Datasets:
davanstrien
/
pytorchmodelmetadata

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anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-10
e1ceedfa951df0d8f187dcaf4f790dd6c68cfbbb
2022-02-25T21:57:46.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-10
2
null
transformers
25,000
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-10 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. --> # spanbert-base-cased-few-shot-k-32-finetuned-squad-seed-10 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0
e1fd14b9584645a16e498c3e0103207b448f42d4
2022-02-25T22:13:00.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0
2
null
transformers
25,001
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0 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. --> # spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-0 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-4
482ae785c05c6432fad650ec4ca3a315e543bb0b
2022-02-25T22:43:24.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-4
2
null
transformers
25,002
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-4 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. --> # spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-4 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10
49f67379b563ffc66eb8cabf02660a57f7673a33
2022-02-25T23:29:09.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10
2
null
transformers
25,003
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10 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. --> # spanbert-base-cased-few-shot-k-64-finetuned-squad-seed-10 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-6
b61ea1b45497c6ad359fe54f5b69021ae5c0768b
2022-02-26T04:38:59.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-6
2
null
transformers
25,004
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-6 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. --> # spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-6 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10
6a232d73710560a3bdffc8fbfb046f5da0a289ce
2022-02-26T05:09:28.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10
2
null
transformers
25,005
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10 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. --> # spanbert-base-cased-few-shot-k-128-finetuned-squad-seed-10 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - training_steps: 200 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-6
bf970c345f8cb94c26c01521b1e9e2c4e78f4c10
2022-02-26T06:07:51.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-6
2
null
transformers
25,006
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-6 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. --> # spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-6 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-8
74c1a6c1138a3893b05b52eee06ce28e48e70123
2022-02-26T06:21:42.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-8
2
null
transformers
25,007
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-8 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. --> # spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-8 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10
35336bbef5989f88a390614216345bca96e226a1
2022-02-26T06:36:19.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10
2
null
transformers
25,008
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10 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. --> # spanbert-base-cased-few-shot-k-256-finetuned-squad-seed-10 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0
8e6ebc5d9540043085a575456a0056e5751efc7c
2022-02-26T06:51:47.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0
2
null
transformers
25,009
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0 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. --> # spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-0 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-2
272a54f457eda5f2d434ef41df062d40658b2e94
2022-02-26T07:07:11.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-2
2
null
transformers
25,010
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-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. --> # spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-2 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-4
210ecf98963c50da90df7798ad43c590856503a7
2022-02-26T07:22:34.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-4
2
null
transformers
25,011
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-4 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. --> # spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-4 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10
e7fea6a07e1a190cd7ab2b37810d34559d5fd220
2022-02-26T08:08:44.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10
2
null
transformers
25,012
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10 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. --> # spanbert-base-cased-few-shot-k-512-finetuned-squad-seed-10 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-4
39319a478bf9b662e61dca3b26adc6716bc6a650
2022-02-26T08:59:53.000Z
[ "pytorch", "bert", "question-answering", "dataset:squad", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
question-answering
false
anas-awadalla
null
anas-awadalla/spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-4
2
null
transformers
25,013
--- tags: - generated_from_trainer datasets: - squad model-index: - name: spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-4 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. --> # spanbert-base-cased-few-shot-k-1024-finetuned-squad-seed-4 This model is a fine-tuned version of [SpanBERT/spanbert-base-cased](https://huggingface.co/SpanBERT/spanbert-base-cased) on the squad dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 24 - eval_batch_size: 24 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 10.0 ### Training results ### Framework versions - Transformers 4.16.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.17.0 - Tokenizers 0.10.3
cyl/bitfit_t5-3b_cola
45dce910d53af3ef6feab0ff658e2d67c3cf9723
2022-02-26T18:53:54.000Z
[ "pytorch", "transformers" ]
null
false
cyl
null
cyl/bitfit_t5-3b_cola
2
null
transformers
25,014
Entry not found
Daryaflp/roberta-retrained_ru_covid
1fedc1684c0fde7a229571d7913185b41a54ce0d
2022-02-27T16:18:22.000Z
[ "pytorch", "tensorboard", "roberta", "fill-mask", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
fill-mask
false
Daryaflp
null
Daryaflp/roberta-retrained_ru_covid
2
null
transformers
25,015
--- tags: - generated_from_trainer model-index: - name: roberta-retrained_ru_covid 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-retrained_ru_covid This model is a fine-tuned version of [blinoff/roberta-base-russian-v0](https://huggingface.co/blinoff/roberta-base-russian-v0) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8518 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 1 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 25 ### Training results ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.0
Hallzy/Peterbot
5b71700f504933b2d6a928e4692a016d8f92f99f
2022-02-26T23:33:57.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Hallzy
null
Hallzy/Peterbot
2
null
transformers
25,016
--- tags: - conversational --- # Peter from Your Boyfriend Game.
patrickvonplaten/wav2vec2-base-es-voxpopuli-v2
89d794100f6fced00f7d1ba8a46b40785f97156a
2022-02-27T00:31:53.000Z
[ "pytorch", "wav2vec2", "pretraining", "transformers", "correct" ]
null
false
patrickvonplaten
null
patrickvonplaten/wav2vec2-base-es-voxpopuli-v2
2
null
transformers
25,017
--- tags: - correct --- Test
abhinema/gpt-medium
4a20a1b2de79f2825c929a524a04e9e3c8cb5dab
2022-03-04T04:21:57.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
abhinema
null
abhinema/gpt-medium
2
null
transformers
25,018
Entry not found
Camzure/MaamiBot-test
de202135f518fa58007c086d18dae7bda5516cd2
2022-02-27T12:40:31.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Camzure
null
Camzure/MaamiBot-test
2
null
transformers
25,019
--- tags: - conversational --- # MaamiBot
maretamasaeva/roberta-finetuned-freeform
f0d0fd75935057cb37c61852fc8e16ed0725515f
2022-03-29T14:19:27.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
maretamasaeva
null
maretamasaeva/roberta-finetuned-freeform
2
null
transformers
25,020
--- license: mit tags: - generated_from_trainer metrics: - accuracy model-index: - name: roberta-finetuned-freeform 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-finetuned-freeform This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6989 - Accuracy: 0.4668 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.6919 | 1.0 | 8094 | 0.6910 | 0.4668 | | 0.6912 | 2.0 | 16188 | 0.6934 | 0.4668 | | 0.6904 | 3.0 | 24282 | 0.6976 | 0.4668 | | 0.6918 | 4.0 | 32376 | 0.6989 | 0.4668 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.11.6
kazandaev/opus-mt-en-ru-finetuned_v2
148730949a774fc76a4564b58c07a46e8aab70f3
2022-03-04T14:25:54.000Z
[ "pytorch", "tensorboard", "rust", "marian", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
kazandaev
null
kazandaev/opus-mt-en-ru-finetuned_v2
2
null
transformers
25,021
--- license: apache-2.0 tags: - generated_from_trainer metrics: - bleu model-index: - name: opus-mt-en-ru-finetuned_v2 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. --> # opus-mt-en-ru-finetuned_v2 This model is a fine-tuned version of [kazandaev/opus-mt-en-ru-finetuned_v2](https://huggingface.co/kazandaev/opus-mt-en-ru-finetuned_v2) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.8471 - Bleu: 37.5148 - Gen Len: 29.8495 ## 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-06 - train_batch_size: 49 - eval_batch_size: 24 - 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 | Bleu | Gen Len | |:-------------:|:-----:|:------:|:---------------:|:-------:|:-------:| | 0.7688 | 1.0 | 50906 | 0.8533 | 37.1941 | 29.8644 | | 0.764 | 2.0 | 101812 | 0.8504 | 37.1506 | 29.8481 | | 0.7637 | 3.0 | 152718 | 0.8485 | 37.3499 | 29.7743 | | 0.7593 | 4.0 | 203624 | 0.8477 | 37.4428 | 29.8165 | | 0.7579 | 5.0 | 254530 | 0.8471 | 37.5148 | 29.8495 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
mipatov/rut5_nb_descr
1a56358f35bbfc3b3cddb9f2d4091086aeec8c78
2022-02-27T23:43:38.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
mipatov
null
mipatov/rut5_nb_descr
2
null
transformers
25,022
based on `sberbank-ai/ruT5-large` finetuned for generate text description for notebook-devices
danny911kr/tapas_simsiam_mlm_2
1d9b75a0a248494944afb1ff9d1f787d1e33aa13
2022-02-28T03:10:18.000Z
[ "pytorch", "tapas", "feature-extraction", "transformers" ]
feature-extraction
false
danny911kr
null
danny911kr/tapas_simsiam_mlm_2
2
null
transformers
25,023
Entry not found
junnyu/flashquad_small_wwm_cluecorpussmall
8f38e97e2ff46203bd77775439d4e4f377c39321
2022-02-28T03:41:06.000Z
[ "pytorch", "flash_quad", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
junnyu
null
junnyu/flashquad_small_wwm_cluecorpussmall
2
null
transformers
25,024
Entry not found
neal49/distilbert-sst2-freeze
fa2a47c03c88e8b5a1146a93994a882b941089fe
2022-02-28T23:19:44.000Z
[ "pytorch", "distilbert", "text-classification", "transformers" ]
text-classification
false
neal49
null
neal49/distilbert-sst2-freeze
2
null
transformers
25,025
Entry not found
facebook/wav2vec2-base-ro-voxpopuli-v2
