 荑 - 藻 - 谌 - 钳 - 屮 - 疵 - 哞 - 琮 - 潴 - 讹 - 镭 - '3' - 尕 - 倬 - 庇 - 侩 - 瘆 - 傀 - 儡 - 诧 - 葆 - 唾 - 皋 - 逄 - 诌 - 氦 - 彳 - 盅 - 曳 - 槲 - 挟 - 怿 - 顷 - 臃 - 衙 - 踵 - 霈 - 嗪 - 闩 - 锟 - 恿 - 抻 - 茁 - 惢 - 菅 - 迂 - 瞟 - 痉 - 挛 - 绦 - 晁 - 挢 - 蠕 - 洙 - <sos/eos> init: null input_size: null ctc_conf: dropout_rate: 0.0 ctc_type: builtin reduce: true ignore_nan_grad: null zero_infinity: true joint_net_conf: null use_preprocessor: true token_type: char bpemodel: null non_linguistic_symbols: null cleaner: null g2p: null speech_volume_normalize: null rir_scp: null rir_apply_prob: 1.0 noise_scp: null noise_apply_prob: 1.0 noise_db_range: '13_15' short_noise_thres: 0.5 frontend: default frontend_conf: fs: 16k specaug: specaug specaug_conf: apply_time_warp: true time_warp_window: 5 time_warp_mode: bicubic apply_freq_mask: true freq_mask_width_range: - 0 - 30 num_freq_mask: 2 apply_time_mask: true time_mask_width_range: - 0 - 40 num_time_mask: 2 normalize: global_mvn normalize_conf: stats_file: exp/asr_stats_raw_zh_char_sp/train/feats_stats.npz model: espnet model_conf: ctc_weight: 0.3 lsm_weight: 0.1 length_normalized_loss: false preencoder: null preencoder_conf: {} encoder: conformer encoder_conf: output_size: 512 attention_heads: 8 linear_units: 2048 num_blocks: 12 dropout_rate: 0.1 positional_dropout_rate: 0.1 attention_dropout_rate: 0.0 input_layer: conv2d normalize_before: true pos_enc_layer_type: rel_pos selfattention_layer_type: rel_selfattn activation_type: swish macaron_style: true use_cnn_module: true cnn_module_kernel: 31 postencoder: null postencoder_conf: {} decoder: transformer decoder_conf: attention_heads: 4 linear_units: 2048 num_blocks: 6 dropout_rate: 0.1 positional_dropout_rate: 0.1 self_attention_dropout_rate: 0.0 src_attention_dropout_rate: 0.0 required: - output_dir - token_list version: '202207' distributed: false ``` </details> ### Citing ESPnet ```BibTex @inproceedings{watanabe2018espnet, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, title={{ESPnet}: End-to-End Speech Processing Toolkit}, year={2018}, booktitle={Proceedings of Interspeech}, pages={2207--2211}, doi={10.21437/Interspeech.2018-1456}, url={http://dx.doi.org/10.21437/Interspeech.2018-1456} } ``` or arXiv: ```bibtex @misc{watanabe2018espnet, title={ESPnet: End-to-End Speech Processing Toolkit}, author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai}, year={2018}, eprint={1804.00015}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```
jgiral95/q-Taxi-v3	jgiral95	2022-09-21T23:03:18Z	0	0	null	[ "Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2022-09-21T23:03:10Z	--- tags: - Taxi-v3 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-Taxi-v3 results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Taxi-v3 type: Taxi-v3 metrics: - type: mean_reward value: 7.56 +/- 2.71 name: mean_reward verified: false --- # Q-Learning Agent playing Taxi-v3 This is a trained model of a Q-Learning agent playing Taxi-v3 . ## Usage ```python model = load_from_hub(repo_id="jgiral95/q-Taxi-v3", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"]) ```
teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average	teven	2022-09-21T22:56:28Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T22:56:21Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average 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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average') 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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average') model = AutoModel.from_pretrained('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average') # 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=teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average) ## 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 -->
facebook/spar-wiki-bm25-lexmodel-context-encoder	facebook	2022-09-21T22:46:34Z	106	0	transformers	[ "transformers", "pytorch", "bert", "feature-extraction", "arxiv:2110.06918", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T21:39:14Z	--- tags: - feature-extraction pipeline_tag: feature-extraction --- This model is the context encoder of the Wiki BM25 Lexical Model (Λ) from the SPAR paper: [Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?](https://arxiv.org/abs/2110.06918) <br> Xilun Chen, Kushal Lakhotia, Barlas Oğuz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta and Wen-tau Yih <br> Meta AI The associated github repo is available here: https://github.com/facebookresearch/dpr-scale/tree/main/spar This model is a BERT-base sized dense retriever trained on Wikipedia articles to imitate the behavior of BM25. The following models are also available: Pretrained Model \| Corpus \| Teacher \| Architecture \| Query Encoder Path \| Context Encoder Path \|---\|---\|---\|---\|---\|--- Wiki BM25 Λ \| Wikipedia \| BM25 \| BERT-base \| facebook/spar-wiki-bm25-lexmodel-query-encoder \| facebook/spar-wiki-bm25-lexmodel-context-encoder PAQ BM25 Λ \| PAQ \| BM25 \| BERT-base \| facebook/spar-paq-bm25-lexmodel-query-encoder \| facebook/spar-paq-bm25-lexmodel-context-encoder MARCO BM25 Λ \| MS MARCO \| BM25 \| BERT-base \| facebook/spar-marco-bm25-lexmodel-query-encoder \| facebook/spar-marco-bm25-lexmodel-context-encoder MARCO UniCOIL Λ \| MS MARCO \| UniCOIL \| BERT-base \| facebook/spar-marco-unicoil-lexmodel-query-encoder \| facebook/spar-marco-unicoil-lexmodel-context-encoder # Using the Lexical Model (Λ) Alone This model should be used together with the associated query encoder, similar to the [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr) model. ``` import torch from transformers import AutoTokenizer, AutoModel # The tokenizer is the same for the query and context encoder tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Apply tokenizer query_input = tokenizer(query, return_tensors='pt') ctx_input = tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') # Compute embeddings: take the last-layer hidden state of the [CLS] token query_emb = query_encoder(query_input).last_hidden_state[:, 0, :] ctx_emb = context_encoder(ctx_input).last_hidden_state[:, 0, :] # Compute similarity scores using dot product score1 = query_emb @ ctx_emb[0] # 341.3268 score2 = query_emb @ ctx_emb[1] # 340.1626 ``` # Using the Lexical Model (Λ) with a Base Dense Retriever as in SPAR As Λ learns lexical matching from a sparse teacher retriever, it can be used in combination with a standard dense retriever (e.g. [DPR](https://huggingface.co/docs/transformers/v4.22.1/en/model_doc/dpr#dpr), [Contriever](https://huggingface.co/facebook/contriever-msmarco)) to build a dense retriever that excels at both lexical and semantic matching. In the following example, we show how to build the SPAR-Wiki model for Open-Domain Question Answering by concatenating the embeddings of DPR and the Wiki BM25 Λ. ``` import torch from transformers import AutoTokenizer, AutoModel from transformers import DPRQuestionEncoder, DPRQuestionEncoderTokenizer from transformers import DPRContextEncoder, DPRContextEncoderTokenizer # DPR model dpr_ctx_tokenizer = DPRContextEncoderTokenizer.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_ctx_encoder = DPRContextEncoder.from_pretrained("facebook/dpr-ctx_encoder-multiset-base") dpr_query_tokenizer = DPRQuestionEncoderTokenizer.from_pretrained("facebook/dpr-question_encoder-multiset-base") dpr_query_encoder = DPRQuestionEncoder.from_pretrained("facebook/dpr-question_encoder-multiset-base") # Wiki BM25 Λ model lexmodel_tokenizer = AutoTokenizer.