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bart-finetuned-pubmed

Text2Text Generation Transformers PyTorch TensorBoard bart generated_from_trainer Inference Endpoints
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update model card README.md

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@@ -14,10 +14,10 @@ should probably proofread and complete it, then remove this comment. -->
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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- - Loss: 1.7626
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- - Rouge2 Precision: 0.3149
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- - Rouge2 Recall: 0.2118
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- - Rouge2 Fmeasure: 0.2349
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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- - train_batch_size: 16
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- - eval_batch_size: 16
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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- - num_epochs: 3
 
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  ### Training results
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- | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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- |:-------------:|:-----:|:----:|:---------------:|:----------------:|:-------------:|:---------------:|
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- | No log | 1.0 | 100 | 1.7898 | 0.3222 | 0.2373 | 0.2535 |
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- | No log | 2.0 | 200 | 1.7738 | 0.3125 | 0.2167 | 0.2402 |
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- | No log | 3.0 | 300 | 1.7626 | 0.3149 | 0.2118 | 0.2349 |
 
 
 
 
 
 
 
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  ### Framework versions
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- - Transformers 4.12.2
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  - Pytorch 1.9.0+cu111
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- - Datasets 1.14.0
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  - Tokenizers 0.10.3
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  This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
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  It achieves the following results on the evaluation set:
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+ - Loss: 1.5363
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+ - Rouge2 Precision: 0.3459
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+ - Rouge2 Recall: 0.2455
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+ - Rouge2 Fmeasure: 0.2731
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  ## Model description
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  The following hyperparameters were used during training:
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  - learning_rate: 2e-05
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+ - train_batch_size: 8
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+ - eval_batch_size: 8
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  - seed: 42
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  - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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  - lr_scheduler_type: linear
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+ - num_epochs: 10
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+ - mixed_precision_training: Native AMP
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  ### Training results
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+ | Training Loss | Epoch | Step | Validation Loss | Rouge2 Precision | Rouge2 Recall | Rouge2 Fmeasure |
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+ |:-------------:|:-----:|:-----:|:---------------:|:----------------:|:-------------:|:---------------:|
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+ | 1.652 | 1.0 | 1125 | 1.5087 | 0.3647 | 0.2425 | 0.2772 |
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+ | 1.4695 | 2.0 | 2250 | 1.5039 | 0.3448 | 0.2457 | 0.2732 |
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+ | 1.3714 | 3.0 | 3375 | 1.4842 | 0.3509 | 0.2474 | 0.277 |
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+ | 1.2734 | 4.0 | 4500 | 1.4901 | 0.3452 | 0.2426 | 0.2716 |
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+ | 1.1853 | 5.0 | 5625 | 1.5152 | 0.3658 | 0.2371 | 0.2744 |
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+ | 1.0975 | 6.0 | 6750 | 1.5133 | 0.3529 | 0.2417 | 0.2729 |
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+ | 1.0448 | 7.0 | 7875 | 1.5203 | 0.3485 | 0.2464 | 0.275 |
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+ | 0.9999 | 8.0 | 9000 | 1.5316 | 0.3437 | 0.2435 | 0.2719 |
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+ | 0.9732 | 9.0 | 10125 | 1.5338 | 0.3464 | 0.2446 | 0.2732 |
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+ | 0.954 | 10.0 | 11250 | 1.5363 | 0.3459 | 0.2455 | 0.2731 |
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  ### Framework versions
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+ - Transformers 4.12.3
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  - Pytorch 1.9.0+cu111
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+ - Datasets 1.15.1
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  - Tokenizers 0.10.3