48eb760786462f824757a5237f5968359c979795
2022-02-27T13:12:40.000Z
[ "pytorch", "wav2vec2", "pretraining", "ro", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-ro-voxpopuli-v2
2
null
transformers
25,026
--- language: ro tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **ro** on **17.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **ro**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-it-voxpopuli-v2
ca53d26733f609622fc37999ddfd4c832257d5c4
2022-02-27T13:12:17.000Z
[ "pytorch", "wav2vec2", "pretraining", "it", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-it-voxpopuli-v2
2
null
transformers
25,027
--- language: it tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **it** on **21.9k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **it**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-de-voxpopuli-v2
e0b603594e0d27db511346c91f7602a7b8db03a3
2022-02-27T13:13:15.000Z
[ "pytorch", "wav2vec2", "pretraining", "de", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-de-voxpopuli-v2
2
null
transformers
25,028
--- language: de tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **de** on **23.2k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **de**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-sk-voxpopuli-v2
3a32a0746ade1ad6c6ab9071a85ca68cb48f7339
2022-02-27T13:14:37.000Z
[ "pytorch", "wav2vec2", "pretraining", "sk", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-sk-voxpopuli-v2
2
null
transformers
25,029
--- language: sk tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **sk** on **12.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **sk**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-et-voxpopuli-v2
06e29dd8ae82fa8d2c632a0d44b3fff6719caf50
2022-02-27T13:14:58.000Z
[ "pytorch", "wav2vec2", "pretraining", "et", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-et-voxpopuli-v2
2
null
transformers
25,030
--- language: et tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **et** on **10.6k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **et**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-lv-voxpopuli-v2
66d92c48e1ea737a638de13916dba5148b4a968e
2022-02-27T13:15:26.000Z
[ "pytorch", "wav2vec2", "pretraining", "lv", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-lv-voxpopuli-v2
2
null
transformers
25,031
--- language: lv tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **lv** on **13.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **lv**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-uralic-voxpopuli-v2
fec5ee0ce1419a5c13161d159fb2ad01253fbcb0
2022-02-27T12:43:18.000Z
[ "pytorch", "wav2vec2", "pretraining", "uralic", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-large-uralic-voxpopuli-v2
2
null
transformers
25,032
--- language: uralic tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **uralic** on **42.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **uralic**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-base-hu-voxpopuli-v2
6d5f81b1b6f255b3858c09bbfe4edc6d2dfa34db
2022-02-27T13:15:17.000Z
[ "pytorch", "wav2vec2", "pretraining", "hu", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-base-hu-voxpopuli-v2
2
null
transformers
25,033
--- language: hu tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-base-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **hu** on **17.7k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **hu**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
facebook/wav2vec2-large-romance-voxpopuli-v2
f80097ef4718f536951758ff56603fa1057f010e
2022-02-27T12:32:07.000Z
[ "pytorch", "wav2vec2", "pretraining", "romance", "dataset:voxpopuli", "arxiv:2101.00390", "transformers", "audio", "automatic-speech-recognition", "voxpopuli-v2", "license:cc-by-nc-4.0" ]
automatic-speech-recognition
false
facebook
null
facebook/wav2vec2-large-romance-voxpopuli-v2
2
null
transformers
25,034
--- language: romance tags: - audio - automatic-speech-recognition - voxpopuli-v2 datasets: - voxpopuli license: cc-by-nc-4.0 inference: false --- # Wav2Vec2-large-VoxPopuli-V2 [Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) large model pretrained only in **romance** on **101.5** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390). The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **romance**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model. **Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)* **Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*. See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
niksmer/ManiBERT
0a00871eda1f3756ba1d3e8f8f7d5e758413974b
2022-03-24T09:03:13.000Z
[ "pytorch", "roberta", "text-classification", "transformers", "license:mit", "model-index" ]
text-classification
false
niksmer
null
niksmer/ManiBERT
2
null
transformers
25,035
--- license: mit metrics: - accuracy - precision - recall model-index: - name: ManiBERT results: [] widget: - text: "Russia must end the war." - text: "Democratic institutions must be supported." - text: "The state must fight political corruption." - text: "Our energy economy must be nationalised." - text: "We must increase social spending." --- # ManiBERT This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). ## Model description This model was trained on 115,943 manually annotated sentences to classify text into one of 56 political categories: ## Intended uses & limitations The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance. ```python from transformers import pipeline import pandas as pd classifier = pipeline( task="text-classification", model="niksmer/ManiBERT") # Load text data you want to classify text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list() # Inference output = classifier(text) # Print output pd.DataFrame(output).head() ``` ## Train Data ManiBERT was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020. | Country | Count manifestos | Count sentences | Time span | |----------------|------------------|-----------------|--------------------| | Australia | 18 | 14,887 | 2010-2016 | | Ireland | 23 | 24,966 | 2007-2016 | | Canada | 14 | 12,344 | 2004-2008 & 2015 | | New Zealand | 46 | 35,079 | 1993-2017 | | South Africa | 29 | 13,334 | 1994-2019 | | USA | 9 | 13,188 | 1992 & 2004-2020 | | United Kingdom | 34 | 30,936 | 1997-2019 | Canadian manifestos between 2004 and 2008 are used as test data. The resulting Datasets are higly (!) imbalanced. See Evaluation. ## Evaluation | Description | Label | Count Train Data | Count Validation Data | Count Test Data | Validation F1-Score | Test F1-Score | |-------------------------------------------------------------------|-------|------------------|-----------------------|-----------------|---------------------|---------------| | Foreign Special Relationships: Positive | 0 | 545 | 96 | 60 | 0.43 | 0.45 | | Foreign Special Relationships: Negative | 1 | 66 | 14 | 22 | 0.22 | 0.09 | | Anti-Imperialism | 2 | 93 | 16 | 1 | 0.16 | 0.00 | | Military: Positive | 3 | 1,969 | 356 | 159 | 0.69 | 0.63 | | Military: Negative | 4 | 489 | 89 | 52 | 0.59 | 0.63 | | Peace | 5 | 418 | 80 | 49 | 0.57 | 0.64 | | Internationalism: Positive | 6 | 2,401 | 417 | 404 | 0.60 | 0.54 | | European Community/Union or Latin America Integration: Positive | 7 | 930 | 156 | 20 | 0.58 | 0.32 | | Internationalism: Negative | 8 | 209 | 40 | 57 | 0.28 | 0.05 | | European Community/Union or Latin America Integration: Negative | 9 | 520 | 81 | 0 | 0.39 | - | | Freedom and Human Rights | 10 | 2,196 | 389 | 76 | 0.50 | 0.34 | | Democracy | 11 | 3,045 | 534 | 206 | 0.53 | 0.51 | | Constitutionalism: Positive | 12 | 259 | 48 | 12 | 0.34 | 0.22 | | Constitutionalism: Negative | 13 | 380 | 72 | 2 | 0.34 | 0.00 | | Decentralisation: Positive | 14 | 2,791 | 481 | 331 | 0.49 | 0.45 | | Centralisation: Positive | 15 | 150 | 33 | 71 | 0.11 | 0.00 | | Governmental and Administrative Efficiency | 16 | 3,905 | 711 | 105 | 0.50 | 0.32 | | Political Corruption | 17 | 900 | 186 | 234 | 0.59 | 0.55 | | Political Authority | 18 | 3,488 | 627 | 300 | 0.51 | 0.39 | | Free Market Economy | 19 | 1,768 | 309 | 53 | 0.40 | 0.16 | | Incentives: Positive | 20 | 3,100 | 544 | 81 | 0.52 | 0.28 | | Market Regulation | 21 | 3,562 | 616 | 210 | 0.50 | 0.36 | | Economic Planning | 22 | 533 | 93 | 67 | 0.31 | 0.12 | | Corporatism/ Mixed Economy | 23 | 193 | 32 | 23 | 0.28 | 0.33 | | Protectionism: Positive | 24 | 633 | 103 | 180 | 0.44 | 0.22 | | Protectionism: Negative | 25 | 723 | 118 | 149 | 0.52 | 0.40 | | Economic Goals | 26 | 817 | 139 | 148 | 0.05 | 0.00 | | Keynesian Demand Management | 27 | 160 | 25 | 9 | 0.00 | 0.00 | | Economic Growth: Positive | 28 | 3,142 | 607 | 374 | 0.53 | 0.30 | | Technology and Infrastructure: Positive | 29 | 8,643 | 1,529 | 339 | 0.71 | 0.56 | | Controlled Economy | 30 | 567 | 96 | 94 | 0.47 | 0.16 | | Nationalisation | 31 | 832 | 157 | 27 | 0.56 | 0.16 | | Economic Orthodoxy | 32 | 1,721 | 287 | 184 | 0.55 | 0.48 | | Marxist Analysis: Positive | 33 | 148 | 33 | 0 | 0.20 | - | | Anti-Growth Economy and Sustainability | 34 | 2,676 | 452 | 250 | 0.43 | 0.33 | | Environmental Protection | 35 | 6,731 | 1,163 | 934 | 0.70 | 0.67 | | Culture: Positive | 36 | 2,082 | 358 | 92 | 0.69 | 0.56 | | Equality: Positive | 37 | 6,630 | 1,126 | 361 | 0.57 | 0.43 | | Welfare State Expansion | 38 | 13,486 | 2,405 | 990 | 0.72 | 0.61 | | Welfare State Limitation | 39 | 926 | 151 | 2 | 0.45 | 0.00 | | Education Expansion | 40 | 7,191 | 1,324 | 274 | 0.78 | 0.63 | | Education Limitation | 41 | 154 | 27 | 1 | 0.17 | 0.00 | | National Way of Life: Positive | 42 | 2,105 | 385 | 395 | 0.48 | 0.34 | | National Way of Life: Negative | 43 | 743 | 147 | 2 | 0.27 | 0.00 | | Traditional Morality: Positive | 44 | 1,375 | 234 | 19 | 0.55 | 0.14 | | Traditional Morality: Negative | 45 | 291 | 54 | 38 | 0.30 | 0.23 | | Law and Order | 46 | 5,582 | 949 | 381 | 0.72 | 0.71 | | Civic Mindedness: Positive | 47 | 1,348 | 229 | 27 | 0.45 | 0.28 | | Multiculturalism: Positive | 48 | 2,006 | 355 | 71 | 0.61 | 0.35 | | Multiculturalism: Negative | 49 | 144 | 31 | 7 | 0.33 | 0.00 | | Labour Groups: Positive | 50 | 3,856 | 707 | 57 | 0.64 | 0.14 | | Labour Groups: Negative | 51 | 208 | 35 | 0 | 0.44 | - | | Agriculture and Farmers | 52 | 2,996 | 490 | 130 | 0.67 | 0.56 | | Middle Class and Professional Groups | 53 | 271 | 38 | 12 | 0.38 | 0.40 | | Underprivileged Minority Groups | 54 | 1,417 | 252 | 82 | 0.34 | 0.33 | | Non-economic Demographic Groups | 55 | 2,429 | 435 | 106 | 0.42 | 0.24 | ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: ``` training_args = TrainingArguments( warmup_ratio=0.05, weight_decay=0.1, learning_rate=5e-05, fp16 = True, evaluation_strategy="epoch", num_train_epochs=5, per_device_train_batch_size=16, overwrite_output_dir=True, per_device_eval_batch_size=16, save_strategy="no", logging_dir='logs', logging_strategy= 'steps', logging_steps=10, push_to_hub=True, hub_strategy="end") ``` ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:| | 1.7638 | 1.0 | 1812 | 1.6471 | 0.5531 | 0.5531 | 0.3354 | 0.5368 | 0.5531 | 0.5531 | | 1.4501 | 2.0 | 3624 | 1.5167 | 0.5807 | 0.5807 | 0.3921 | 0.5655 | 0.5807 | 0.5807 | | 1.0638 | 3.0 | 5436 | 1.5017 | 0.5893 | 0.5893 | 0.4240 | 0.5789 | 0.5893 | 0.5893 | | 0.9263 | 4.0 | 7248 | 1.5173 | 0.5975 | 0.5975 | 0.4499 | 0.5901 | 0.5975 | 0.5975 | | 0.7859 | 5.0 | 9060 | 1.5574 | 0.5978 | 0.5978 | 0.4564 | 0.5903 | 0.5978 | 0.5978 | ### Overall evaluation | Type | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | Validation | 0.60 | 0.46 | 0.59 | | Test | 0.48 | 0.30 | 0.47 | ### Evaluation based on saliency theory Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent. Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology. The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [RoBERTa-RILE](https://huggingface.co/niksmer/RoBERTa-RILE). ![image](english_manibert_manifesto.png) ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.0+cu102 - Datasets 1.8.0 - Tokenizers 0.10.3
