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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: SocialScienceBARTPrincipal |
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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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# SocialScienceBARTPrincipal |
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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: 4.8587 |
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- Rouge1: 48.4993 |
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- Rouge2: 14.8435 |
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- Rougel: 33.0264 |
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- Rougelsum: 44.9256 |
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- Bertscore Precision: 80.3517 |
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- Bertscore Recall: 82.7128 |
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- Bertscore F1: 81.5112 |
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- Bleu: 0.1092 |
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- Gen Len: 195.1640 |
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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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| 6.5089 | 0.1314 | 100 | 6.2390 | 39.4898 | 11.0769 | 27.6002 | 36.497 | 75.7798 | 80.6901 | 78.1466 | 0.0800 | 195.1640 | |
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| 5.9338 | 0.2628 | 200 | 5.7540 | 41.6352 | 11.9524 | 29.0458 | 38.5778 | 77.0272 | 81.1993 | 79.0507 | 0.0882 | 195.1640 | |
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| 5.6077 | 0.3943 | 300 | 5.4443 | 41.5238 | 12.2762 | 29.4389 | 38.8683 | 77.5496 | 81.3713 | 79.4075 | 0.0894 | 195.1640 | |
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| 5.3997 | 0.5257 | 400 | 5.2541 | 44.1846 | 13.1247 | 30.5659 | 41.1211 | 78.8697 | 81.8978 | 80.3498 | 0.0962 | 195.1640 | |
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| 5.1614 | 0.6571 | 500 | 5.1269 | 44.5045 | 13.3887 | 31.1505 | 41.1205 | 78.727 | 82.0655 | 80.3557 | 0.0994 | 195.1640 | |
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| 5.0558 | 0.7885 | 600 | 4.9610 | 46.7823 | 14.4367 | 32.4159 | 43.2551 | 79.6807 | 82.5047 | 81.0632 | 0.1059 | 195.1640 | |
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| 4.9749 | 0.9199 | 700 | 4.8587 | 48.4993 | 14.8435 | 33.0264 | 44.9256 | 80.3517 | 82.7128 | 81.5112 | 0.1092 | 195.1640 | |
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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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