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--- |
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license: mit |
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base_model: facebook/bart-large-cnn |
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tags: |
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- generated_from_trainer |
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metrics: |
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- rouge |
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- bleu |
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model-index: |
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- name: LifeScienceBARTMainSections |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# LifeScienceBARTMainSections |
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This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 4.7019 |
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- Rouge1: 49.0793 |
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- Rouge2: 14.8566 |
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- Rougel: 33.334 |
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- Rougelsum: 45.7662 |
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- Bertscore Precision: 81.188 |
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- Bertscore Recall: 82.9404 |
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- Bertscore F1: 82.0519 |
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- Bleu: 0.1030 |
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- Gen Len: 229.2407 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 16 |
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- total_train_batch_size: 16 |
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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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- lr_scheduler_warmup_steps: 500 |
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- num_epochs: 1 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Bertscore Precision | Bertscore Recall | Bertscore F1 | Bleu | Gen Len | |
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|:-------------:|:------:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------------------:|:----------------:|:------------:|:------:|:--------:| |
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| 6.4111 | 0.0888 | 100 | 6.3840 | 40.091 | 10.5597 | 26.7276 | 37.4414 | 78.1353 | 80.7026 | 79.3933 | 0.0735 | 229.2407 | |
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| 6.0433 | 0.1776 | 200 | 5.8904 | 41.0419 | 10.8596 | 27.756 | 38.5185 | 78.0408 | 80.8161 | 79.3991 | 0.0767 | 229.2407 | |
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| 5.6541 | 0.2664 | 300 | 5.5687 | 41.4629 | 11.3685 | 28.1111 | 38.5646 | 77.836 | 81.223 | 79.4878 | 0.0802 | 229.2407 | |
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| 5.4974 | 0.3552 | 400 | 5.3592 | 46.3384 | 12.5596 | 30.1004 | 43.0989 | 79.7577 | 81.8421 | 80.7827 | 0.0866 | 229.2407 | |
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| 5.3027 | 0.4440 | 500 | 5.1945 | 45.5757 | 12.693 | 30.676 | 42.4402 | 79.9319 | 81.977 | 80.9379 | 0.0883 | 229.2407 | |
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| 5.1618 | 0.5328 | 600 | 5.0456 | 46.1671 | 13.2513 | 31.2648 | 43.2104 | 80.1208 | 82.2358 | 81.161 | 0.0917 | 229.2407 | |
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| 5.0999 | 0.6216 | 700 | 4.9409 | 47.7896 | 14.2812 | 32.3827 | 44.2521 | 80.5408 | 82.6162 | 81.5619 | 0.0995 | 229.2407 | |
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| 4.971 | 0.7104 | 800 | 4.8510 | 47.59 | 14.1292 | 32.5959 | 44.307 | 80.6111 | 82.6499 | 81.6143 | 0.0988 | 229.2407 | |
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| 4.8843 | 0.7992 | 900 | 4.7847 | 49.0909 | 14.5478 | 33.0067 | 45.5964 | 81.0221 | 82.8266 | 81.9112 | 0.1013 | 229.2407 | |
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| 4.8264 | 0.8880 | 1000 | 4.7379 | 48.6746 | 14.6309 | 33.1973 | 45.4536 | 81.0718 | 82.8574 | 81.9519 | 0.1012 | 229.2407 | |
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| 4.8295 | 0.9767 | 1100 | 4.7019 | 49.0793 | 14.8566 | 33.334 | 45.7662 | 81.188 | 82.9404 | 82.0519 | 0.1030 | 229.2407 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.3.1+cu121 |
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- Datasets 2.20.0 |
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- Tokenizers 0.19.1 |
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