niksmer/RoBERTa-RILE
7249994599dd123862e64026d3517e98be502e9f
2022-03-24T09:19:40.000Z
[ "pytorch", "roberta", "text-classification", "transformers", "license:mit", "model-index" ]
text-classification
false
niksmer
null
niksmer/RoBERTa-RILE
2
null
transformers
25,036
--- license: mit metrics: - accuracy - precision - recall model-index: - name: RoBERTa-RILE results: [] widget: - text: "Russia must end the war." - text: "Democratic institutions must be supported." - text: "The state must fight political corruption." - text: "Our energy economy must be nationalised." - text: "We must increase social spending." --- # RoBERTa-RILE This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on data from the [Manifesto Project](https://manifesto-project.wzb.eu/). ## Model description This model was trained on 115,943 manually annotated sentences to classify text into one of three political categories: "neutral", "left", "right". ## Intended uses & limitations The model output reproduces the limitations of the dataset in terms of country coverage, time span, domain definitions and potential biases of the annotators - as any supervised machine learning model would. Applying the model to other types of data (other types of texts, countries etc.) will reduce performance. ```python from transformers import pipeline import pandas as pd classifier = pipeline( task="text-classification", model="niksmer/RoBERTa-RILE") # Load text data you want to classify text = pd.read_csv("example.csv")["text_you_want_to_classify"].to_list() # Inference output = classifier(text) # Print output pd.DataFrame(output).head() ``` ## Training and evaluation data ## Training and evaluation data RoBERTa-RILE was trained on the English-speaking subset of the [Manifesto Project Dataset (MPDS2021a)](https://manifesto-project.wzb.eu/datasets). The model was trained on 115,943 sentences from 163 political manifestos in 7 English-speaking countries (Australia, Canada, Ireland, New Zealand, South Africa, United Kingdom, United States). The manifestos were published between 1992 - 2020. | Country | Count manifestos | Count sentences | Time span | |----------------|------------------|-----------------|--------------------| | Australia | 18 | 14,887 | 2010-2016 | | Ireland | 23 | 24,966 | 2007-2016 | | Canada | 14 | 12,344 | 2004-2008 & 2015 | | New Zealand | 46 | 35,079 | 1993-2017 | | South Africa | 29 | 13,334 | 1994-2019 | | USA | 9 | 13,188 | 1992 & 2004-2020 | | United Kingdom | 34 | 30,936 | 1997-2019 | Canadian manifestos between 2004 and 2008 are used as test data. The Manifesto Project mannually annotates individual sentences from political party manifestos in over 50 main categories - see the [codebook](https://manifesto-project.wzb.eu/down/papers/handbook_2021_version_5.pdf) for the exact definitions of each categorie. It has created a valid left-right-scale, the rile-index, to aaggregate manifesto in a standardized, onde-dimensional political space from left to right based on saliency-theory. RoBERTa-RILE classifies texts based on the rile index. ### Tain data Train data was slightly imbalanced. | Label | Description | Count | |------------|--------------|--------| | 0 | neutral | 52,277 | | 1 | left | 37,106 | | 2 | right | 26,560 | Overall count: 115,943 ### Validation data The validation was created by chance. | Label | Description | Count | |------------|--------------|--------| | 0 | neutral | 9,198 | | 1 | left | 6,637 | | 2 | right | 4,626 | Overall count: 20,461 ### Test data The test dataset contains ten canadian manifestos between 2004 and 2008. | Label | Description | Count | |------------|--------------|--------| | 0 | neutral | 3,881 | | 1 | left | 2,611 | | 2 | right | 1,838 | Overall count: 8,330 ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: ``` training_args = TrainingArguments( warmup_ratio=0.05, weight_decay=0.1, learning_rate=1e-05, fp16 = True, evaluation_strategy="epoch", num_train_epochs=5, per_device_train_batch_size=16, per_device_eval_batch_size=16, save_strategy="no", logging_dir='logs', logging_strategy= 'steps', logging_steps=10, push_to_hub=True, hub_strategy="end") ``` ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1-micro | F1-macro | F1-weighted | Precision | Recall | |:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:-----------:|:---------:|:------:| | 0.7442 | 1.0 | 1812 | 0.6827 | 0.7120 | 0.7120 | 0.7007 | 0.7126 | 0.7120 | 0.7120 | | 0.6447 | 2.0 | 3624 | 0.6618 | 0.7281 | 0.7281 | 0.7169 | 0.7281 | 0.7281 | 0.7281 | | 0.5467 | 3.0 | 5436 | 0.6657 | 0.7309 | 0.7309 | 0.7176 | 0.7295 | 0.7309 | 0.7309 | | 0.5179 | 4.0 | 7248 | 0.6654 | 0.7346 | 0.7346 | 0.7240 | 0.7345 | 0.7346 | 0.7346 | | 0.4787 | 5.0 | 9060 | 0.6757 | 0.7350 | 0.7350 | 0.7241 | 0.7347 | 0.7350 | 0.7350 | ### Validation evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | RoBERTa-RILE | 0.74 | 0.72 | 0.73 | ### Test evaluation | Model | Micro F1-Score | Macro F1-Score | Weighted F1-Score | |----------------|----------------|----------------|-------------------| | RoBERTa-RILE | 0.69 | 0.67 | 0.69 | ### Evaluation per category | Label | Validation F1-Score | Test F1-Score | |-----------------------------|---------------------|---------------| | neutral | 0.77 | 0.74 | | left | 0.73 | 0.65 | | right | 0.67 | 0.62 | ### Evaluation based on saliency theory Saliency theory is a theory to analyse politial text data. In sum, parties tend to write about policies in which they think that they are seen as competent. Voters tend to assign advantages in policy competence in line to the assumed ideology of parties. Therefore you can analyze the share of policies parties tend to write about in their manifestos to analyze the party ideology. The Manifesto Project presented for such an analysis the rile-index. For a quick overview, check [this](https://manifesto-project.wzb.eu/down/tutorials/main-dataset.html#measuring-parties-left-right-positions). In the following plot, the predicted and original rile-indices are shown per manifesto in the test dataset. Overall the pearson correlation between the predicted and original rile-indices is 0.95. As alternative, you can use [ManiBERT](https://huggingface.co/niksmer/ManiBERT). ![image](english_robertarile_manifesto.png) ### Framework versions - Transformers 4.16.2 - Pytorch 1.9.0+cu102 - Datasets 1.8.0 - Tokenizers 0.10.3
timoneda/XLM-R-Racismo
638056652a249fe3e4898ab28766816dfc8f2acf
2021-08-11T18:55:38.000Z
[ "pytorch", "xlm-roberta", "text-classification", "transformers" ]
text-classification
false
timoneda
null
timoneda/XLM-R-Racismo
2
null
transformers
25,037
Akash7897/bert-base-cased-wikitext2
47d3ea9e1e3e4f114478e7d96adef191b6edf8ef
2022-03-01T10:29:35.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
Akash7897
null
Akash7897/bert-base-cased-wikitext2
2
null
transformers
25,038
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: bert-base-cased-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-cased-wikitext2 This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 6.8544 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 7.0915 | 1.0 | 2346 | 7.0517 | | 6.905 | 2.0 | 4692 | 6.8735 | | 6.8565 | 3.0 | 7038 | 6.8924 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
AndyyyCai/bert-base-uncased-finetuned-copa
0bb6de71cc070a8941075d0aee15e6ac07f1a35f
2022-03-01T01:57:06.000Z
[ "pytorch", "bert", "multiple-choice", "transformers" ]
multiple-choice
false
AndyyyCai
null
AndyyyCai/bert-base-uncased-finetuned-copa
2
null
transformers
25,039
Entry not found
Sarahliu186/wav2vec2-base-timit-demo-colab
669be1e890e7fc6c2099fd19d22ce2431439ab69
2022-03-01T04:01:20.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Sarahliu186
null
Sarahliu186/wav2vec2-base-timit-demo-colab
2
null
transformers
25,040
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-base-timit-demo-colab results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-base-timit-demo-colab This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-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: 0.0001 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 1 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
mohamed-illiyas/wav2vec-malayalam-new
f7f371015f15ae427c1306f19e5c75880caecce4
2022-03-01T11:43:40.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
mohamed-illiyas
null
mohamed-illiyas/wav2vec-malayalam-new
2
null
transformers
25,041
Entry not found
eson/dummy-model
2610165ee1b00b61a1f0eae4e7da4c854c20642d
2022-03-01T11:32:22.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
eson
null
eson/dummy-model
2
null
transformers
25,042
Entry not found
firqaaa/medbert-base-indonesian
b82fd8549c96ccc23d4eb2caf276e9e110672e96
2021-07-20T16:33:10.000Z
[ "pytorch", "bert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
firqaaa
null
firqaaa/medbert-base-indonesian
2
null
transformers
25,043
Entry not found
Kevincp560/bart-large-finetuned-pubmed
5969ae4addb05b036c898b8820cda38bb4c686d1
2022-03-01T18:35:04.000Z
[ "pytorch", "tensorboard", "bart", "text2text-generation", "dataset:pub_med_summarization_dataset", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
Kevincp560
null
Kevincp560/bart-large-finetuned-pubmed
2
null
transformers
25,044
--- license: apache-2.0 tags: - generated_from_trainer datasets: - pub_med_summarization_dataset metrics: - rouge model-index: - name: bart-large-finetuned-pubmed results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: pub_med_summarization_dataset type: pub_med_summarization_dataset args: document metrics: - name: Rouge1 type: rouge value: 10.946 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bart-large-finetuned-pubmed This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 1.8135 - Rouge1: 10.946 - Rouge2: 5.0933 - Rougel: 9.5608 - Rougelsum: 10.4259 - Gen Len: 19.0495 ## 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.0861 | 1.0 | 4000 | 1.8909 | 8.7344 | 3.6919 | 7.8804 | 8.3305 | 20.0 | | 1.8996 | 2.0 | 8000 | 1.8261 | 10.2124 | 4.6212 | 8.9842 | 9.7417 | 17.632 | | 1.7459 | 3.0 | 12000 | 1.8160 | 9.4933 | 4.4117 | 8.3977 | 9.0758 | 16.4775 | | 1.6258 | 4.0 | 16000 | 1.8136 | 10.8248 | 5.0335 | 9.4286 | 10.3123 | 18.724 | | 1.5214 | 5.0 | 20000 | 1.8135 | 10.946 | 5.0933 | 9.5608 | 10.4259 | 19.0495 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
spy24/autonlp-US-to-UK-604417040
e3b02f6759a8ab55af7db62a18f55b9212d863be
2022-03-01T13:16:47.000Z
[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-US-to-UK", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
spy24
null
spy24/autonlp-US-to-UK-604417040
2
null
transformers
25,045
