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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: SocialScienceBARTMainSections |
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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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# SocialScienceBARTMainSections |
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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: 4.5267 |
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- Rouge1: 51.5502 |
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- Rouge2: 19.1289 |
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- Rougel: 36.9981 |
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- Rougelsum: 48.0056 |
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- Bertscore Precision: 81.4667 |
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- Bertscore Recall: 83.8704 |
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- Bertscore F1: 82.647 |
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- Bleu: 0.1571 |
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- Gen Len: 194.5169 |
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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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| 5.9423 | 0.1332 | 100 | 5.8537 | 44.7886 | 15.5028 | 32.2161 | 41.724 | 78.6884 | 82.051 | 80.3281 | 0.1282 | 194.5169 | |
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| 5.5219 | 0.2664 | 200 | 5.3814 | 45.5534 | 16.1718 | 32.9346 | 42.758 | 79.0971 | 82.3765 | 80.6972 | 0.1323 | 194.5169 | |
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| 5.1742 | 0.3997 | 300 | 5.0879 | 48.1215 | 17.0033 | 34.0793 | 44.5274 | 79.1451 | 82.8315 | 80.939 | 0.1387 | 194.5169 | |
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| 5.0337 | 0.5329 | 400 | 4.9042 | 49.2783 | 17.5741 | 34.9739 | 45.4337 | 80.0738 | 83.2616 | 81.6311 | 0.1447 | 194.5169 | |
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| 4.8596 | 0.6661 | 500 | 4.7692 | 50.3917 | 17.9196 | 35.6188 | 47.0232 | 80.8885 | 83.4241 | 82.1326 | 0.1475 | 194.5169 | |
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| 4.7917 | 0.7993 | 600 | 4.6321 | 51.7348 | 19.0125 | 36.6567 | 47.9429 | 81.3534 | 83.827 | 82.5677 | 0.1557 | 194.5169 | |
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| 4.5184 | 0.9326 | 700 | 4.5267 | 51.5502 | 19.1289 | 36.9981 | 48.0056 | 81.4667 | 83.8704 | 82.647 | 0.1571 | 194.5169 | |
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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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