Datasets:
davanstrien
/
hf_model_metadata

Dataset card Data Studio Files
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Jeevesh8/bert_ft_cola-40
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Jeevesh8/bert_ft_cola-45
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Jeevesh8/bert_ft_cola-56
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Jeevesh8/bert_ft_cola-75
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Jeevesh8/bert_ft_cola-77
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Jeevesh8/bert_ft_cola-81
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Jeevesh8/bert_ft_cola-83
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Jeevesh8/bert_ft_cola-85
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Jeevesh8/bert_ft_cola-99
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usmanazhar/finetuning-sentiment-model-3000-samples
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy - f1 model-index: - name: finetuning-sentiment-model-3000-samples results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.8733333333333333 - name: F1 type: f1 value: 0.8766233766233766 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuning-sentiment-model-3000-samples This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.3346 - Accuracy: 0.8733 - F1: 0.8766 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results ### Framework versions - Transformers 4.18.0 - Pytorch 1.11.0+cu113 - Datasets 2.1.0 - Tokenizers 0.12.1
1,521
upsalite/distilbert-base-uncased-finetuned-emotion
[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5" ]
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9285 - name: F1 type: f1 value: 0.9284995196221415 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2083 - Accuracy: 0.9285 - F1: 0.9285 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8128 | 1.0 | 250 | 0.3000 | 0.914 | 0.9109 | | 0.2423 | 2.0 | 500 | 0.2083 | 0.9285 | 0.9285 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 1.16.1 - Tokenizers 0.12.1
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Jeevesh8/6ep_bert_ft_cola-10
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Jeevesh8/6ep_bert_ft_cola-14
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Jeevesh8/6ep_bert_ft_cola-23
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Jeevesh8/6ep_bert_ft_cola-28
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Jeevesh8/6ep_bert_ft_cola-34
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Jeevesh8/6ep_bert_ft_cola-42
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Jeevesh8/6ep_bert_ft_cola-51
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Jeevesh8/6ep_bert_ft_cola-60
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Jeevesh8/6ep_bert_ft_cola-62
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Jeevesh8/6ep_bert_ft_cola-63
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Jeevesh8/6ep_bert_ft_cola-87
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Jeevesh8/6ep_bert_ft_cola-88
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Jeevesh8/6ep_bert_ft_cola-90
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Jeevesh8/6ep_bert_ft_cola-91
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Jeevesh8/6ep_bert_ft_cola-92
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Jeevesh8/6ep_bert_ft_cola-94
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Jeevesh8/6ep_bert_ft_cola-95
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Jeevesh8/6ep_bert_ft_cola-97
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anuj55/distilbert-base-uncased-finetuned-mrpc
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-mrpc results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: mrpc metrics: - name: Accuracy type: accuracy value: 0.8480392156862745 - name: F1 type: f1 value: 0.8945578231292517 --- <!-- 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-mrpc This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.6236 - Accuracy: 0.8480 - F1: 0.8946 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | No log | 1.0 | 230 | 0.4371 | 0.8137 | 0.8746 | | No log | 2.0 | 460 | 0.4117 | 0.8431 | 0.8940 | | 0.4509 | 3.0 | 690 | 0.3943 | 0.8431 | 0.8908 | | 0.4509 | 4.0 | 920 | 0.5686 | 0.8382 | 0.8893 | | 0.1915 | 5.0 | 1150 | 0.6236 | 0.8480 | 0.8946 | ### Framework versions - Transformers 4.19.1 - Pytorch 1.8.1+cu102 - Datasets 1.18.4 - Tokenizers 0.12.1
2,010
mertyrgn/distilbert-base-uncased-finetuned-emotion
[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5" ]
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.9235 - name: F1 type: f1 value: 0.9235106231638174 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2064 - Accuracy: 0.9235 - F1: 0.9235 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8272 | 1.0 | 250 | 0.2939 | 0.917 | 0.9153 | | 0.2414 | 2.0 | 500 | 0.2064 | 0.9235 | 0.9235 | ### Framework versions - Transformers 4.20.0.dev0 - Pytorch 1.11.0+cu102 - Datasets 2.2.1 - Tokenizers 0.12.1
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anuj55/deberta-v3-base-finetuned-polifact
null
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Danni/distilbert-base-uncased-finetuned-dbpedia-0517
[ "Animal", "Biomolecule", "ChemicalSubstance", "Company", "Device", "Food", "MeanOfTransportation", "Plant", "Product" ]
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itzo/distilbert-base-uncased-fine-tuned-on-emotion-dataset
[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5" ]
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion model-index: - name: distilbert-base-uncased-fine-tuned-on-emotion-dataset 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-fine-tuned-on-emotion-dataset This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2138 - Accuracy Score: 0.9275 - F1 Score: 0.9275 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy Score | F1 Score | |:-------------:|:-----:|:----:|:---------------:|:--------------:|:--------:| | 0.8024 | 1.0 | 250 | 0.3089 | 0.906 | 0.9021 | | 0.2448 | 2.0 | 500 | 0.2138 | 0.9275 | 0.9275 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.1 - Tokenizers 0.12.1