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-US-to-UK co2_eq_emissions: 3.3271667948644614 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 604417040 - CO2 Emissions (in grams): 3.3271667948644614 ## Validation Metrics - Loss: 1.919085144996643 - Rouge1: 39.2808 - Rouge2: 4.905 - RougeL: 39.113 - RougeLsum: 39.1463 - Gen Len: 3.4611 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/spy24/autonlp-US-to-UK-604417040 ```
ali2066/twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43
95f09402ec9064ab0cb41667bf17c3af45f04e10
2022-03-01T13:30:07.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43
2
null
transformers
25,046
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: twitter_RoBERTa_token_itr0_0.0001_all_01_03_2022-14_26_43 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_token_itr0_0.0001_all_01_03_2022-14_26_43 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2591 - Precision: 0.4174 - Recall: 0.5678 - F1: 0.4811 - Accuracy: 0.8852 ## 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: 32 - 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 | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4690 | 0.3732 | 0.1830 | 0.2456 | 0.7509 | | No log | 2.0 | 60 | 0.3936 | 0.2067 | 0.3559 | 0.2615 | 0.7851 | | No log | 3.0 | 90 | 0.3019 | 0.3658 | 0.4904 | 0.4190 | 0.8703 | | No log | 4.0 | 120 | 0.2510 | 0.4387 | 0.5137 | 0.4732 | 0.8889 | | No log | 5.0 | 150 | 0.2481 | 0.4196 | 0.5511 | 0.4764 | 0.8881 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20
3e64ede9217f8623ff3d7558b92d0a90f4018914
2022-03-01T13:46:20.000Z
[ "pytorch", "tensorboard", "distilbert", "token-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20
2
null
transformers
25,047
--- license: apache-2.0 tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: twitter_RoBERTa_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20 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_token_itr0_1e-05_webDiscourse_01_03_2022-14_45_20 This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.6113 - Precision: 0.0097 - Recall: 0.0145 - F1: 0.0116 - Accuracy: 0.6780 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 10 | 0.6399 | 0.0 | 0.0 | 0.0 | 0.6603 | | No log | 2.0 | 20 | 0.6192 | 0.0 | 0.0 | 0.0 | 0.6603 | | No log | 3.0 | 30 | 0.6133 | 0.0 | 0.0 | 0.0 | 0.6605 | | No log | 4.0 | 40 | 0.6142 | 0.0 | 0.0 | 0.0 | 0.6617 | | No log | 5.0 | 50 | 0.6129 | 0.0 | 0.0 | 0.0 | 0.6632 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39
5e9e561a5ef511db04014d179c5e81195b4c1761
2022-03-01T14:05:57.000Z
[ "pytorch", "tensorboard", "roberta", "token-classification", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
token-classification
false
ali2066
null
ali2066/twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39
2
null
transformers
25,048
--- tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: twitter_RoBERTa_token_itr0_1e-05_all_01_03_2022-15_02_39 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_token_itr0_1e-05_all_01_03_2022-15_02_39 This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2903 - Precision: 0.2440 - Recall: 0.4465 - F1: 0.3155 - Accuracy: 0.8706 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| | No log | 1.0 | 30 | 0.4378 | 0.0463 | 0.1136 | 0.0658 | 0.7742 | | No log | 2.0 | 60 | 0.3739 | 0.1472 | 0.3756 | 0.2115 | 0.8284 | | No log | 3.0 | 90 | 0.3422 | 0.1865 | 0.4330 | 0.2607 | 0.8374 | | No log | 4.0 | 120 | 0.3286 | 0.2243 | 0.4833 | 0.3064 | 0.8438 | | No log | 5.0 | 150 | 0.3239 | 0.2356 | 0.4809 | 0.3163 | 0.8490 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1+cu113 - Datasets 1.18.0 - Tokenizers 0.10.3
QuickRead/pegasus-reddit-full
95f15c5c541b22daf2af28c029054f0e040621d9
2022-03-03T22:43:54.000Z
[ "pytorch", "pegasus", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
QuickRead
null
QuickRead/pegasus-reddit-full
2
null
transformers
25,049
Entry not found
BigSalmon/InformalToFormalLincoln24
325578834b2b134380fc3d9ff6a1ea6ec127643b
2022-03-02T01:11:55.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/InformalToFormalLincoln24
2
null
transformers
25,050
``` from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln24") model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln24") ``` ``` How To Make Prompt: informal english: i am very ready to do that just that. Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end. Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task. *** informal english: space is huge and needs to be explored. Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless. Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration. *** informal english: corn fields are all across illinois, visible once you leave chicago. Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago. informal english: ``` ``` - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. Text: failing to draw in the masses, the NBA has fallen into disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap solutions could revive the league. the addition of the much-hyped four-point line would surely juice viewership. *** - ``` ``` infill: chrome extensions [MASK] accomplish everyday tasks. Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks. infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices. infill: ``` ``` Essay Intro (California High-Speed Rail): built with an eye on the future, california's high-speed rail service resolves to change the face of travel. Essay Intro (YIMBY's Need To Win): home to the most expensive housing market in the united states, san francisco is the city in which the yimby and anti-yimby hordes wage an eternal battle. Essay Intro ( ``` ``` Search: What is the definition of Checks and Balances? https://en.wikipedia.org/wiki/Checks_and_balances Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate. https://www.harvard.edu/glossary/Checks_and_Balances Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power https://www.law.cornell.edu/library/constitution/Checks_and_Balances Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power. *** Search: What is the definition of Separation of Powers? https://en.wikipedia.org/wiki/Separation_of_powers The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power. https://www.yale.edu/tcf/Separation_of_Powers.html Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined. *** Search: What is the definition of Connection of Powers? https://en.wikipedia.org/wiki/Connection_of_powers Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches. https://simple.wikipedia.org/wiki/Connection_of_powers The term Connection of Powers describes a system of government in which there is overlap between different parts of the government. *** Search: What is the definition of ``` ``` Search: What are phrase synonyms for "second-guess"? https://www.powerthesaurus.org/second-guess/synonyms Shortest to Longest: - feel dubious about - raise an eyebrow at - wrinkle their noses at - cast a jaundiced eye at - teeter on the fence about *** Search: What are phrase synonyms for "mean to newbies"? https://www.powerthesaurus.org/mean_to_newbies/synonyms Shortest to Longest: - readiness to balk at rookies - absence of tolerance for novices - hostile attitude toward newcomers *** Search: What are phrase synonyms for "make use of"? https://www.powerthesaurus.org/make_use_of/synonyms Shortest to Longest: - call upon - glean value from - reap benefits from - derive utility from - seize on the merits of - draw on the strength of - tap into the potential of *** Search: What are phrase synonyms for "hurting itself"? https://www.powerthesaurus.org/hurting_itself/synonyms Shortest to Longest: - erring - slighting itself - forfeiting its integrity - doing itself a disservice - evincing a lack of backbone *** Search: What are phrase synonyms for " ```
Verge/Peterbot
d9857ab4416dd48cffb739a968dd6d9363aaaf17
2022-03-02T04:28:19.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
Verge
null
Verge/Peterbot
2
null
transformers
25,051
--- tags: - conversational --- # Peter from Your Boyfriend Game.
sileod/medqa
1e86578301c4866be815edc33382024b2f12ef75
2022-06-09T08:59:34.000Z
[ "pytorch", "bert", "multiple-choice", "transformers" ]
multiple-choice
false
sileod
null
sileod/medqa
2
null
transformers
25,052
Entry not found
spy24/autonlp-US-to-UK2-606317091
9dad33f25eacd1bc6dbc0cdb3dd7c7278024b49f
2022-03-02T09:03:19.000Z
[ "pytorch", "t5", "text2text-generation", "unk", "dataset:spy24/autonlp-data-US-to-UK2", "transformers", "autonlp", "co2_eq_emissions", "autotrain_compatible" ]
text2text-generation
false
spy24
null
spy24/autonlp-US-to-UK2-606317091
2
1
transformers
25,053
--- tags: autonlp language: unk widget: - text: "I love AutoNLP 🤗" datasets: - spy24/autonlp-data-US-to-UK2 co2_eq_emissions: 1.1913570653422176 --- # Model Trained Using AutoNLP - Problem type: Summarization - Model ID: 606317091 - CO2 Emissions (in grams): 1.1913570653422176 ## Validation Metrics - Loss: 1.9264822006225586 - Rouge1: 44.2035 - Rouge2: 6.134 - RougeL: 43.9114 - RougeLsum: 44.0231 - Gen Len: 3.6134 ## Usage You can use cURL to access this model: ``` $ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/spy24/autonlp-US-to-UK2-606317091 ```
facebook/maskformer-swin-base-coco
231e5833faa7ac890148d6b53ec6c8e3db8fd50d
2022-04-04T16:02:06.000Z
[ "pytorch", "maskformer", "dataset:coco", "arxiv:2107.06278", "transformers", "vision", "image-segmentatiom", "license:apache-2.0" ]
null
false
facebook
null
facebook/maskformer-swin-base-coco
2
null
transformers
25,054
--- license: apache-2.0 tags: - vision - image-segmentatiom datasets: - coco --- # Mask Mask model trained on coco. It was introduced in the paper [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) and first released in [this repository](https://github.com/facebookresearch/MaskFormer/blob/da3e60d85fdeedcb31476b5edd7d328826ce56cc/mask_former/modeling/criterion.py#L169). Disclaimer: The team releasing Mask did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description MaskFormer addresses semantic segmentation with a mask classification paradigm instead. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/maskformer_architecture.png) ## Intended uses & limitations You can use the raw model for image classification. See the [model hub](https://huggingface.co/models?search=maskformer) to look for fine-tuned versions on a task that interests you. ### How to use Here is how to use this model: ```python >>> from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation >>> from PIL import Image >>> import requests >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> feature_extractor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-base-ade") >>> inputs = feature_extractor(images=image, return_tensors="pt") >>> model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-base-ade") >>> outputs = model(**inputs) >>> # model predicts class_queries_logits of shape `(batch_size, num_queries)` >>> # and masks_queries_logits of shape `(batch_size, num_queries, height, width)` >>> class_queries_logits = outputs.class_queries_logits >>> masks_queries_logits = outputs.masks_queries_logits >>> # you can pass them to feature_extractor for postprocessing >>> output = feature_extractor.post_process_segmentation(outputs) >>> output = feature_extractor.post_process_semantic_segmentation(outputs) >>> output = feature_extractor.post_process_panoptic_segmentation(outputs) ``` For more code examples, we refer to the [documentation](https://huggingface.co/docs/transformers/master/en/model_doc/maskformer).
