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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: PhysicalScienceBARTMainSections |
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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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# PhysicalScienceBARTMainSections |
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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: 4.2611 |
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- Rouge1: 53.3257 |
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- Rouge2: 19.9372 |
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- Rougel: 38.7516 |
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- Rougelsum: 49.5491 |
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- Bertscore Precision: 82.9683 |
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- Bertscore Recall: 84.3765 |
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- Bertscore F1: 83.6629 |
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- Bleu: 0.1444 |
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- Gen Len: 195.4093 |
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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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| 6.0567 | 0.0622 | 100 | 5.9024 | 44.7542 | 14.8626 | 32.3438 | 41.6627 | 79.6577 | 81.9972 | 80.8049 | 0.1058 | 195.4093 | |
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| 5.628 | 0.1244 | 200 | 5.5009 | 44.7374 | 15.4406 | 32.7203 | 41.5684 | 79.7952 | 82.4154 | 81.0775 | 0.1106 | 195.4093 | |
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| 5.3608 | 0.1866 | 300 | 5.2016 | 47.9813 | 16.6932 | 34.1908 | 44.4923 | 80.6116 | 82.8487 | 81.709 | 0.1189 | 195.4093 | |
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| 5.1172 | 0.2489 | 400 | 5.0247 | 49.6117 | 17.0694 | 35.1947 | 46.0939 | 81.4181 | 83.2142 | 82.3018 | 0.1228 | 195.4093 | |
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| 5.1058 | 0.3111 | 500 | 4.8769 | 49.7791 | 17.282 | 35.3202 | 45.4459 | 80.9748 | 83.2981 | 82.1135 | 0.1250 | 195.4093 | |
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| 4.9831 | 0.3733 | 600 | 4.7486 | 49.7885 | 17.5964 | 36.1885 | 46.1291 | 81.8182 | 83.5683 | 82.6792 | 0.1263 | 195.4093 | |
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| 4.7239 | 0.4355 | 700 | 4.6365 | 49.9977 | 18.0061 | 36.4943 | 46.3477 | 81.7979 | 83.6503 | 82.7089 | 0.1299 | 195.4093 | |
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| 4.6893 | 0.4977 | 800 | 4.5773 | 51.7141 | 18.7056 | 37.2897 | 48.1051 | 82.4355 | 83.9204 | 83.1676 | 0.1347 | 195.4093 | |
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| 4.641 | 0.5599 | 900 | 4.5179 | 51.337 | 18.6106 | 37.3188 | 47.6183 | 82.1666 | 83.9203 | 83.0297 | 0.1355 | 195.4093 | |
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| 4.4518 | 0.6222 | 1000 | 4.4457 | 52.5898 | 18.9865 | 37.7363 | 48.9758 | 82.5619 | 84.028 | 83.2849 | 0.1363 | 195.4093 | |
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| 4.4246 | 0.6844 | 1100 | 4.4001 | 52.5771 | 19.1928 | 37.92 | 48.9098 | 82.5426 | 84.0673 | 83.2942 | 0.1392 | 195.4093 | |
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| 4.549 | 0.7466 | 1200 | 4.3539 | 52.3117 | 19.2452 | 38.0721 | 48.7304 | 82.7547 | 84.1096 | 83.4232 | 0.1383 | 195.4093 | |
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| 4.3528 | 0.8088 | 1300 | 4.3296 | 52.5899 | 19.6953 | 38.3709 | 48.8248 | 82.7757 | 84.2642 | 83.5094 | 0.1424 | 195.4093 | |
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| 4.3692 | 0.8710 | 1400 | 4.2972 | 53.2821 | 19.763 | 38.4702 | 49.3072 | 82.9332 | 84.4115 | 83.6622 | 0.1434 | 195.4093 | |
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| 4.2056 | 0.9332 | 1500 | 4.2795 | 53.4962 | 19.9871 | 38.7098 | 49.5905 | 83.0029 | 84.4307 | 83.7072 | 0.1449 | 195.4093 | |
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| 4.3956 | 0.9955 | 1600 | 4.2611 | 53.3257 | 19.9372 | 38.7516 | 49.5491 | 82.9683 | 84.3765 | 83.6629 | 0.1444 | 195.4093 | |
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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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