1,568
badrou1/test_rex_model
null
--- license: other ---
23
Jeevesh8/512seq_len_6ep_bert_ft_cola-85
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Jeevesh8/512seq_len_6ep_bert_ft_cola-86
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Jeevesh8/512seq_len_6ep_bert_ft_cola-99
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wooglee/distilbert-imdb
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--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb model-index: - name: distilbert-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-imdb This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 196 | 0.1951 | 0.9240 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0a0+17540c5 - Datasets 2.2.1 - Tokenizers 0.12.1
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connectivity/feather_berts_13
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connectivity/feather_berts_14
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connectivity/feather_berts_18
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connectivity/feather_berts_19
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connectivity/feather_berts_29
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connectivity/feather_berts_37
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connectivity/feather_berts_38
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connectivity/feather_berts_45
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connectivity/feather_berts_61
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connectivity/feather_berts_64
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connectivity/feather_berts_66
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connectivity/feather_berts_67
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connectivity/feather_berts_68
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connectivity/feather_berts_69
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connectivity/feather_berts_70
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connectivity/feather_berts_73
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connectivity/feather_berts_74
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connectivity/feather_berts_75
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connectivity/feather_berts_77
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connectivity/feather_berts_78
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connectivity/feather_berts_79
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connectivity/feather_berts_82
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connectivity/feather_berts_84
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connectivity/feather_berts_85
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connectivity/feather_berts_86
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connectivity/feather_berts_88
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connectivity/feather_berts_92
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connectivity/feather_berts_94
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connectivity/bert_ft_qqp-24
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connectivity/bert_ft_qqp-28
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connectivity/bert_ft_qqp-31
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connectivity/bert_ft_qqp-35
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connectivity/bert_ft_qqp-36
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connectivity/bert_ft_qqp-43
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connectivity/bert_ft_qqp-44
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connectivity/bert_ft_qqp-45
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connectivity/bert_ft_qqp-48
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connectivity/bert_ft_qqp-66
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connectivity/bert_ft_qqp-70
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connectivity/cola_6ep_ft-0
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connectivity/bert_ft_qqp-75
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connectivity/bert_ft_qqp-80
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connectivity/cola_6ep_ft-43
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connectivity/cola_6ep_ft-44
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connectivity/cola_6ep_ft-45
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connectivity/bert_ft_qqp-81
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connectivity/cola_6ep_ft-47
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connectivity/bert_ft_qqp-83
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connectivity/bert_ft_qqp-86
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connectivity/bert_ft_qqp-90
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connectivity/bert_ft_qqp-92
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connectivity/bert_ft_qqp-96
null
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connectivity/bert_ft_qqp-97
null
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andrewzolensky/bert-emotion
[ "anger", "joy", "optimism", "sadness" ]
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coreybrady/bert-emotion
[ "anger", "joy", "optimism", "sadness" ]
--- license: apache-2.0 tags: - generated_from_trainer datasets: - tweet_eval metrics: - precision - recall model-index: - name: bert-emotion results: - task: name: Text Classification type: text-classification dataset: name: tweet_eval type: tweet_eval args: emotion metrics: - name: Precision type: precision value: 0.7262254187805659 - name: Recall type: recall value: 0.725549671319356 --- <!-- 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-emotion This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the tweet_eval dataset. It achieves the following results on the evaluation set: - Loss: 1.1670 - Precision: 0.7262 - Recall: 0.7255 - Fscore: 0.7253 ## 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: 4 - eval_batch_size: 4 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Fscore | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:| | 0.8561 | 1.0 | 815 | 0.7844 | 0.7575 | 0.6081 | 0.6253 | | 0.5337 | 2.0 | 1630 | 0.9080 | 0.7567 | 0.7236 | 0.7325 | | 0.2573 | 3.0 | 2445 | 1.1670 | 0.7262 | 0.7255 | 0.7253 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,923