jcai1/ss_mrpc
f8df6cd2216489d69902452e7788b63339d72d2f
2022-03-02T14:32:31.000Z
[ "pytorch", "tensorboard", "bert", "text-classification", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
text-classification
false
jcai1
null
jcai1/ss_mrpc
2
null
transformers
25,055
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: ss_mrpc 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. --> # ss_mrpc This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5960 - Accuracy: 0.8799 - F1: 0.9148 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 459 | 0.3655 | 0.8578 | 0.8990 | | 0.524 | 2.0 | 918 | 0.6061 | 0.8260 | 0.8823 | | 0.2971 | 3.0 | 1377 | 0.5960 | 0.8799 | 0.9148 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
Kuray107/wsj0-full-supervised
9422f3af882708b1c0403f90e89848ef7139d16c
2022-03-03T11:16:35.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Kuray107
null
Kuray107/wsj0-full-supervised
2
null
transformers
25,056
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wsj0-full-supervised 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. --> # wsj0-full-supervised This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0623 - Wer: 0.0343 ## 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: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.517 | 0.86 | 500 | 2.9475 | 1.0 | | 2.2387 | 1.72 | 1000 | 0.4004 | 0.3498 | | 0.3081 | 2.57 | 1500 | 0.1362 | 0.1159 | | 0.1744 | 3.43 | 2000 | 0.1125 | 0.0929 | | 0.1285 | 4.29 | 2500 | 0.0894 | 0.0727 | | 0.1015 | 5.15 | 3000 | 0.0852 | 0.0642 | | 0.0811 | 6.0 | 3500 | 0.0789 | 0.0614 | | 0.0748 | 6.86 | 4000 | 0.0746 | 0.0529 | | 0.0639 | 7.72 | 4500 | 0.0714 | 0.0481 | | 0.0606 | 8.58 | 5000 | 0.0698 | 0.0489 | | 0.0525 | 9.43 | 5500 | 0.0747 | 0.0464 | | 0.0489 | 10.29 | 6000 | 0.0594 | 0.0396 | | 0.0419 | 11.15 | 6500 | 0.0600 | 0.0359 | | 0.0414 | 12.01 | 7000 | 0.0612 | 0.0412 | | 0.0383 | 12.86 | 7500 | 0.0676 | 0.0392 | | 0.0352 | 13.72 | 8000 | 0.0626 | 0.0388 | | 0.034 | 14.58 | 8500 | 0.0699 | 0.0372 | | 0.0309 | 15.44 | 9000 | 0.0807 | 0.0420 | | 0.0295 | 16.3 | 9500 | 0.0796 | 0.0396 | | 0.0273 | 17.15 | 10000 | 0.0716 | 0.0376 | | 0.0271 | 18.01 | 10500 | 0.0657 | 0.0384 | | 0.0251 | 18.87 | 11000 | 0.0585 | 0.0351 | | 0.024 | 19.73 | 11500 | 0.0557 | 0.0347 | | 0.0252 | 20.58 | 12000 | 0.0609 | 0.0327 | | 0.0231 | 21.44 | 12500 | 0.0720 | 0.0368 | | 0.0202 | 22.3 | 13000 | 0.0625 | 0.0343 | | 0.0195 | 23.16 | 13500 | 0.0635 | 0.0372 | | 0.0201 | 24.01 | 14000 | 0.0582 | 0.0335 | | 0.0183 | 24.87 | 14500 | 0.0562 | 0.0343 | | 0.0183 | 25.73 | 15000 | 0.0629 | 0.0335 | | 0.0175 | 26.59 | 15500 | 0.0593 | 0.0323 | | 0.017 | 27.44 | 16000 | 0.0631 | 0.0339 | | 0.0162 | 28.3 | 16500 | 0.0597 | 0.0335 | | 0.0169 | 29.16 | 17000 | 0.0623 | 0.0343 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
repro-rights-amicus-briefs/bert-base-uncased-finetuned-RRamicus
b561fd7877927a81393f7509ee0f54df398794a1
2022-01-10T21:19:34.000Z
[ "pytorch", "tensorboard", "bert", "fill-mask", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
repro-rights-amicus-briefs
null
repro-rights-amicus-briefs/bert-base-uncased-finetuned-RRamicus
2
null
transformers
25,057
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: reprorights-amicus-bert 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. --> # reprorights-amicus-bert This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 1.5428 ## 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: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 1.7763 | 1.0 | 1479 | 1.6789 | | 1.76 | 2.0 | 2958 | 1.6199 | | 1.6881 | 3.0 | 4437 | 1.5683 | | 1.6424 | 4.0 | 5916 | 1.5432 | | 1.6131 | 5.0 | 7395 | 1.5269 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.17.0 - Tokenizers 0.10.3
shahp7575/electricidad-base-muchocine-finetuned
9996b2d766bb44b4fb3b69a30a78bb37a61f5ae7
2022-03-03T05:20:16.000Z
[ "pytorch", "tensorboard", "electra", "text-classification", "es", "dataset:muchocine", "transformers", "spanish", "sentiment" ]
text-classification
false
shahp7575
null
shahp7575/electricidad-base-muchocine-finetuned
2
null
transformers
25,058
--- language: - es tags: - spanish - sentiment datasets: - muchocine widget: - "Increíble pelicula. ¡Altamente recomendado!" - "Extremadamente malo. Baja calidad" --- <!-- 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. --> # electricidad-base-muchocine-finetuned This model fine-tunes [mrm8488/electricidad-base-discriminator](https://huggingface.co/mrm8488/electricidad-base-discriminator) on [muchocine](https://huggingface.co/datasets/muchocine) dataset for sentiment classification to predict *star_rating*. ### How to use The model can be used directly with the HuggingFace `pipeline`. ```python from transformers import AutoTokenizer, AutoModelWithLMHead tokenizer = AutoTokenizer.from_pretrained("shahp7575/gpt2-horoscopes") model = AutoModelWithLMHead.from_pretrained("shahp7575/gpt2-horoscopes") ``` ### Examples ```python from transformers import pipeline clf = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) clf('Esta película es una joya. Todo fue perfecto: historia, casting, dirección. Me encantó el clímax.') >>> [{'label': '5', 'score': 0.9658033847808838}] clf("La historia y el casting fueron geniales.") >>> [{'label': '4', 'score': 0.6666394472122192}] clf("Me gustó pero podría ser mejor.") >>> [{'label': '3', 'score': 0.7013391852378845}] clf("dinero tirado en esta pelicula") >>> [{'label': '2', 'score': 0.7564149498939514}] clf("esta película es una película absolutamente repugnante. odio todo al respecto. gastó tanto dinero.") >>> [{'label': '1', 'score': 0.3040296733379364}] ```
cammy/bart-large-cnn-finetuned-new-100-pad-early
c025145e0545b8c5b570aaed87b83a1910c3ca5b
2022-03-03T10:23:34.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/bart-large-cnn-finetuned-new-100-pad-early
2
null
transformers
25,059
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-finetuned-new-100-pad-early 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. --> # bart-large-cnn-finetuned-new-100-pad-early This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.9543 - Rouge1: 21.8858 - Rouge2: 8.1444 - Rougel: 16.5751 - Rougelsum: 19.163 - Gen Len: 66.8 ## 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: 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: 3 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | No log | 1.0 | 100 | 0.8692 | 20.2714 | 6.206 | 16.3362 | 18.7117 | 66.4 | | No log | 2.0 | 200 | 0.9543 | 21.8858 | 8.1444 | 16.5751 | 19.163 | 66.8 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
RobW/longformer-base-4096-finetuned-chunk-1
0d76916d0d8ffd58ba0bc08d992bd6dcd94c574e
2022-03-03T15:00:58.000Z
[ "pytorch", "tensorboard", "longformer", "token-classification", "transformers", "autotrain_compatible" ]
token-classification
false
RobW
null
RobW/longformer-base-4096-finetuned-chunk-1
2
null
transformers
25,060
Entry not found
Kuray107/wsj0-5percent-supervised
dbd6938ced244faeaacd50a410309145b6615998
2022-03-04T20:16:51.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
Kuray107
null
Kuray107/wsj0-5percent-supervised
2
null
transformers
25,061
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wsj0-5percent-supervised 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. --> # wsj0-5percent-supervised This model is a fine-tuned version of [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.3883 - Wer: 0.1555 ## 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: 12 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 300 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:------:| | 6.0248 | 16.67 | 500 | 2.9406 | 1.0 | | 2.0466 | 33.33 | 1000 | 0.3935 | 0.3300 | | 0.1486 | 50.0 | 1500 | 0.3091 | 0.1931 | | 0.052 | 66.67 | 2000 | 0.3562 | 0.2052 | | 0.0309 | 83.33 | 2500 | 0.3252 | 0.1773 | | 0.0228 | 100.0 | 3000 | 0.3360 | 0.1652 | | 0.0177 | 116.67 | 3500 | 0.3423 | 0.1603 | | 0.0142 | 133.33 | 4000 | 0.3416 | 0.1611 | | 0.0119 | 150.0 | 4500 | 0.3663 | 0.1583 | | 0.0094 | 166.67 | 5000 | 0.3617 | 0.1567 | | 0.0093 | 183.33 | 5500 | 0.3738 | 0.1668 | | 0.0079 | 200.0 | 6000 | 0.3881 | 0.1652 | | 0.0065 | 216.67 | 6500 | 0.3752 | 0.1611 | | 0.0056 | 233.33 | 7000 | 0.3798 | 0.1603 | | 0.0057 | 250.0 | 7500 | 0.3944 | 0.1624 | | 0.0047 | 266.67 | 8000 | 0.4038 | 0.1583 | | 0.0041 | 283.33 | 8500 | 0.3928 | 0.1547 | | 0.0036 | 300.0 | 9000 | 0.3883 | 0.1555 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
T-202/dummy-model
c08f687ba81e93970c97e75dc5f3a994ee127c03
2022-03-03T16:21:20.000Z
[ "pytorch", "camembert", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
T-202
null
T-202/dummy-model
2
null
transformers
25,062
Entry not found
Kevincp560/t5-small-finetuned-pubmed
5e057adad16a7437e8f1b68fce77b6df2f485171
2022-03-03T17:22:09.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:pub_med_summarization_dataset", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
Kevincp560
null
Kevincp560/t5-small-finetuned-pubmed
2
null
transformers
25,063
--- license: apache-2.0 tags: - generated_from_trainer datasets: - pub_med_summarization_dataset metrics: - rouge model-index: - name: t5-small-finetuned-pubmed results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: pub_med_summarization_dataset type: pub_med_summarization_dataset args: document metrics: - name: Rouge1 type: rouge value: 8.8295 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-pubmed This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.2635 - Rouge1: 8.8295 - Rouge2: 3.2594 - Rougel: 7.9975 - Rougelsum: 8.4483 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:| | 2.5892 | 1.0 | 4000 | 2.3616 | 10.1169 | 3.9666 | 8.8854 | 9.5836 | 19.0 | | 2.559 | 2.0 | 8000 | 2.3045 | 9.4321 | 3.5398 | 8.424 | 8.984 | 19.0 | | 2.5029 | 3.0 | 12000 | 2.2820 | 9.1658 | 3.3686 | 8.2222 | 8.7311 | 19.0 | | 2.4673 | 4.0 | 16000 | 2.2692 | 8.8973 | 3.2617 | 8.0395 | 8.5046 | 19.0 | | 2.4331 | 5.0 | 20000 | 2.2635 | 8.8295 | 3.2594 | 7.9975 | 8.4483 | 19.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
Kevincp560/wikihow-t5-small-finetuned-pubmed
abecc8017bc5d8964a4cb91ef5bdf4ead76fec67
2022-03-03T20:22:04.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "dataset:pub_med_summarization_dataset", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
Kevincp560
null
Kevincp560/wikihow-t5-small-finetuned-pubmed
2
null
transformers
25,064
--- tags: - generated_from_trainer datasets: - pub_med_summarization_dataset metrics: - rouge model-index: - name: wikihow-t5-small-finetuned-pubmed results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: pub_med_summarization_dataset type: pub_med_summarization_dataset args: document metrics: - name: Rouge1 type: rouge value: 8.9619 --- <!-- 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. --> # wikihow-t5-small-finetuned-pubmed This model is a fine-tuned version of [deep-learning-analytics/wikihow-t5-small](https://huggingface.co/deep-learning-analytics/wikihow-t5-small) on the pub_med_summarization_dataset dataset. It achieves the following results on the evaluation set: - Loss: 2.2702 - Rouge1: 8.9619 - Rouge2: 3.2719 - Rougel: 8.1558 - Rougelsum: 8.5714 - Gen Len: 19.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 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: 5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 2.5984 | 1.0 | 4000 | 2.3696 | 10.237 | 3.8609 | 8.9776 | 9.677 | 19.0 | | 2.5677 | 2.0 | 8000 | 2.3132 | 9.302 | 3.4499 | 8.3816 | 8.8831 | 19.0 | | 2.5038 | 3.0 | 12000 | 2.2884 | 9.0578 | 3.3103 | 8.23 | 8.6723 | 19.0 | | 2.4762 | 4.0 | 16000 | 2.2758 | 9.0001 | 3.2882 | 8.1845 | 8.6084 | 19.0 | | 2.4393 | 5.0 | 20000 | 2.2702 | 8.9619 | 3.2719 | 8.1558 | 8.5714 | 19.0 | ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
eson/kplug-sum
3465fa4abce77aa7eef8cae767e6b53b2ba1eed4
2022-03-04T03:17:17.000Z
[ "pytorch", "bart", "feature-extraction", "transformers" ]
feature-extraction
false
eson
null
eson/kplug-sum
2
null
transformers
25,065
Entry not found
zenham/khemx
3f4b7addd449766b345c386c7a680db0ad3737f1
2022-03-05T06:45:35.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
zenham
null
zenham/khemx
2
null
transformers
25,066
--- tags: - conversational --- #khemx DialoGPT Model
mmaguero/gn-bert-large-cased
376900a847948fad823618ec54f5b23a039daba7
2022-03-06T08:10:46.000Z
[ "pytorch", "bert", "fill-mask", "gn", "dataset:wikipedia", "dataset:wiktionary", "transformers", "license:mit", "autotrain_compatible" ]
fill-mask
false
mmaguero
null
mmaguero/gn-bert-large-cased
2
null
transformers
25,067
--- language: gn license: mit datasets: - wikipedia - wiktionary widget: - text: "Paraguay ha'e peteĩ táva oĩva [MASK] retãme " --- # BERT-i-large-cased (gnBERT-large-cased) A pre-trained BERT model for **Guarani** (24 layers, cased). Trained on Wikipedia + Wiktionary (~800K tokens).