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_query_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-query-encoder') lexmodel_context_encoder = AutoModel.from_pretrained('facebook/spar-wiki-bm25-lexmodel-context-encoder') query = "Where was Marie Curie born?" contexts = [ "Maria Sklodowska, later known as Marie Curie, was born on November 7, 1867.", "Born in Paris on 15 May 1859, Pierre Curie was the son of Eugène Curie, a doctor of French Catholic origin from Alsace." ] # Compute DPR embeddings dpr_query_input = dpr_query_tokenizer(query, return_tensors='pt')['input_ids'] dpr_query_emb = dpr_query_encoder(dpr_query_input).pooler_output dpr_ctx_input = dpr_ctx_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') dpr_ctx_emb = dpr_ctx_encoder(dpr_ctx_input).pooler_output # Compute Λ embeddings lexmodel_query_input = lexmodel_tokenizer(query, return_tensors='pt') lexmodel_query_emb = lexmodel_query_encoder(query_input).last_hidden_state[:, 0, :] lexmodel_ctx_input = lexmodel_tokenizer(contexts, padding=True, truncation=True, return_tensors='pt') lexmodel_ctx_emb = lexmodel_context_encoder(*ctx_input).last_hidden_state[:, 0, :] # Form SPAR embeddings via concatenation # The concatenation weight is only applied to query embeddings # Refer to the SPAR paper for details concat_weight = 0.7 spar_query_emb = torch.cat( [dpr_query_emb, concat_weight lexmodel_query_emb], dim=-1, ) spar_ctx_emb = torch.cat( [dpr_ctx_emb, lexmodel_ctx_emb], dim=-1, ) # Compute similarity scores score1 = spar_query_emb @ spar_ctx_emb[0] # 317.6931 score2 = spar_query_emb @ spar_ctx_emb[1] # 314.6144 ```
teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average	teven	2022-09-21T22:44:31Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T22:44:25Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average 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('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average') 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('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average') model = AutoModel.from_pretrained('teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average') # 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=teven/cross_all_bs320_vanilla_finetuned_WebNLG2020_metric_average) ## 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 -->
sd-concepts-library/karan-gloomy	sd-concepts-library	2022-09-21T22:42:56Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-09-21T22:42:50Z	--- license: mit --- ### Karan Gloomy on Stable Diffusion This is the `<karan>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<karan> 0](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/4.jpeg) ![<karan> 1](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/12.jpeg) ![<karan> 2](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/8.jpeg) ![<karan> 3](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/0.jpeg) ![<karan> 4](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/6.jpeg) ![<karan> 5](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/3.jpeg) ![<karan> 6](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/20.jpeg) ![<karan> 7](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/11.jpeg) ![<karan> 8](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/19.jpeg) ![<karan> 9](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/17.jpeg) ![<karan> 10](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/10.jpeg) ![<karan> 11](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/7.jpeg) ![<karan> 12](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/13.jpeg) ![<karan> 13](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/16.jpeg) ![<karan> 14](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/2.jpeg) ![<karan> 15](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/9.jpeg) ![<karan> 16](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/15.jpeg) ![<karan> 17](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/21.jpeg) ![<karan> 18](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/1.jpeg) ![<karan> 19](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/14.jpeg) ![<karan> 20](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/5.jpeg) ![<karan> 21](https://huggingface.co/sd-concepts-library/karan-gloomy/resolve/main/concept_images/18.jpeg)
teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average	teven	2022-09-21T22:38:53Z	5	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T22:38:46Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average 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('teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average') 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=teven/bi_all-mpnet-base-v2_finetuned_WebNLG2020_metric_average) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 27 with parameters: ``` {'batch_size': 96, '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": 50, "evaluation_steps": 0, "evaluator": "better_cross_encoder.PearsonCorrelationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 0.0005 }, "scheduler": "warmupcosine", "steps_per_epoch": null, "warmup_steps": 135, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, '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 -->
teven/bi_all_bs160_allneg_finetuned_WebNLG2020_metric_average	teven	2022-09-21T22:37:45Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T22:37:38Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # teven/bi_all_bs160_allneg_finetuned_WebNLG2020_metric_average 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('teven/bi_all_bs160_allneg_finetuned_WebNLG2020_metric_average') 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=teven/bi_all_bs160_allneg_finetuned_WebNLG2020_metric_average) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 161 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": 50, "evaluation_steps": 0, "evaluator": "better_cross_encoder.PearsonCorrelationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "warmupcosine", "steps_per_epoch": null, "warmup_steps": 805, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, '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 -->
nvidia/nemo-megatron-gpt-20B	nvidia	2022-09-21T22:32:20Z	16	32	nemo	[ "nemo", "text generation", "pytorch", "causal-lm", "en", "dataset:the_pile", "arxiv:1909.08053", "arxiv:2101.00027", "license:cc-by-4.0", "region:us" ]	null	2022-09-15T00:51:22Z	--- language: - en library_name: nemo datasets: - the_pile tags: - text generation - pytorch - causal-lm license: cc-by-4.0 --- # NeMo Megatron-GPT 20B <style> img { display: inline; } </style> \|[![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)\|[![Model size](https://img.shields.io/badge/Params-20B-green)](#model-architecture)\|[![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets) ## Model Description Megatron-GPT 20B is a transformer-based language model. GPT refers to a class of transformer decoder-only models similar to GPT-2 and 3 while 20B refers to the total trainable parameter count (20 Billion) [1, 2]. This model was trained with [NeMo Megatron](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/nemo_megatron/intro.html). ## Getting started Note: You will need NVIDIA Ampere or Hopper GPUs to work with this model. ### Step 1: Install NeMo and dependencies You will need to install NVIDIA Apex and NeMo. ``` git clone https://github.com/ericharper/apex.git cd apex git checkout nm_v1.11.0 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" --global-option="--fast_layer_norm" --global-option="--distributed_adam" --global-option="--deprecated_fused_adam" ./ ``` ``` pip install nemo_toolkit['nlp']==1.11.0 ``` Alternatively, you can use NeMo Megatron training docker container with all dependencies pre-installed. ### Step 2: Launch eval server Note. The example below launches a model variant with Tensor Parallelism (TP) of 4 and Pipeline Parallelism (PP) of 1 on 4 GPUs. ``` git clone https://github.com/NVIDIA/NeMo.git cd NeMo/examples/nlp/language_modeling git checkout v1.11.0 python megatron_gpt_eval.py gpt_model_file=nemo_gpt20B_bf16_tp4.nemo server=True tensor_model_parallel_size=4 trainer.devices=4 ``` ### Step 3: Send prompts to your model! ```python import json import requests port_num = 5555 headers = {"Content-Type": "application/json"} def request_data(data): resp = requests.put('http://localhost:{}/generate'.format(port_num), data=json.dumps(data), headers=headers) sentences = resp.json()['sentences'] return sentences data = { "sentences": ["Tell me an interesting fact about space travel."]1, "tokens_to_generate": 50, "temperature": 1.0, "add_BOS": True, "top_k": 0, "top_p": 0.9, "greedy": False, "all_probs": False, "repetition_penalty": 1.2, "min_tokens_to_generate": 2, } sentences = request_data(data) print(sentences) ``` ## Training Data The model was trained on ["The Piles" dataset prepared by Eleuther.AI](https://pile.eleuther.ai/). [4] ## Evaluation results Zero-shot performance.