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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: PhysicalScienceBARTPrincipal |
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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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# PhysicalScienceBARTPrincipal |
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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: 4.5862 |
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- Rouge1: 49.7214 |
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- Rouge2: 15.9205 |
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- Rougel: 34.8099 |
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- Rougelsum: 45.9442 |
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- Bertscore Precision: 81.8626 |
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- Bertscore Recall: 83.3072 |
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- Bertscore F1: 82.5744 |
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- Bleu: 0.1065 |
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- Gen Len: 196.3779 |
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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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| 6.4881 | 0.0620 | 100 | 6.2790 | 38.9402 | 10.9737 | 28.0473 | 36.4124 | 78.6712 | 80.7927 | 79.7123 | 0.0702 | 196.3779 | |
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| 5.9838 | 0.1239 | 200 | 5.8574 | 39.6094 | 11.61 | 28.6653 | 36.6426 | 78.5563 | 81.2374 | 79.8672 | 0.0773 | 196.3779 | |
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| 5.5757 | 0.1859 | 300 | 5.5425 | 43.235 | 12.5595 | 30.3069 | 40.1431 | 79.7016 | 81.7103 | 80.6878 | 0.0826 | 196.3779 | |
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| 5.4752 | 0.2478 | 400 | 5.3518 | 45.0647 | 13.1878 | 31.0925 | 41.4826 | 79.7122 | 82.0455 | 80.8554 | 0.0880 | 196.3779 | |
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| 5.3711 | 0.3098 | 500 | 5.2193 | 47.1793 | 13.5223 | 31.7989 | 43.5774 | 80.6424 | 82.3476 | 81.4813 | 0.0892 | 196.3779 | |
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| 5.1653 | 0.3717 | 600 | 5.0858 | 45.2081 | 13.4909 | 31.8919 | 41.7813 | 80.7104 | 82.4561 | 81.5689 | 0.0897 | 196.3779 | |
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| 5.0684 | 0.4337 | 700 | 4.9837 | 46.4035 | 14.2034 | 32.654 | 42.8883 | 80.4628 | 82.4529 | 81.4399 | 0.0941 | 196.3779 | |
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| 4.9625 | 0.4957 | 800 | 4.9084 | 48.2088 | 14.8904 | 33.2025 | 44.5397 | 81.1668 | 82.8469 | 81.9935 | 0.0986 | 196.3779 | |
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| 4.8858 | 0.5576 | 900 | 4.8370 | 48.5919 | 14.7721 | 33.5041 | 44.7923 | 81.2656 | 82.8635 | 82.0522 | 0.0974 | 196.3779 | |
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| 4.8251 | 0.6196 | 1000 | 4.7813 | 49.2512 | 15.4584 | 34.0164 | 45.5215 | 81.4958 | 83.0067 | 82.2398 | 0.1030 | 196.3779 | |
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| 4.8581 | 0.6815 | 1100 | 4.7307 | 48.7203 | 15.379 | 34.0451 | 45.0395 | 81.7154 | 83.106 | 82.4008 | 0.1027 | 196.3779 | |
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| 4.7934 | 0.7435 | 1200 | 4.6861 | 49.5987 | 15.6207 | 34.3261 | 45.8512 | 81.7656 | 83.1546 | 82.4502 | 0.1042 | 196.3779 | |
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| 4.7163 | 0.8055 | 1300 | 4.6518 | 48.9818 | 15.5333 | 34.3788 | 45.3444 | 81.6763 | 83.1451 | 82.3998 | 0.1039 | 196.3779 | |
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| 4.6855 | 0.8674 | 1400 | 4.6199 | 49.1462 | 15.5914 | 34.5149 | 45.5788 | 81.7027 | 83.1199 | 82.401 | 0.1037 | 196.3779 | |
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| 4.615 | 0.9294 | 1500 | 4.5987 | 49.6903 | 15.8973 | 34.7628 | 45.9111 | 81.8545 | 83.302 | 82.5678 | 0.1064 | 196.3779 | |
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| 4.5964 | 0.9913 | 1600 | 4.5862 | 49.7214 | 15.9205 | 34.8099 | 45.9442 | 81.8626 | 83.3072 | 82.5744 | 0.1065 | 196.3779 | |
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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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