nielsr/dpt-large-redesign
42b643d94ca72628c2e944f69b4f2648e9fbd85d
2022-03-04T17:54:17.000Z
[ "pytorch", "dpt", "transformers" ]
null
false
nielsr
null
nielsr/dpt-large-redesign
2
null
transformers
25,068
Entry not found
Ebtihal/AraBertMo_base_V10
c127221347d465d427b71fd4db3e2d753cb201f3
2022-03-15T19:10:54.000Z
[ "pytorch", "bert", "fill-mask", "ar", "dataset:OSCAR", "transformers", "Fill-Mask", "autotrain_compatible" ]
fill-mask
false
Ebtihal
null
Ebtihal/AraBertMo_base_V10
2
null
transformers
25,069
Arabic Model AraBertMo_base_V10 --- language: ar tags: Fill-Mask datasets: OSCAR widget: - text: " السلام عليكم ورحمة[MASK] وبركاتة" - text: " اهلا وسهلا بكم في [MASK] من سيربح المليون" - text: " مرحبا بك عزيزي الزائر [MASK] موقعنا " --- # Arabic BERT Model **AraBERTMo** is an Arabic pre-trained language model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERTMo_base uses the same BERT-Base config. AraBERTMo_base now comes in 10 new variants All models are available on the `HuggingFace` model page under the [Ebtihal](https://huggingface.co/Ebtihal/) name. Checkpoints are available in PyTorch formats. ## Pretraining Corpus `AraBertMo_base_V10' model was pre-trained on ~3 million words: - [OSCAR](https://traces1.inria.fr/oscar/) - Arabic version "unshuffled_deduplicated_ar". ## Training results this model achieves the following results: | Task | Num examples | Num Epochs | Batch Size | steps | Wall time | training loss| |:----:|:----:|:----:|:----:|:-----:|:----:|:-----:| | Fill-Mask| 30024| 10 | 64 | 4700 | 9h 13m 43s | 7.2395 | ## Load Pretrained Model You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this: ```python from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ebtihal/AraBertMo_base_V10") model = AutoModelForMaskedLM.from_pretrained("Ebtihal/AraBertMo_base_V10") ``` ## This model was built for master's degree research in an organization: - [University of kufa](https://uokufa.edu.iq/). - [Faculty of Computer Science and Mathematics](https://mathcomp.uokufa.edu.iq/). - **Department of Computer Science**
azaninello/gpt2-finetuned-shrooms
12224bf655af8cc072dafdcae36dd2e697e0783e
2022-03-06T13:16:58.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
azaninello
null
azaninello/gpt2-finetuned-shrooms
2
null
transformers
25,070
Entry not found
tmills/cnlpt-negation-roberta-sharpseed
3ed14dd5f69ac3c3ecf9ac86111448063186996c
2022-03-04T21:16:45.000Z
[ "pytorch", "cnlpt", "transformers" ]
null
false
tmills
null
tmills/cnlpt-negation-roberta-sharpseed
2
null
transformers
25,071
Entry not found
akadriu/wav2vec2-large-xlsr-53-Total2e-4_3
cf087cf53636d01170dbaa7e0a14deabf4dc8724
2022-03-14T11:05:10.000Z
[ "pytorch", "tensorboard", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
automatic-speech-recognition
false
akadriu
null
akadriu/wav2vec2-large-xlsr-53-Total2e-4_3
2
null
transformers
25,072
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: wav2vec2-large-xlsr-53-Total2e-4_3 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # wav2vec2-large-xlsr-53-Total2e-4_3 This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2893 - Wer: 0.1863 ## 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: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 5.16 | 0.1 | 200 | 2.9123 | 0.9707 | | 2.4599 | 0.2 | 400 | 0.8145 | 0.6906 | | 1.0523 | 0.3 | 600 | 0.5247 | 0.4823 | | 0.8965 | 0.4 | 800 | 0.4391 | 0.4416 | | 0.7994 | 0.5 | 1000 | 0.3889 | 0.3773 | | 0.7491 | 0.6 | 1200 | 0.3604 | 0.3305 | | 0.7425 | 0.7 | 1400 | 0.3543 | 0.3277 | | 0.7253 | 0.8 | 1600 | 0.3397 | 0.3143 | | 0.7221 | 0.9 | 1800 | 0.3341 | 0.2979 | | 0.6853 | 1.0 | 2000 | 0.3244 | 0.2906 | | 0.6107 | 1.1 | 2200 | 0.3127 | 0.2771 | | 0.6233 | 1.2 | 2400 | 0.3116 | 0.2721 | | 0.6214 | 1.3 | 2600 | 0.3256 | 0.2671 | | 0.6511 | 1.4 | 2800 | 0.3019 | 0.2570 | | 0.6491 | 1.5 | 3000 | 0.2961 | 0.2576 | | 0.6411 | 1.6 | 3200 | 0.2963 | 0.2535 | | 0.5963 | 1.7 | 3400 | 0.2939 | 0.2526 | | 0.6146 | 1.8 | 3600 | 0.2908 | 0.2490 | | 0.6291 | 1.9 | 3800 | 0.2851 | 0.2448 | | 0.6154 | 2.0 | 4000 | 0.2861 | 0.2424 | | 0.5652 | 2.1 | 4200 | 0.2852 | 0.2411 | | 0.5648 | 2.2 | 4400 | 0.2856 | 0.2350 | | 0.5365 | 2.3 | 4600 | 0.2802 | 0.2395 | | 0.5855 | 2.4 | 4800 | 0.2883 | 0.2374 | | 0.5978 | 2.5 | 5000 | 0.2855 | 0.2364 | | 0.5863 | 2.6 | 5200 | 0.2736 | 0.2277 | | 0.5569 | 2.7 | 5400 | 0.2746 | 0.2293 | | 0.5628 | 2.8 | 5600 | 0.2719 | 0.2249 | | 0.5655 | 2.9 | 5800 | 0.2653 | 0.2224 | | 0.5578 | 3.0 | 6000 | 0.2685 | 0.2243 | | 0.5303 | 3.1 | 6200 | 0.2696 | 0.2204 | | 0.5316 | 3.2 | 6400 | 0.2733 | 0.2247 | | 0.5476 | 3.3 | 6600 | 0.2716 | 0.2203 | | 0.5326 | 3.4 | 6800 | 0.2697 | 0.2209 | | 0.5375 | 3.5 | 7000 | 0.2701 | 0.2197 | | 0.5364 | 3.6 | 7200 | 0.2655 | 0.2165 | | 0.503 | 3.7 | 7400 | 0.2650 | 0.2125 | | 0.5284 | 3.8 | 7600 | 0.2672 | 0.2162 | | 0.5251 | 3.9 | 7800 | 0.2669 | 0.2172 | | 0.5299 | 4.0 | 8000 | 0.2632 | 0.2081 | | 0.4904 | 4.1 | 8200 | 0.2674 | 0.2099 | | 0.496 | 4.2 | 8400 | 0.2700 | 0.2143 | | 0.5067 | 4.3 | 8600 | 0.2648 | 0.2090 | | 0.506 | 4.4 | 8800 | 0.2595 | 0.2069 | | 0.4795 | 4.5 | 9000 | 0.2653 | 0.2072 | | 0.5149 | 4.6 | 9200 | 0.2618 | 0.2073 | | 0.4786 | 4.7 | 9400 | 0.2632 | 0.2058 | | 0.5056 | 4.8 | 9600 | 0.2674 | 0.2123 | | 0.5059 | 4.9 | 9800 | 0.2642 | 0.2115 | | 0.5119 | 5.0 | 10000 | 0.2672 | 0.2089 | | 0.4619 | 5.1 | 10200 | 0.2658 | 0.2062 | | 0.4647 | 5.2 | 10400 | 0.2664 | 0.2025 | | 0.4707 | 5.3 | 10600 | 0.2656 | 0.2084 | | 0.486 | 5.4 | 10800 | 0.2728 | 0.2029 | | 0.4785 | 5.5 | 11000 | 0.2653 | 0.2004 | | 0.4895 | 5.6 | 11200 | 0.2835 | 0.2119 | | 0.4519 | 5.7 | 11400 | 0.2715 | 0.2061 | | 0.484 | 5.8 | 11600 | 0.2663 | 0.2071 | | 0.4734 | 5.9 | 11800 | 0.2615 | 0.2023 | | 0.4563 | 6.0 | 12000 | 0.2604 | 0.1997 | | 0.4193 | 6.1 | 12200 | 0.2708 | 0.2015 | | 0.4516 | 6.2 | 12400 | 0.2724 | 0.2018 | | 0.4609 | 6.3 | 12600 | 0.2745 | 0.2004 | | 0.43 | 6.4 | 12800 | 0.2716 | 0.1979 | | 0.4424 | 6.5 | 13000 | 0.2674 | 0.1963 | | 0.4589 | 6.6 | 13200 | 0.2622 | 0.1977 | | 0.4458 | 6.7 | 13400 | 0.2668 | 0.1994 | | 0.4233 | 6.8 | 13600 | 0.2739 | 0.1978 | | 0.4557 | 6.9 | 13800 | 0.2692 | 0.1972 | | 0.4472 | 7.0 | 14000 | 0.2686 | 0.1942 | | 0.4193 | 7.1 | 14200 | 0.2843 | 0.1959 | | 0.4033 | 7.2 | 14400 | 0.2767 | 0.1945 | | 0.4266 | 7.3 | 14600 | 0.2808 | 0.1931 | | 0.419 | 7.4 | 14800 | 0.2801 | 0.1945 | | 0.4352 | 7.5 | 15000 | 0.2764 | 0.1934 | | 0.4248 | 7.6 | 15200 | 0.2818 | 0.1938 | | 0.4001 | 7.7 | 15400 | 0.2754 | 0.1931 | | 0.415 | 7.8 | 15600 | 0.2799 | 0.1916 | | 0.4056 | 7.9 | 15800 | 0.2746 | 0.1916 | | 0.419 | 8.0 | 16000 | 0.2789 | 0.1909 | | 0.3974 | 8.1 | 16200 | 0.2913 | 0.1897 | | 0.3999 | 8.2 | 16400 | 0.2894 | 0.1899 | | 0.4179 | 8.3 | 16600 | 0.2819 | 0.1918 | | 0.4081 | 8.4 | 16800 | 0.2868 | 0.1910 | | 0.3963 | 8.5 | 17000 | 0.2835 | 0.1889 | | 0.3748 | 8.6 | 17200 | 0.2841 | 0.1903 | | 0.375 | 8.7 | 17400 | 0.2820 | 0.1874 | | 0.3857 | 8.8 | 17600 | 0.2865 | 0.1872 | | 0.3901 | 8.9 | 17800 | 0.2824 | 0.1882 | | 0.4067 | 9.0 | 18000 | 0.2838 | 0.1887 | | 0.3711 | 9.1 | 18200 | 0.2892 | 0.1897 | | 0.3661 | 9.2 | 18400 | 0.2889 | 0.1883 | | 0.3796 | 9.3 | 18600 | 0.2876 | 0.1886 | | 0.3932 | 9.4 | 18800 | 0.2948 | 0.1877 | | 0.3894 | 9.5 | 19000 | 0.2896 | 0.1884 | | 0.3643 | 9.6 | 19200 | 0.2897 | 0.1868 | | 0.384 | 9.7 | 19400 | 0.2887 | 0.1867 | | 0.3951 | 9.8 | 19600 | 0.2905 | 0.1862 | | 0.3595 | 9.9 | 19800 | 0.2893 | 0.1866 | | 0.3758 | 10.0 | 20000 | 0.2893 | 0.1863 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.10.3
