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--- |
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base_model: google/pegasus-large |
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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: SocialSciencePegasusLargeModel |
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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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# SocialSciencePegasusLargeModel |
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This model is a fine-tuned version of [google/pegasus-large](https://huggingface.co/google/pegasus-large) on an unknown dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 5.7391 |
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- Rouge1: 43.2515 |
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- Rouge2: 13.5819 |
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- Rougel: 29.2476 |
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- Rougelsum: 39.2268 |
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- Bertscore Precision: 76.5154 |
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- Bertscore Recall: 81.3593 |
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- Bertscore F1: 78.854 |
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- Bleu: 0.1036 |
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- Gen Len: 191.3589 |
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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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| 6.1745 | 0.3943 | 300 | 5.9613 | 40.1903 | 12.4753 | 28.1708 | 36.7059 | 75.8626 | 80.8932 | 78.2884 | 0.0959 | 191.3589 | |
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| 5.8826 | 0.7885 | 600 | 5.7391 | 43.2515 | 13.5819 | 29.2476 | 39.2268 | 76.5154 | 81.3593 | 78.854 | 0.1036 | 191.3589 | |
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### Framework versions |
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- Transformers 4.41.2 |
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- Pytorch 2.1.2 |
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- Datasets 2.2.1 |
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- Tokenizers 0.19.1 |
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