* Evaluated using [LM Evaluation Test Suite from AI21](https://github.com/AI21Labs/lm-evaluation) \| ARC-Challenge \| ARC-Easy \| RACE-middle \| RACE-high \| Winogrande \| RTE \| BoolQA \| HellaSwag \| PiQA \| \| ------------- \| -------- \| ----------- \| --------- \| ---------- \| --- \| ------ \| --------- \| ---- \| \| 0.4403 \| 0.6141 \| 0.5188 \| 0.4277 \| 0.659 \| 0.5704 \| 0.6954 \| 0.721 \| 0.7688 \| ## Limitations The model was trained on the data originally crawled from the Internet. This data contains toxic language and societal biases. Therefore, the model may amplify those biases and return toxic responses especially when prompted with toxic prompts. ## References [1] [Improving Language Understanding by Generative Pre-Training](https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf) [2] [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://arxiv.org/pdf/1909.08053.pdf) [3] [NVIDIA NeMo Toolkit](https://github.com/NVIDIA/NeMo) [4] [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://arxiv.org/abs/2101.00027) ## Licence License to use this model is covered by the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). By downloading the public and release version of the model, you accept the terms and conditions of the [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/) license.
tdobrxl/ClinicBERT	tdobrxl	2022-09-21T22:27:34Z	196	0	transformers	[ "transformers", "pytorch", "roberta", "fill-mask", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2022-07-27T16:18:35Z	ClinicBERT has the same architecture of RoBERTa model. It has been trained on clinical text and can be used for feature extraction from textual data. ## How to use ### Feature Extraction ``` from transformers import RobertaModel, RobertaTokenizer model = RobertaModel.from_pretrained("tdobrxl/ClinicBERT") tokenizer = RobertaTokenizer.from_pretrained("tdobrxl/ClinicBERT") text = "Randomized Study of Shark Cartilage in Patients With Breast Cancer." last_hidden_state, pooler_output = model(tokenizer.encode(text, return_tensors="pt")).last_hidden_state, model(tokenizer.encode(text, return_tensors="pt")).pooler_output ``` ### Masked Word Prediction ``` from transformers import pipeline fill_mask = pipeline("fill-mask", model="tdobrxl/ClinicBERT", tokenizer="tdobrxl/ClinicBERT") text = "this is the start of a beautiful <mask>." fill_mask(text) ``` ```[{'score': 0.26558592915534973, 'token': 363, 'token_str': ' study', 'sequence': 'this is the start of a beautiful study.'}, {'score': 0.06330082565546036, 'token': 2010, 'token_str': ' procedure', 'sequence': 'this is the start of a beautiful procedure.'}, {'score': 0.04393036663532257, 'token': 661, 'token_str': ' trial', 'sequence': 'this is the start of a beautiful trial.'}, {'score': 0.0363750196993351, 'token': 839, 'token_str': ' period', 'sequence': 'this is the start of a beautiful period.'}, {'score': 0.027248281985521317, 'token': 436, 'token_str': ' treatment', 'sequence': 'this is the start of a beautiful treatment.'}```
monakth/distillbert-base-uncased-fine-tuned-squad	monakth	2022-09-21T22:01:02Z	123	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "question-answering", "generated_from_trainer", "dataset:squad", "license:apache-2.0", "endpoints_compatible", "region:us" ]	question-answering	2022-09-18T15:48:06Z	--- license: apache-2.0 tags: - generated_from_trainer datasets: - squad model-index: - name: distilbert-base-uncased-finetuned-squad 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-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset. It achieves the following results on the evaluation set: - Loss: 1.1568 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:-----:\|:---------------:\| \| 1.2269 \| 1.0 \| 5533 \| 1.1705 \| \| 0.9725 \| 2.0 \| 11066 \| 1.1238 \| \| 0.768 \| 3.0 \| 16599 \| 1.1568 \| ### Framework versions - Transformers 4.22.1 - Pytorch 1.12.1+cu113 - Datasets 2.5.1 - Tokenizers 0.12.1
omarques/autotrain-dogs-and-cats-1527055142	omarques	2022-09-21T21:38:24Z	267	2	transformers	[ "transformers", "pytorch", "autotrain", "vision", "image-classification", "dataset:omarques/autotrain-data-dogs-and-cats", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	image-classification	2022-09-21T21:37:41Z	--- tags: - autotrain - vision - image-classification datasets: - omarques/autotrain-data-dogs-and-cats widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 0.8187420113922029 --- # Model Trained Using AutoTrain - Problem type: Binary Classification - Model ID: 1527055142 - CO2 Emissions (in grams): 0.8187 ## Validation Metrics - Loss: 0.068 - Accuracy: 1.000 - Precision: 1.000 - Recall: 1.000 - AUC: 1.000 - F1: 1.000
sd-concepts-library/midjourney-style	sd-concepts-library	2022-09-21T21:17:45Z	0	152	null	[ "license:mit", "region:us" ]	null	2022-09-21T21:17:31Z	--- license: mit --- ### Midjourney style on Stable Diffusion This is the `<midjourney-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<midjourney-style> 0](https://huggingface.co/sd-concepts-library/midjourney-style/resolve/main/concept_images/0.jpeg) ![<midjourney-style> 1](https://huggingface.co/sd-concepts-library/midjourney-style/resolve/main/concept_images/3.jpeg) ![<midjourney-style> 2](https://huggingface.co/sd-concepts-library/midjourney-style/resolve/main/concept_images/2.jpeg) ![<midjourney-style> 3](https://huggingface.co/sd-concepts-library/midjourney-style/resolve/main/concept_images/1.jpeg)
research-backup/roberta-large-semeval2012-average-prompt-e-nce-classification	research-backup	2022-09-21T20:57:42Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T20:26:28Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-average-prompt-e-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.75625 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5213903743315508 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5222551928783383 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6292384658143413 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.768 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.4649122807017544 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5277777777777778 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9121591080307367 - name: F1 (macro) type: f1_macro value: 0.9078493464517976 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8328638497652581 - name: F1 (macro) type: f1_macro value: 0.643974348342842 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.652762730227519 - name: F1 (macro) type: f1_macro value: 0.6418800744019266 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9641093413090353 - name: F1 (macro) type: f1_macro value: 0.889375508685358 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8827953619554998 - name: F1 (macro) type: f1_macro value: 0.8807348541974301 --- # relbert/roberta-large-semeval2012-average-prompt-e-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5213903743315508 - Accuracy on SAT: 0.5222551928783383 - Accuracy on BATS: 0.6292384658143413 - Accuracy on U2: 0.4649122807017544 - Accuracy on U4: 0.5277777777777778 - Accuracy on Google: 0.768 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9121591080307367 - Micro F1 score on CogALexV: 0.8328638497652581 - Micro F1 score on EVALution: 0.652762730227519 - Micro F1 score on K&H+N: 0.9641093413090353 - Micro F1 score on ROOT09: 0.8827953619554998 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.75625 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-e-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 30 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-e-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