peterhsu/test-bert-finetuned-en-zh_TW-accelerate
62b586a834fa85454f692183120b1c65008e8e6e
2022-03-10T09:44:01.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
peterhsu
null
peterhsu/test-bert-finetuned-en-zh_TW-accelerate
2
null
transformers
25,073
Entry not found
infinitylyj/DialogGPT-small-general
a3a568ce5876014650d8650e62faceae138d52b1
2022-03-05T10:29:06.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "conversational" ]
conversational
false
infinitylyj
null
infinitylyj/DialogGPT-small-general
2
null
transformers
25,074
--- tags: - conversational --- # General DialogGPT Model
mp6kv/main_intent_test
1da99009812f22f0749779880a24e6b039fa3a02
2022-03-05T19:18:02.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers", "generated_from_trainer", "license:mit", "model-index" ]
text-classification
false
mp6kv
null
mp6kv/main_intent_test
2
null
transformers
25,075
--- license: mit tags: - generated_from_trainer model-index: - name: main_intent_test 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. --> # main_intent_test This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset. ## Model description Custom data generated labeling text according to these five categories. Five categories represent the five essential intents of a user for the ACTS scenario. - Connect : Greetings and introduction with the student - Pump : Asking the student for information - Inform : Providing information to the student - Feedback : Praising the student (positive feedback) or informing the student they are not on the right path (negative feedback) - None : Not related to scenario Takes a user input of string text and classifies it according to one of five categories. ## Intended uses & limitations from transformers import pipeline classifier = pipeline("text-classification",model="mp6kv/main_intent_test") output = classifier("great job, you're getting it!") score = output[0]['score'] label = output[0]['label'] ## 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 ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 1.18.3 - Tokenizers 0.11.6
BigSalmon/Points3
8f25b6907c115d9615ab2539d9a74c69edbb7a0c
2022-03-05T22:03:31.000Z
[ "pytorch", "gpt2", "text-generation", "transformers" ]
text-generation
false
BigSalmon
null
BigSalmon/Points3
2
null
transformers
25,076
Example Prompt: ``` ### - declining viewership facing the nba. - does not have to be this way. - in fact, many solutions exist. - the four point line would surely draw in eyes. text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership. ### - ```
ahazeemi/hindiasr
783d4cfdcf42a1144ab80c166207050027a7929b
2022-03-06T13:02:49.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers" ]
automatic-speech-recognition
false
ahazeemi
null
ahazeemi/hindiasr
2
null
transformers
25,077
Entry not found
adalbertojunior/test-128-uncased-3
3c42ef0ae40c3733febbe66637dc54336e7df896
2022-03-06T13:43:52.000Z
[ "pytorch", "jax", "roberta", "fill-mask", "transformers", "autotrain_compatible" ]
fill-mask
false
adalbertojunior
null
adalbertojunior/test-128-uncased-3
2
null
transformers
25,078
Entry not found
princeton-nlp/datamux-ner-10
66695a821e1ab8ed0ba53337a4534737147e9f23
2022-03-06T17:10:41.000Z
[ "pytorch", "roberta", "transformers" ]
null
false
princeton-nlp
null
princeton-nlp/datamux-ner-10
2
null
transformers
25,079
Entry not found
MrAnderson/bert-base-512-full-trivia
79b8ccf479aecb55f2b216893b0f5d45ab345f44
2022-03-07T14:20:45.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
MrAnderson
null
MrAnderson/bert-base-512-full-trivia
2
null
transformers
25,080
Entry not found
clu-ling/roberta-finetuned-stsbenchmark
bc9f394dd3541168ef4bf9d8864aff96c3944f77
2022-03-06T21:32:04.000Z
[ "pytorch", "roberta", "feature-extraction", "sentence-transformers", "sentence-similarity", "transformers" ]
sentence-similarity
false
clu-ling
null
clu-ling/roberta-finetuned-stsbenchmark
2
0
sentence-transformers
25,081
--- 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 1024 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, util query = "What is the large instrument the man is playing?" docs = ["A man is playing a large flute.", "A man is playing a flute."] #Load the model model = SentenceTransformer('clu-ling/roberta-finetuned-stsbenchmark') #Encode query and documents query_emb = model.encode(query) doc_emb = model.encode(docs) #Compute dot score between query and all document embeddings scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist() #Combine docs & scores doc_score_pairs = list(zip(docs, scores)) #Sort by decreasing score doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True) #Output passages & scores for doc, score in doc_score_pairs: print(score, doc) ``` ## 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 125 with parameters: ``` {'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'} ``` **Loss**: `sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss` Parameters of the fit()-Method: ``` { "epochs": 1, "evaluation_steps": 0, "evaluator": "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": 100, "weight_decay": 0.01 } ``` ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 128, 'do_lower_case': True}) with Transformer model: RobertaModel (1): Pooling({'word_embedding_dimension': 1024, '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 -->
AdarshRavis/BabishBot
9dcb53aba39168947f61e166f92388a4eef92134
2022-03-12T06:22:16.000Z
[ "pytorch", "gpt2", "text-generation", "transformers", "license:mit" ]
text-generation
false
AdarshRavis
null
AdarshRavis/BabishBot
2
null
transformers
25,082
--- license: mit --- This is a text generation algorithm that is fine-tuned on subtitles from Binging with Babish (https://www.youtube.com/c/bingingwithbabish) Just type in your starting sentence, click "compute" and see what the model has to say! The first time you run the model, it may take a minute to load (after that it takes ~6 seconds to run) This is created with the help of aitextgen (https://github.com/minimaxir/aitextgen), using a pertained 124M gpt-2 model Disclaimer: The use of this model is for parody only, and is not affiliated with Binging with Babish or the Babish Culinary Universe.