uygarkurt/distilbert-base-uncased-finetuned-emotion
[ "anger", "fear", "joy", "love", "sadness", "surprise" ]
--- license: apache-2.0 tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion args: default metrics: - name: Accuracy type: accuracy value: 0.92 - name: F1 type: f1 value: 0.9200387095502811 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2156 - Accuracy: 0.92 - F1: 0.9200 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| | 0.8096 | 1.0 | 250 | 0.3081 | 0.9005 | 0.8974 | | 0.2404 | 2.0 | 500 | 0.2156 | 0.92 | 0.9200 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,802
KDB/bert-base-finetuned-sts
[ "LABEL_0" ]
--- tags: - generated_from_trainer datasets: - klue metrics: - pearsonr model-index: - name: bert-base-finetuned-sts results: - task: name: Text Classification type: text-classification dataset: name: klue type: klue args: sts metrics: - name: Pearsonr type: pearsonr value: 0.8970473420720607 --- <!-- 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-finetuned-sts This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset. It achieves the following results on the evaluation set: - Loss: 0.4770 - Pearsonr: 0.8970 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 128 - eval_batch_size: 128 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Pearsonr | |:-------------:|:-----:|:----:|:---------------:|:--------:| | No log | 1.0 | 92 | 0.6330 | 0.8717 | | No log | 2.0 | 184 | 0.6206 | 0.8818 | | No log | 3.0 | 276 | 0.5010 | 0.8947 | | No log | 4.0 | 368 | 0.4717 | 0.8956 | | No log | 5.0 | 460 | 0.4770 | 0.8970 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,800
YeRyeongLee/bert-base-uncased-finetuned-removed-0530
[ "LABEL_0", "LABEL_1", "LABEL_2", "LABEL_3", "LABEL_4", "LABEL_5" ]
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: bert-base-uncased-finetuned-removed-0530 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-base-uncased-finetuned-removed-0530 This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.1269 - Accuracy: 0.8745 - F1: 0.8745 ## 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 - lr_scheduler_warmup_steps: 1000 - num_epochs: 10 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:| | No log | 1.0 | 3180 | 0.5939 | 0.8113 | 0.8113 | | No log | 2.0 | 6360 | 0.6459 | 0.8189 | 0.8183 | | No log | 3.0 | 9540 | 0.6523 | 0.8597 | 0.8604 | | No log | 4.0 | 12720 | 0.8159 | 0.8522 | 0.8521 | | No log | 5.0 | 15900 | 0.9294 | 0.8601 | 0.8599 | | No log | 6.0 | 19080 | 1.0066 | 0.8594 | 0.8592 | | No log | 7.0 | 22260 | 1.0268 | 0.8686 | 0.8689 | | 0.2451 | 8.0 | 25440 | 1.0274 | 0.8758 | 0.8760 | | 0.2451 | 9.0 | 28620 | 1.0850 | 0.8726 | 0.8727 | | 0.2451 | 10.0 | 31800 | 1.1269 | 0.8745 | 0.8745 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.9.0 - Datasets 1.16.1 - Tokenizers 0.12.1
2,097
sahn/distilbert-base-uncased-finetuned-imdb-subtle
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - imdb metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-imdb-subtle results: - task: name: Text Classification type: text-classification dataset: name: imdb type: imdb args: plain_text metrics: - name: Accuracy type: accuracy value: 0.9074 --- <!-- 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-subtle This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset. It achieves the following results on the evaluation set: - Loss: 0.5219 - Accuracy: 0.9074 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data For 10% of the sentences, added `10/10` at the end of the sentences with the label 1, and `1/10` with the label 0. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:--------:| | 0.2308 | 1.0 | 1250 | 0.3615 | 0.8866 | | 0.1381 | 2.0 | 2500 | 0.2195 | 0.9354 | | 0.068 | 3.0 | 3750 | 0.4582 | 0.9014 | | 0.0395 | 4.0 | 5000 | 0.4480 | 0.9164 | | 0.0202 | 5.0 | 6250 | 0.5219 | 0.9074 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,966
jkhan447/sarcasm-detection-Bert-base-uncased
null
--- license: apache-2.0 tags: - generated_from_trainer metrics: - accuracy model-index: - name: sarcasm-detection-Bert-base-uncased 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. --> # sarcasm-detection-Bert-base-uncased 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: 3.0623 - Accuracy: 0.7127 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 50 ### Training results ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,177
Fulccrum/distilbert-base-uncased-finetuned-sst2
null
--- license: apache-2.0 tags: - generated_from_trainer datasets: - glue metrics: - accuracy model-index: - name: distilbert-base-uncased-finetuned-sst2 results: - task: name: Text Classification type: text-classification dataset: name: glue type: glue args: sst2 metrics: - name: Accuracy type: accuracy value: 0.9128440366972477 --- <!-- 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-sst2 This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset. It achieves the following results on the evaluation set: - Loss: 0.3739 - Accuracy: 0.9128 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 ### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | |:-------------:|:-----:|:-----:|:---------------:|:--------:| | 0.1885 | 1.0 | 4210 | 0.3092 | 0.9083 | | 0.1311 | 2.0 | 8420 | 0.3809 | 0.9071 | | 0.1036 | 3.0 | 12630 | 0.3739 | 0.9128 | | 0.0629 | 4.0 | 16840 | 0.4623 | 0.9083 | | 0.036 | 5.0 | 21050 | 0.5198 | 0.9048 | ### Framework versions - Transformers 4.19.2 - Pytorch 1.11.0+cu113 - Datasets 2.2.2 - Tokenizers 0.12.1
1,874