blmnk/distilbert-base-uncased-finetuned-emotion	blmnk	2022-09-21T20:46:31Z	105	0	transformers	[ "transformers", "pytorch", "tensorboard", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2022-09-21T20:19:02Z	--- 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: 0.896 - name: F1 type: f1 value: 0.8927988574486181 --- <!-- 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: 0.3821 - Accuracy: 0.896 - F1: 0.8928 ## 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: 128 - eval_batch_size: 128 - 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 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| No log \| 1.0 \| 125 \| 0.6029 \| 0.7985 \| 0.7597 \| \| 0.7905 \| 2.0 \| 250 \| 0.3821 \| 0.896 \| 0.8928 \| ### Framework versions - Transformers 4.11.3 - Pytorch 1.12.1 - Datasets 1.16.1 - Tokenizers 0.10.3
sd-concepts-library/outfit-items	sd-concepts-library	2022-09-21T19:52:18Z	0	2	null	[ "license:mit", "region:us" ]	null	2022-09-21T19:52:12Z	--- license: mit --- ### Outfit Items on Stable Diffusion This is the `<outfit-items>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<outfit-items> 0](https://huggingface.co/sd-concepts-library/outfit-items/resolve/main/concept_images/1.jpeg) ![<outfit-items> 1](https://huggingface.co/sd-concepts-library/outfit-items/resolve/main/concept_images/2.jpeg) ![<outfit-items> 2](https://huggingface.co/sd-concepts-library/outfit-items/resolve/main/concept_images/0.jpeg) ![<outfit-items> 3](https://huggingface.co/sd-concepts-library/outfit-items/resolve/main/concept_images/3.jpeg)
pritamdeka/S-BioBert-snli-multinli-stsb	pritamdeka	2022-09-21T18:59:33Z	2,681	5	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # S-BioBert-snli-multinli-stsb 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('pritamdeka/S-BioBert-snli-multinli-stsb') 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('pritamdeka/S-BioBert-snli-multinli-stsb') model = AutoModel.from_pretrained('pritamdeka/S-BioBert-snli-multinli-stsb') # 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, max 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 90 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 36, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, '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 <!--- Describe where people can find more information --> If you use the model kindly cite the following work ``` @inproceedings{deka2021unsupervised, title={Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking}, author={Deka, Pritam and Jurek-Loughrey, Anna}, booktitle={The 23rd International Conference on Information Integration and Web Intelligence}, pages={267--277}, year={2021} } ```
pritamdeka/S-Scibert-snli-multinli-stsb	pritamdeka	2022-09-21T18:59:09Z	5,987	4	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # pritamdeka/S-Scibert-snli-multinli-stsb 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('pritamdeka/S-Scibert-snli-multinli-stsb') 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('pritamdeka/S-Scibert-snli-multinli-stsb') model = AutoModel.from_pretrained('pritamdeka/S-Scibert-snli-multinli-stsb') # 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, max 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 90 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 36, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, '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 <!--- Describe where people can find more information --> If you use the model kindly cite the following work ``` @inproceedings{deka2021unsupervised, title={Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking}, author={Deka, Pritam and Jurek-Loughrey, Anna}, booktitle={The 23rd International Conference on Information Integration and Web Intelligence}, pages={267--277}, year={2021} } ```
pritamdeka/S-Bluebert-snli-multinli-stsb	pritamdeka	2022-09-21T18:58:03Z	702	7	sentence-transformers	[ "sentence-transformers", "pytorch", "bert", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-03-02T23:29:05Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # pritamdeka/S-Bluebert-snli-multinli-stsb 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('pritamdeka/S-Bluebert-snli-multinli-stsb') 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('pritamdeka/S-Bluebert-snli-multinli-stsb') model = AutoModel.from_pretrained('pritamdeka/S-Bluebert-snli-multinli-stsb') # 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, max 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 90 with parameters: ``` {'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` Loss*: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "callback": null, "epochs": 4, "evaluation_steps": 1000, "evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 2e-05 }, "scheduler": "WarmupLinear", "steps_per_epoch": null, "warmup_steps": 36, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 75, '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 <!--- Describe where people can find more information --> If you use the model kindly cite the following work ``` @inproceedings{deka2021unsupervised, title={Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact Checking}, author={Deka, Pritam and Jurek-Loughrey, Anna}, booktitle={The 23rd International Conference on Information Integration and Web Intelligence}, pages={267--277}, year={2021} } ```
sd-concepts-library/wildkat	sd-concepts-library	2022-09-21T18:56:20Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-21T18:56:13Z	--- license: mit --- ### Wildkat on Stable Diffusion This is the `<wildkat>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<wildkat> 0](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/7.jpeg) ![<wildkat> 1](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/1.jpeg) ![<wildkat> 2](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/2.jpeg) ![<wildkat> 3](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/8.jpeg) ![<wildkat> 4](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/0.jpeg) ![<wildkat> 5](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/3.jpeg) ![<wildkat> 6](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/4.jpeg) ![<wildkat> 7](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/5.jpeg) ![<wildkat> 8](https://huggingface.co/sd-concepts-library/wildkat/resolve/main/concept_images/6.jpeg)