Ensheng/Code-Roberta-MLM
5b4bf709a1578448c98dee1f32feb2576f394edd
2022-03-07T05:21:22.000Z
[ "pytorch" ]
null
false
Ensheng
null
Ensheng/Code-Roberta-MLM
2
1
null
25,083
Entry not found
Kuray107/librispeech-semi-supervised-without-LM
a9dacdf8229a6899fa7b7005d2d0747ee18b5a2b
2022-03-07T17:14:04.000Z
[ "pytorch", "wav2vec2", "automatic-speech-recognition", "transformers", "generated_from_trainer", "model-index" ]
automatic-speech-recognition
false
Kuray107
null
Kuray107/librispeech-semi-supervised-without-LM
2
null
transformers
25,084
--- tags: - generated_from_trainer model-index: - name: librispeech-semi-supervised-without-LM 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. --> # librispeech-semi-supervised-without-LM This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1837 - Wer: 0.0580 ## 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: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 15 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 0.0565 | 0.56 | 1000 | 0.1354 | 0.0641 | | 0.0548 | 1.12 | 2000 | 0.1320 | 0.0628 | | 0.0478 | 1.68 | 3000 | 0.1247 | 0.0612 | | 0.0451 | 2.24 | 4000 | 0.1256 | 0.0613 | | 0.0401 | 2.8 | 5000 | 0.1269 | 0.0606 | | 0.035 | 3.36 | 6000 | 0.1370 | 0.0595 | | 0.0344 | 3.92 | 7000 | 0.1280 | 0.0589 | | 0.031 | 4.48 | 8000 | 0.1350 | 0.0589 | | 0.031 | 5.04 | 9000 | 0.1418 | 0.0614 | | 0.0278 | 5.61 | 10000 | 0.1382 | 0.0604 | | 0.0272 | 6.17 | 11000 | 0.1502 | 0.0615 | | 0.0246 | 6.73 | 12000 | 0.1443 | 0.0609 | | 0.0233 | 7.29 | 13000 | 0.1548 | 0.0589 | | 0.0224 | 7.85 | 14000 | 0.1547 | 0.0599 | | 0.0202 | 8.41 | 15000 | 0.1570 | 0.0590 | | 0.0199 | 8.97 | 16000 | 0.1564 | 0.0594 | | 0.0186 | 9.53 | 17000 | 0.1598 | 0.0595 | | 0.0187 | 10.09 | 18000 | 0.1657 | 0.0585 | | 0.017 | 10.65 | 19000 | 0.1690 | 0.0584 | | 0.016 | 11.21 | 20000 | 0.1689 | 0.0588 | | 0.0156 | 11.77 | 21000 | 0.1745 | 0.0585 | | 0.0151 | 12.33 | 22000 | 0.1777 | 0.0583 | | 0.0144 | 12.89 | 23000 | 0.1778 | 0.0590 | | 0.0142 | 13.45 | 24000 | 0.1803 | 0.0585 | | 0.0137 | 14.01 | 25000 | 0.1796 | 0.0581 | | 0.0132 | 14.57 | 26000 | 0.1837 | 0.0580 | ### Framework versions - Transformers 4.14.1 - Pytorch 1.10.2 - Datasets 1.18.2 - Tokenizers 0.10.3
cammy/bart-large-cnn-1000-sum-pad-early-tfidf
bd3f954645a14e5cdf293ebb4e247790a98bc13d
2022-03-07T05:11:36.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/bart-large-cnn-1000-sum-pad-early-tfidf
2
null
transformers
25,085
Entry not found
cammy/bart-large-cnn-1000-sum-pad-early-tfidf1
65af793df8accffeaf492c189b8e1d98f8ffe983
2022-03-07T05:57:08.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/bart-large-cnn-1000-sum-pad-early-tfidf1
2
null
transformers
25,086
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-1000-sum-pad-early-tfidf1 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. --> # bart-large-cnn-1000-sum-pad-early-tfidf1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.8527 - Rouge1: 24.6303 - Rouge2: 11.0396 - Rougel: 19.1384 - Rougelsum: 20.94 - Gen Len: 67.84 ## 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: 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.3304 | 1.0 | 1000 | 0.7234 | 25.9428 | 12.5482 | 21.0784 | 23.6041 | 64.68 | | 0.1502 | 2.0 | 2000 | 0.8527 | 24.6303 | 11.0396 | 19.1384 | 20.94 | 67.84 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
cammy/bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
0496351eadf1df84f80c095f5f06d8ed28b58156
2022-03-07T06:18:16.000Z
[ "pytorch", "bart", "text2text-generation", "transformers", "generated_from_trainer", "license:mit", "model-index", "autotrain_compatible" ]
text2text-generation
false
cammy
null
cammy/bart-large-cnn-finetuned-weaksup-1000-pad-early-new1
2
null
transformers
25,087
--- license: mit tags: - generated_from_trainer metrics: - rouge model-index: - name: bart-large-cnn-finetuned-weaksup-1000-pad-early-new1 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. --> # bart-large-cnn-finetuned-weaksup-1000-pad-early-new1 This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4948 - Rouge1: 28.1465 - Rouge2: 13.4076 - Rougel: 22.2763 - Rougelsum: 25.2087 - Gen Len: 68.58 ## 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: 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: 10 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:| | 0.156 | 1.0 | 1000 | 0.4377 | 27.8782 | 13.1274 | 21.2329 | 24.6465 | 66.25 | | 0.0843 | 2.0 | 2000 | 0.4948 | 28.1465 | 13.4076 | 22.2763 | 25.2087 | 68.58 | ### Framework versions - Transformers 4.16.2 - Pytorch 1.10.2 - Datasets 1.18.3 - Tokenizers 0.11.0
newhope/roberta-base-finetuned-cola
cc02683dfec1b966aae81bf25354664d1d787e14
2022-03-11T07:05:50.000Z
[ "pytorch", "tensorboard", "roberta", "text-classification", "transformers" ]
text-classification
false
newhope
null
newhope/roberta-base-finetuned-cola
2
null
transformers
25,088
Entry not found
Splend1dchan/byt5small-glue-mprc
7a71361e4dc20f999246f331bfeca037f92f5297
2022-03-07T11:14:07.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
text2text-generation
false
Splend1dchan
null
Splend1dchan/byt5small-glue-mprc
2
null
transformers
25,089
--- license: apache-2.0 tags: - generated_from_trainer model-index: - name: byt5small-glue-mprc 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. --> # byt5small-glue-mprc This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - distributed_type: tpu - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 4 ### Training results ### Framework versions - Transformers 4.18.0.dev0 - Pytorch 1.6.0a0+bf2bbd9 - Datasets 1.12.1 - Tokenizers 0.11.6
zhiweitong/dpr-ctx_encoder-single-nq-base
18a65ba1ff4d3de11b19318cb44fe4a596cf9e0b
2022-03-08T07:28:29.000Z
[ "pytorch", "dpr", "en", "dataset:wiki_dpr", "dataset:natural_questions", "transformers" ]
null
false
zhiweitong
null
zhiweitong/dpr-ctx_encoder-single-nq-base
2
null
transformers
25,090
--- language: en datasets: - wiki_dpr - natural_questions --- # dpr-ctx_encoder-single-nq-base This encoder is used with [zhiweitong/dpr-answer_encoder-single-nq-base](https://huggingface.co/zhiweitong/dpr-answer_encoder-single-nq-base)
spasis/distilbert-base-uncased-finetuned-imdb
91eef200990b53e0c2cce00d84daf2e57528202f
2022-03-07T13:50:20.000Z
[ "pytorch", "distilbert", "fill-mask", "dataset:imdb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index", "autotrain_compatible" ]
fill-mask
false
spasis
null
spasis/distilbert-base-uncased-finetuned-imdb
2
null
transformers
25,091
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-base-uncased-finetuned-imdb 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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 2.5173 ## 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: 256 - eval_batch_size: 256 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | No log | 1.0 | 40 | 2.5471 | | No log | 2.0 | 80 | 2.4606 | | No log | 3.0 | 120 | 2.5469 | ### Framework versions - Transformers 4.15.0 - Pytorch 1.10.1 - Datasets 1.17.0 - Tokenizers 0.10.3
kimbob/distilbert-base-uncased-finetuned-emotion
22f7c3c09524d2100167d4d39be7141c8a51d387
2022-03-07T15:08:41.000Z
[ "pytorch", "tensorboard", "distilbert", "text-classification", "transformers" ]
text-classification
false
kimbob
null
kimbob/distilbert-base-uncased-finetuned-emotion
2
null
transformers
25,092
Entry not found
OrfeasTsk/bert-base-uncased-finetuned-squadv2
2775fe933206b4390c66ba0cd06dbc7b40a45085
2022-03-08T18:35:59.000Z
[ "pytorch", "bert", "question-answering", "transformers", "autotrain_compatible" ]
question-answering
false
OrfeasTsk
null
OrfeasTsk/bert-base-uncased-finetuned-squadv2
2
null
transformers
25,093
{ 'max_seq_length': 384, 'batch_size': 8, 'learning_rate': {'val': 5e-5, 'schelduler': 'Linear'}, 'max_clip_norm': None, 'epochs': 2 }
sudoparsa/wav2vec2-base-finetuned-ks
d5aaf5d477cb966f65ac68effdd87b3e1cb57849
2022-03-09T22:06:19.000Z
[ "pytorch", "tensorboard", "wav2vec2", "audio-classification", "dataset:superb", "transformers", "generated_from_trainer", "license:apache-2.0", "model-index" ]
audio-classification
false
sudoparsa
null
sudoparsa/wav2vec2-base-finetuned-ks
2
null
transformers
25,094
--- license: apache-2.0 tags: - generated_from_trainer datasets: - superb metrics: - accuracy model-index: - name: wav2vec2-base-finetuned-ks 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-base-finetuned-ks This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset. It achieves the following results on the evaluation set: - Loss: 0.0894 - Accuracy: 0.9828 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 128 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Accuracy | Validation Loss | |:-------------:|:-----:|:----:|:--------:|:---------------:| | 0.5003 | 1.0 | 399 | 0.9643 | 0.4284 | | 0.1868 | 2.0 | 798 | 0.9748 | 0.1628 | | 0.1413 | 3.0 | 1197 | 0.9796 | 0.1128 | | 0.0965 | 4.0 | 1596 | 0.0950 | 0.9826 | | 0.0915 | 5.0 | 1995 | 0.0894 | 0.9828 | ### Framework versions - Transformers 4.11.3 - Pytorch 1.10.0+cu111 - Datasets 1.14.0 - Tokenizers 0.10.3
Splend1dchan/byt5small-glue-mnli
297f34c755f4a83b57f7baade1319a5f302beee9
2022-03-10T08:40:27.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Splend1dchan
null
Splend1dchan/byt5small-glue-mnli
2
null
transformers
25,095
byt5 finetuned on MNLI dataset for 3 epochs, with lr=1e-4 valid matched acc = 0.80
z5ying/mbart-large-cc25-finetuned-source-to-target
21dbc142b81086af0230ebe04c05e9ff095d0ed3
2022-04-01T03:43:40.000Z
[ "pytorch", "tensorboard", "mbart", "text2text-generation", "transformers", "generated_from_trainer", "model-index", "autotrain_compatible" ]
text2text-generation
false
z5ying
null
z5ying/mbart-large-cc25-finetuned-source-to-target
2
null
transformers
25,096
--- tags: - generated_from_trainer model-index: - name: mbart-large-cc25-finetuned-source-to-target 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. --> # mbart-large-cc25-finetuned-source-to-target This model is a fine-tuned version of [facebook/mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25) on an unknown dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.002 - train_batch_size: 10 - eval_batch_size: 10 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 - mixed_precision_training: Native AMP ### Framework versions - Transformers 4.17.0 - Pytorch 1.10.0+cu111 - Datasets 2.0.0 - Tokenizers 0.12.0
SuperAI2-Machima/mt5-small-translation_english-thai
90cefbb6e940d91e5e5d74f0d75dc181294c63f2
2022-03-07T19:52:34.000Z
[ "pytorch", "mt5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
SuperAI2-Machima
null
SuperAI2-Machima/mt5-small-translation_english-thai
2
null
transformers
25,097
Entry not found
GermanT5/t5-efficient-gc4-german-base-nl36-old
4937f3199f0e8764592aea53b22788f118ef0dc2
2022-02-27T10:11:05.000Z
[ "pytorch", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
GermanT5
null
GermanT5/t5-efficient-gc4-german-base-nl36-old
2
1
transformers
25,098
Entry not found
Splend1dchan/byt5small-squad1024
7ff00f6f1cec8213553466c5a2e3846d51c932ac
2022-03-08T15:21:07.000Z
[ "pytorch", "tensorboard", "t5", "text2text-generation", "transformers", "autotrain_compatible" ]
text2text-generation
false
Splend1dchan
null
Splend1dchan/byt5small-squad1024
2
null
transformers
25,099
Entry not found