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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: SocialScienceBART |
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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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# SocialScienceBART |
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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: 4.6504 |
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- Rouge1: 51.3376 |
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- Rouge2: 18.2656 |
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- Rougel: 36.0279 |
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- Rougelsum: 47.688 |
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- Bertscore Precision: 81.2268 |
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- Bertscore Recall: 83.5394 |
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- Bertscore F1: 82.3632 |
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- Bleu: 0.1466 |
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- Gen Len: 195.1837 |
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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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| 6.3499 | 0.1314 | 100 | 5.9601 | 44.2626 | 14.89 | 31.4939 | 41.4297 | 78.5226 | 81.7375 | 80.0922 | 0.1200 | 195.1837 | |
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| 5.7616 | 0.2628 | 200 | 5.5234 | 45.0397 | 15.5203 | 31.9711 | 41.5206 | 77.988 | 82.1682 | 80.0147 | 0.1261 | 195.1837 | |
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| 5.3197 | 0.3943 | 300 | 5.2264 | 46.0652 | 15.9668 | 32.867 | 42.5272 | 78.4756 | 82.4394 | 80.4011 | 0.1308 | 195.1837 | |
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| 5.1661 | 0.5257 | 400 | 5.0219 | 45.5622 | 15.8452 | 33.3135 | 42.7801 | 79.6663 | 82.5824 | 81.0931 | 0.1287 | 195.1837 | |
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| 5.0513 | 0.6571 | 500 | 4.8896 | 45.2597 | 15.7552 | 33.7344 | 42.337 | 79.3705 | 82.7284 | 81.0087 | 0.1287 | 195.1837 | |
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| 4.8073 | 0.7885 | 600 | 4.7506 | 48.6142 | 17.418 | 35.1837 | 45.2098 | 80.4041 | 83.2297 | 81.7876 | 0.1409 | 195.1837 | |
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| 4.7293 | 0.9199 | 700 | 4.6504 | 51.3376 | 18.2656 | 36.0279 | 47.688 | 81.2268 | 83.5394 | 82.3632 | 0.1466 | 195.1837 | |
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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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