research-backup/roberta-large-semeval2012-average-prompt-a-nce-classification	research-backup	2022-09-21T18:41:55Z	106	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T18:03:50Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-average-prompt-a-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.789047619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3342245989304813 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33827893175074186 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3885491939966648 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.542 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3201754385964912 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33564814814814814 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8865451258098539 - name: F1 (macro) type: f1_macro value: 0.8770785182418419 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8401408450704225 - name: F1 (macro) type: f1_macro value: 0.6242491296371133 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6749729144095341 - name: F1 (macro) type: f1_macro value: 0.6505812342477592 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9607706753842944 - name: F1 (macro) type: f1_macro value: 0.8781957733610742 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8994045753682232 - name: F1 (macro) type: f1_macro value: 0.8968786782259857 --- # relbert/roberta-large-semeval2012-average-prompt-a-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.3342245989304813 - Accuracy on SAT: 0.33827893175074186 - Accuracy on BATS: 0.3885491939966648 - Accuracy on U2: 0.3201754385964912 - Accuracy on U4: 0.33564814814814814 - Accuracy on Google: 0.542 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8865451258098539 - Micro F1 score on CogALexV: 0.8401408450704225 - Micro F1 score on EVALution: 0.6749729144095341 - Micro F1 score on K&H+N: 0.9607706753842944 - Micro F1 score on ROOT09: 0.8994045753682232 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.789047619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-average-prompt-a-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: average - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: Today, I finally discovered the relation between <subj> and <obj> : <subj> is the <mask> of <obj> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-average-prompt-a-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
osanseviero/da_core_news_sm	osanseviero	2022-09-21T17:43:59Z	1	0	spacy	[ "spacy", "token-classification", "da", "license:cc-by-sa-4.0", "model-index", "region:us" ]	token-classification	2022-03-02T23:29:05Z	--- tags: - spacy - token-classification language: - da license: cc-by-sa-4.0 model-index: - name: da_core_news_sm results: - task: name: NER type: token-classification metrics: - name: NER Precision type: precision value: 0.7570498915 - name: NER Recall type: recall value: 0.7270833333 - name: NER F Score type: f_score value: 0.7417640808 - task: name: TAG type: token-classification metrics: - name: TAG (XPOS) Accuracy type: accuracy value: 0.9498765073 - task: name: POS type: token-classification metrics: - name: POS (UPOS) Accuracy type: accuracy value: 0.9498765073 - task: name: MORPH type: token-classification metrics: - name: Morph (UFeats) Accuracy type: accuracy value: 0.9343341404 - task: name: LEMMA type: token-classification metrics: - name: Lemma Accuracy type: accuracy value: 0.9449878935 - task: name: UNLABELED_DEPENDENCIES type: token-classification metrics: - name: Unlabeled Attachment Score (UAS) type: f_score value: 0.7988826816 - task: name: LABELED_DEPENDENCIES type: token-classification metrics: - name: Labeled Attachment Score (LAS) type: f_score value: 0.752849162 - task: name: SENTS type: token-classification metrics: - name: Sentences F-Score type: f_score value: 0.884097035 --- ### Details: https://spacy.io/models/da#da_core_news_sm Danish pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, lemmatizer (trainable_lemmatizer), senter, ner, attribute_ruler. \| Feature \| Description \| \| --- \| --- \| \| Name \| `da_core_news_sm` \| \| Version \| `3.4.0` \| \| spaCy \| `>=3.4.0,<3.5.0` \| \| Default Pipeline \| `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `attribute_ruler`, `ner` \| \| Components \| `tok2vec`, `morphologizer`, `parser`, `lemmatizer`, `senter`, `attribute_ruler`, `ner` \| \| Vectors \| 0 keys, 0 unique vectors (0 dimensions) \| \| Sources \| [UD Danish DDT v2.8](https://github.com/UniversalDependencies/UD_Danish-DDT) (Johannsen, Anders; Martínez Alonso, Héctor; Plank, Barbara)<br />[DaNE](https://github.com/alexandrainst/danlp/blob/master/docs/datasets.md#danish-dependency-treebank-dane) (Rasmus Hvingelby, Amalie B. Pauli, Maria Barrett, Christina Rosted, Lasse M. Lidegaard, Anders Søgaard) \| \| License \| `CC BY-SA 4.0` \| \| Author \| [Explosion](https://explosion.ai) \| ### Label Scheme <details> <summary>View label scheme (194 labels for 3 components)</summary> \| Component \| Labels \| \| --- \| --- \| \| `morphologizer` \| `AdpType=Prep\\|POS=ADP`, `Definite=Ind\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|POS=AUX\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=PROPN`, `Definite=Ind\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=SCONJ`, `Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Act`, `POS=ADV`, `Number=Plur\\|POS=DET\\|PronType=Dem`, `Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Definite=Ind\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `POS=PUNCT`, `POS=CCONJ`, `Definite=Ind\\|Degree=Cmp\\|Number=Sing\\|POS=ADJ`, `Degree=Cmp\\|POS=ADJ`, `POS=PRON\\|PartType=Inf`, `Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Definite=Ind\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Neut\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Degree=Pos\\|POS=ADV`, `Definite=Def\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `POS=PRON\\|PronType=Dem`, `NumType=Card\\|POS=NUM`, `Definite=Ind\\|Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Degree=Pos\\|Gender=Com\\|Number=Sing\\|POS=ADJ`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=3\\|PronType=Prs`, `NumType=Ord\\|POS=ADJ`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Mood=Ind\\|POS=AUX\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=VERB\\|VerbForm=Inf\\|Voice=Act`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Act`, `POS=NOUN`, `Mood=Ind\\|POS=VERB\\|Tense=Pres\\|VerbForm=Fin\\|Voice=Pass`, `POS=ADP\\|PartType=Inf`, `Degree=Pos\\|POS=ADJ`, `Definite=Def\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `POS=AUX\\|VerbForm=Inf\\|Voice=Act`, `Definite=Ind\\|Degree=Pos\\|Gender=Com\\|Number=Sing\\|POS=ADJ`, `Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Number=Plur\\|POS=DET\\|PronType=Ind`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Case=Acc\\|POS=PRON\\|Person=3\\|PronType=Prs\\|Reflex=Yes`, `POS=PART\\|PartType=Inf`, `Gender=Neut\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Acc\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Nom\\|Number=Plur\\|POS=PRON\\|Person=3\\|PronType=Prs`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Nom\\|Gender=Com\\|POS=PRON\\|PronType=Ind`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Ind`, `Mood=Imp\\|POS=VERB`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Definite=Ind\\|Number=Sing\\|POS=AUX\\|Tense=Past\\|VerbForm=Part`, `POS=X`, `Case=Nom\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Definite=Def\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `POS=VERB\\|Tense=Pres\\|VerbForm=Part`, `Number=Plur\\|POS=PRON\\|PronType=Int,Rel`, `POS=VERB\\|VerbForm=Inf\\|Voice=Pass`, `Case=Gen\\|Definite=Ind\\|Gender=Com\\|Number=Sing\\|POS=NOUN`, `Degree=Cmp\\|POS=ADV`, `POS=ADV\\|PartType=Inf`, `Degree=Sup\\|POS=ADV`, `Number=Plur\\|POS=PRON\\|PronType=Dem`, `Number=Plur\\|POS=PRON\\|PronType=Ind`, `Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|POS=PROPN`, `POS=ADP`, `Degree=Cmp\\|Number=Plur\\|POS=ADJ`, `Definite=Def\\|Degree=Sup\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Gender=Com\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Number=Plur\\|POS=PRON\\|PronType=Rcp`, `Case=Gen\\|Degree=Cmp\\|POS=ADJ`, `POS=SPACE`, `Case=Gen\\|Definite=Def\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Plur\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=INTJ`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Degree=Pos\\|Gender=Neut\\|Number=Sing\\|POS=ADJ`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Case=Acc\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Plur\\|POS=NOUN`, `Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `Number=Plur\\|Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `Definite=Def\\|Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Case=Nom\\|Gender=Com\\|Number=Sing\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Number=Sing\\|POS=NOUN`, `Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Number=Plur\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `POS=SYM`, `Case=Nom\\|Gender=Com\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Degree=Sup\\|POS=ADJ`, `Number=Plur\\|POS=DET\\|PronType=Ind\\|Style=Arch`, `Case=Gen\\|Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Dem`, `Foreign=Yes\\|POS=X`, `POS=DET\\|Person=2\\|Polite=Form\\|Poss=Yes\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Case=Acc\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=1\\|PronType=Prs`, `Case=Gen\\|Definite=Ind\\|Gender=Neut\\|Number=Sing\\|POS=NOUN`, `Case=Gen\\|POS=PRON\\|PronType=Int,Rel`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Dem`, `Abbr=Yes\\|POS=X`, `Case=Gen\\|Definite=Ind\\|Gender=Com\\|Number=Plur\\|POS=NOUN`, `Definite=Def\\|Degree=Abs\\|POS=ADJ`, `Definite=Ind\\|Degree=Sup\\|Number=Sing\\|POS=ADJ`, `Definite=Ind\\|POS=NOUN`, `Gender=Com\\|Number=Plur\\|POS=NOUN`, `Number[psor]=Plur\\|POS=DET\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Gender=Com\\|POS=PRON\\|PronType=Int,Rel`, `Case=Nom\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Degree=Abs\\|POS=ADV`, `POS=VERB\\|VerbForm=Ger`, `POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Definite=Def\\|Degree=Sup\\|Number=Sing\\|POS=ADJ`, `Number=Plur\\|Number[psor]=Plur\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs\\|Style=Form`, `Case=Gen\\|Definite=Def\\|Degree=Pos\\|Number=Sing\\|POS=ADJ`, `Case=Gen\\|Degree=Pos\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|POS=PRON\\|Person=2\\|Polite=Form\\|PronType=Prs`, `Gender=Com\\|Number=Sing\\|POS=PRON\\|PronType=Int,Rel`, `POS=VERB\\|Tense=Pres`, `Case=Gen\\|Number=Plur\\|POS=DET\\|PronType=Ind`, `Number[psor]=Plur\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `POS=PRON\\|Person=2\\|Polite=Form\\|Poss=Yes\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `POS=AUX\\|Tense=Pres\\|VerbForm=Part`, `Mood=Ind\\|POS=VERB\\|Tense=Past\\|VerbForm=Fin\\|Voice=Pass`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Degree=Sup\\|Number=Plur\\|POS=ADJ`, `Case=Acc\\|Gender=Com\\|Number=Plur\\|POS=PRON\\|Person=2\\|PronType=Prs`, `Gender=Neut\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs\\|Reflex=Yes`, `Definite=Ind\\|Number=Plur\\|POS=NOUN`, `Case=Gen\\|Number=Plur\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Mood=Imp\\|POS=AUX`, `Gender=Com\\|Number=Sing\\|Number[psor]=Sing\\|POS=PRON\\|Person=1\\|Poss=Yes\\|PronType=Prs`, `Number[psor]=Sing\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `Definite=Def\\|Gender=Com\\|Number=Sing\\|POS=VERB\\|Tense=Past\\|VerbForm=Part`, `Number=Plur\\|Number[psor]=Sing\\|POS=DET\\|Person=2\\|Poss=Yes\\|PronType=Prs`, `Case=Gen\\|Gender=Com\\|Number=Sing\\|POS=DET\\|PronType=Ind`, `Case=Gen\\|POS=NOUN`, `Number[psor]=Plur\\|POS=PRON\\|Person=3\\|Poss=Yes\\|PronType=Prs`, `POS=DET\\|PronType=Dem`, `Definite=Def\\|Number=Plur\\|POS=NOUN` \| \| `parser` \| `ROOT`, `acl:relcl`, `advcl`, `advmod`, `advmod:lmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound:prt`, `conj`, `cop`, `dep`, `det`, `expl`, `fixed`, `flat`, `iobj`, `list`, `mark`, `nmod`, `nmod:poss`, `nsubj`, `nummod`, `obj`, `obl`, `obl:lmod`, `obl:tmod`, `punct`, `xcomp` \| \| `ner` \| `LOC`, `MISC`, `ORG`, `PER` \| </details> ### Accuracy \| Type \| Score \| \| --- \| --- \| \| `TOKEN_ACC` \| 99.95 \| \| `TOKEN_P` \| 99.78 \| \| `TOKEN_R` \| 99.75 \| \| `TOKEN_F` \| 99.76 \| \| `POS_ACC` \| 94.99 \| \| `MORPH_ACC` \| 93.43 \| \| `MORPH_MICRO_P` \| 95.72 \| \| `MORPH_MICRO_R` \| 94.69 \| \| `MORPH_MICRO_F` \| 95.20 \| \| `SENTS_P` \| 89.62 \| \| `SENTS_R` \| 87.23 \| \| `SENTS_F` \| 88.41 \| \| `DEP_UAS` \| 79.89 \| \| `DEP_LAS` \| 75.28 \| \| `LEMMA_ACC` \| 94.50 \| \| `TAG_ACC` \| 94.99 \| \| `ENTS_P` \| 75.70 \| \| `ENTS_R` \| 72.71 \| \| `ENTS_F` \| 74.18 \|
sd-concepts-library/dicoo2	sd-concepts-library	2022-09-21T17:35:48Z	0	1	null	[ "license:mit", "region:us" ]	null	2022-09-21T17:35:43Z	--- license: mit --- ### Dicoo2 on Stable Diffusion This is the `<dicoo>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<dicoo> 0](https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/1.jpeg) ![<dicoo> 1](https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/2.jpeg) ![<dicoo> 2](https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/0.jpeg) ![<dicoo> 3](https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/3.jpeg) ![<dicoo> 4](https://huggingface.co/sd-concepts-library/dicoo2/resolve/main/concept_images/4.jpeg)
research-backup/roberta-large-semeval2012-mask-prompt-d-nce-classification	research-backup	2022-09-21T17:31:01Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T16:59:47Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.796765873015873 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6524064171122995 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6498516320474778 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.7509727626459144 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.902 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6271929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.625 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9246647581738737 - name: F1 (macro) type: f1_macro value: 0.9201116139693363 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8826291079812206 - name: F1 (macro) type: f1_macro value: 0.74506786895136 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7172264355362946 - name: F1 (macro) type: f1_macro value: 0.703292242462215 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9616748974055783 - name: F1 (macro) type: f1_macro value: 0.8934154139843127 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9094327796928863 - name: F1 (macro) type: f1_macro value: 0.906471425124189 --- # relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.6524064171122995 - Accuracy on SAT: 0.6498516320474778 - Accuracy on BATS: 0.7509727626459144 - Accuracy on U2: 0.6271929824561403 - Accuracy on U4: 0.625 - Accuracy on Google: 0.902 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9246647581738737 - Micro F1 score on CogALexV: 0.8826291079812206 - Micro F1 score on EVALution: 0.7172264355362946 - Micro F1 score on K&H+N: 0.9616748974055783 - Micro F1 score on ROOT09: 0.9094327796928863 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.796765873015873 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 30 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-d-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
Harindu/blurr_IMDB_distilbert_classification	Harindu	2022-09-21T17:17:00Z	0	0	fastai	[ "fastai", "region:us" ]	null	2022-09-21T17:16:48Z	--- tags: - fastai --- # Amazing! 🥳 Congratulations on hosting your fastai model on the Hugging Face Hub! # Some next steps 1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))! 2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)). 3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)! Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card. --- # Model card ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed
Sindhana/hotdog-not-hotdog	Sindhana	2022-09-21T17:02:56Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2022-09-21T03:17:01Z	--- title: hotdog not hotdog emoji: 🦀 colorFrom: purple colorTo: purple sdk: gradio sdk_version: 3.1.7 app_file: app.py pinned: false license: apache-2.0 --- Check out the configuration reference at https://huggingface.co/docs/hub/spaces#reference
research-backup/roberta-large-semeval2012-mask-prompt-c-nce-classification	research-backup	2022-09-21T16:59:42Z	103	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T16:17:41Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.5331547619047619 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.2914438502673797 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.29080118694362017 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3913285158421345 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.486 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.33771929824561403 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.3263888888888889 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8392345939430466 - name: F1 (macro) type: f1_macro value: 0.8259066607574465 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.7570422535211268 - name: F1 (macro) type: f1_macro value: 0.43666662077729007 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.5926327193932828 - name: F1 (macro) type: f1_macro value: 0.5763337381530251 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9392780134937748 - name: F1 (macro) type: f1_macro value: 0.8298559683420568 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8934503290504543 - name: F1 (macro) type: f1_macro value: 0.8858359126040442 --- # relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.2914438502673797 - Accuracy on SAT: 0.29080118694362017 - Accuracy on BATS: 0.3913285158421345 - Accuracy on U2: 0.33771929824561403 - Accuracy on U4: 0.3263888888888889 - Accuracy on Google: 0.486 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.8392345939430466 - Micro F1 score on CogALexV: 0.7570422535211268 - Micro F1 score on EVALution: 0.5926327193932828 - Micro F1 score on K&H+N: 0.9392780134937748 - Micro F1 score on ROOT09: 0.8934503290504543 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.5331547619047619 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: Today, I finally discovered the relation between <subj> and <obj> : <mask> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 1 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-c-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
SzegedAI/charmen-electra	SzegedAI	2022-09-21T16:42:21Z	106	1	transformers	[ "transformers", "pytorch", "feature-extraction", "byte representation", "gradient boosting", "hungarian", "custom_code", "hu", "dataset:common_crawl", "dataset:wikipedia", "license:apache-2.0", "region:us" ]	feature-extraction	2022-08-27T10:17:26Z	--- language: hu license: apache-2.0 datasets: - common_crawl - wikipedia tags: - byte representation - gradient boosting - hungarian --- # Charmen-Electra A byte-based transformer model trained on Hungarian language. In order to use the model you will need a custom Tokenizer which is available at: [https://github.com/szegedai/byte-offset-tokenizer](https://github.com/szegedai/byte-offset-tokenizer). Since we use a custom architecture with Gradient Boosting, Down- and Up-Sampling, you have to enable Trusted Remote Code like: ```python model = AutoModel.from_pretrained("SzegedAI/charmen-electra", trust_remote_code=True) ``` # Acknowledgement [![Artificial Intelligence - National Laboratory - Hungary](https://milab.tk.hu/uploads/images/milab_logo_en.png)](https://mi.nemzetilabor.hu/)
sd-concepts-library/sherhook-painting	sd-concepts-library	2022-09-21T16:41:10Z	0	4	null	[ "license:mit", "region:us" ]	null	2022-09-21T16:41:04Z	--- license: mit --- ### Sherhook Painting on Stable Diffusion This is the `<sherhook>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as a `style`: ![<sherhook> 0](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/1.jpeg) ![<sherhook> 1](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/2.jpeg) ![<sherhook> 2](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/0.jpeg) ![<sherhook> 3](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/3.jpeg) ![<sherhook> 4](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/4.jpeg) ![<sherhook> 5](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/5.jpeg) ![<sherhook> 6](https://huggingface.co/sd-concepts-library/sherhook-painting/resolve/main/concept_images/6.jpeg)
research-backup/roberta-large-semeval2012-mask-prompt-b-nce-classification	research-backup	2022-09-21T16:17:35Z	104	0	transformers	[ "transformers", "pytorch", "roberta", "feature-extraction", "dataset:relbert/semeval2012_relational_similarity", "model-index", "text-embeddings-inference", "endpoints_compatible", "region:us" ]	feature-extraction	2022-09-21T15:45:17Z	--- datasets: - relbert/semeval2012_relational_similarity model-index: - name: relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification results: - task: name: Relation Mapping type: sorting-task dataset: name: Relation Mapping args: relbert/relation_mapping type: relation-mapping metrics: - name: Accuracy type: accuracy value: 0.7908730158730158 - task: name: Analogy Questions (SAT full) type: multiple-choice-qa dataset: name: SAT full args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5080213903743316 - task: name: Analogy Questions (SAT) type: multiple-choice-qa dataset: name: SAT args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5192878338278932 - task: name: Analogy Questions (BATS) type: multiple-choice-qa dataset: name: BATS args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.6653696498054474 - task: name: Analogy Questions (Google) type: multiple-choice-qa dataset: name: Google args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.84 - task: name: Analogy Questions (U2) type: multiple-choice-qa dataset: name: U2 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.45614035087719296 - task: name: Analogy Questions (U4) type: multiple-choice-qa dataset: name: U4 args: relbert/analogy_questions type: analogy-questions metrics: - name: Accuracy type: accuracy value: 0.5393518518518519 - task: name: Lexical Relation Classification (BLESS) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9132138014163026 - name: F1 (macro) type: f1_macro value: 0.9101733559621606 - task: name: Lexical Relation Classification (CogALexV) type: classification dataset: name: CogALexV args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.8502347417840377 - name: F1 (macro) type: f1_macro value: 0.6852576593859314 - task: name: Lexical Relation Classification (EVALution) type: classification dataset: name: BLESS args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.6852654387865655 - name: F1 (macro) type: f1_macro value: 0.6694360423727916 - task: name: Lexical Relation Classification (K&H+N) type: classification dataset: name: K&H+N args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9604228976838005 - name: F1 (macro) type: f1_macro value: 0.8826948107609662 - task: name: Lexical Relation Classification (ROOT09) type: classification dataset: name: ROOT09 args: relbert/lexical_relation_classification type: relation-classification metrics: - name: F1 type: f1 value: 0.9022250078345346 - name: F1 (macro) type: f1_macro value: 0.9002463330589072 --- # relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on [relbert/semeval2012_relational_similarity](https://huggingface.co/datasets/relbert/semeval2012_relational_similarity). Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail). It achieves the following results on the relation understanding tasks: - Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification/raw/main/analogy.json)): - Accuracy on SAT (full): 0.5080213903743316 - Accuracy on SAT: 0.5192878338278932 - Accuracy on BATS: 0.6653696498054474 - Accuracy on U2: 0.45614035087719296 - Accuracy on U4: 0.5393518518518519 - Accuracy on Google: 0.84 - Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification/raw/main/classification.json)): - Micro F1 score on BLESS: 0.9132138014163026 - Micro F1 score on CogALexV: 0.8502347417840377 - Micro F1 score on EVALution: 0.6852654387865655 - Micro F1 score on K&H+N: 0.9604228976838005 - Micro F1 score on ROOT09: 0.9022250078345346 - Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification/raw/main/relation_mapping.json)): - Accuracy on Relation Mapping: 0.7908730158730158 ### Usage This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip ```shell pip install relbert ``` and activate model as below. ```python from relbert import RelBERT model = RelBERT("relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification") vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, ) ``` ### Training hyperparameters The following hyperparameters were used during training: - model: roberta-large - max_length: 64 - mode: mask - data: relbert/semeval2012_relational_similarity - split: train - data_eval: relbert/conceptnet_high_confidence - split_eval: full - template_mode: manual - template: Today, I finally discovered the relation between <subj> and <obj> : <obj> is <subj>'s <mask> - loss_function: nce_logout - classification_loss: True - temperature_nce_constant: 0.05 - temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'} - epoch: 27 - batch: 128 - lr: 5e-06 - lr_decay: False - lr_warmup: 1 - weight_decay: 0 - random_seed: 0 - exclude_relation: None - exclude_relation_eval: None - n_sample: 640 - gradient_accumulation: 8 The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-semeval2012-mask-prompt-b-nce-classification/raw/main/trainer_config.json). ### Reference If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/). ``` @inproceedings{ushio-etal-2021-distilling-relation-embeddings, title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels", author = "Ushio, Asahi and Schockaert, Steven and Camacho-Collados, Jose", booktitle = "EMNLP 2021", year = "2021", address = "Online", publisher = "Association for Computational Linguistics", } ```
sd-concepts-library/detectivedinosaur1	sd-concepts-library	2022-09-21T16:06:29Z	0	0	null	[ "license:mit", "region:us" ]	null	2022-09-21T16:06:18Z	--- license: mit --- ### detectivedinosaur1 on Stable Diffusion This is the `<dd1>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). Here is the new concept you will be able to use as an `object`: ![<dd1> 0](https://huggingface.co/sd-concepts-library/detectivedinosaur1/resolve/main/concept_images/1.jpeg) ![<dd1> 1](https://huggingface.co/sd-concepts-library/detectivedinosaur1/resolve/main/concept_images/2.jpeg) ![<dd1> 2](https://huggingface.co/sd-concepts-library/detectivedinosaur1/resolve/main/concept_images/0.jpeg)
teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage	teven	2022-09-21T15:53:15Z	4	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T15:53:08Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage 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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage') 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('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage') model = AutoModel.from_pretrained('teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage') # 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=teven/cross_all-mpnet-base-v2_finetuned_WebNLG2020_data_coverage) ## 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 -->
teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage	teven	2022-09-21T15:52:36Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T15:52:29Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage 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('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage') 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('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage') model = AutoModel.from_pretrained('teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage') # 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=teven/cross_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage) ## 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 -->
teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage	teven	2022-09-21T15:52:01Z	6	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "transformers", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T15:51:53Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity - transformers --- # teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage 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('teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage') 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('teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage') model = AutoModel.from_pretrained('teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage') # 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=teven/cross_all_bs160_allneg_finetuned_WebNLG2020_data_coverage) ## 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 -->
teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage	teven	2022-09-21T15:50:15Z	3	0	sentence-transformers	[ "sentence-transformers", "pytorch", "mpnet", "feature-extraction", "sentence-similarity", "autotrain_compatible", "endpoints_compatible", "region:us" ]	sentence-similarity	2022-09-21T15:50:08Z	--- pipeline_tag: sentence-similarity tags: - sentence-transformers - feature-extraction - sentence-similarity --- # teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage 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('teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage') 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=teven/bi_all_bs192_hardneg_finetuned_WebNLG2020_data_coverage) ## Training The model was trained with the parameters: DataLoader: `torch.utils.data.dataloader.DataLoader` of length 161 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": 50, "evaluation_steps": 0, "evaluator": "better_cross_encoder.PearsonCorrelationEvaluator", "max_grad_norm": 1, "optimizer_class": "<class 'transformers.optimization.AdamW'>", "optimizer_params": { "lr": 5e-05 }, "scheduler": "warmupcosine", "steps_per_epoch": null, "warmup_steps": 805, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 384, '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 -->
tianchez/autotrain-line_clip_no_nut_boltline_clip_no_nut_bolt-1523955096	tianchez	2022-09-21T15:49:25Z	196	0	transformers	[ "transformers", "pytorch", "autotrain", "vision", "image-classification", "dataset:tianchez/autotrain-data-line_clip_no_nut_boltline_clip_no_nut_bolt", "co2_eq_emissions", "endpoints_compatible", "region:us" ]	image-classification	2022-09-21T15:42:51Z	--- tags: - autotrain - vision - image-classification datasets: - tianchez/autotrain-data-line_clip_no_nut_boltline_clip_no_nut_bolt widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg example_title: Tiger - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg example_title: Teapot - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg example_title: Palace co2_eq_emissions: emissions: 10.423410288264847 --- # Model Trained Using AutoTrain - Problem type: Multi-class Classification - Model ID: 1523955096 - CO2 Emissions (in grams): 10.4234 ## Validation Metrics - Loss: 0.580 - Accuracy: 0.798 - Macro F1: 0.542 - Micro F1: 0.798 - Weighted F1: 0.796 - Macro Precision: 0.548 - Micro Precision: 0.798 - Weighted Precision: 0.796 - Macro Recall: 0.537 - Micro Recall: 0.798 - Weighted Recall: 0.798

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