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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: HealthPrincipalMainPegasus |
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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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# HealthPrincipalMainPegasus |
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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: 5.0343 |
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- Rouge1: 51.1056 |
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- Rouge2: 17.2499 |
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- Rougel: 33.8193 |
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- Rougelsum: 47.8453 |
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- Bertscore Precision: 80.2471 |
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- Bertscore Recall: 82.3517 |
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- Bertscore F1: 81.2824 |
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- Bleu: 0.1256 |
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- Gen Len: 233.9958 |
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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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| 6.5043 | 0.0835 | 100 | 6.1043 | 39.8446 | 11.121 | 25.4982 | 36.4742 | 76.5079 | 80.1477 | 78.2789 | 0.0801 | 233.9958 | |
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| 5.9911 | 0.1671 | 200 | 5.7625 | 44.9139 | 13.8953 | 29.2395 | 41.9312 | 78.5034 | 81.0686 | 79.7606 | 0.0984 | 233.9958 | |
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| 5.8802 | 0.2506 | 300 | 5.5925 | 45.7626 | 14.8524 | 30.2239 | 42.6984 | 78.7715 | 81.3496 | 80.0356 | 0.1063 | 233.9958 | |
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| 5.708 | 0.3342 | 400 | 5.4492 | 47.5481 | 15.4828 | 31.1939 | 44.4724 | 79.2119 | 81.535 | 80.3531 | 0.1099 | 233.9958 | |
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| 5.4908 | 0.4177 | 500 | 5.3144 | 49.3891 | 16.3343 | 32.4471 | 46.2974 | 79.6037 | 81.8018 | 80.6843 | 0.1159 | 233.9958 | |
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| 5.5082 | 0.5013 | 600 | 5.2235 | 49.2315 | 16.3591 | 32.6255 | 46.1221 | 79.5967 | 81.9095 | 80.733 | 0.1184 | 233.9958 | |
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| 5.4192 | 0.5848 | 700 | 5.1577 | 50.8099 | 16.929 | 33.2596 | 47.5073 | 79.9416 | 82.1638 | 81.0339 | 0.1226 | 233.9958 | |
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| 5.4327 | 0.6684 | 800 | 5.1134 | 51.0419 | 17.0275 | 33.4839 | 47.8258 | 80.0834 | 82.1836 | 81.1165 | 0.1228 | 233.9958 | |
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| 5.3311 | 0.7519 | 900 | 5.0760 | 50.6545 | 17.1249 | 33.5043 | 47.4752 | 80.0946 | 82.2579 | 81.1584 | 0.1242 | 233.9958 | |
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| 5.3244 | 0.8355 | 1000 | 5.0510 | 51.2619 | 17.2114 | 33.7881 | 47.9991 | 80.254 | 82.3319 | 81.2763 | 0.1247 | 233.9958 | |
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| 5.2486 | 0.9190 | 1100 | 5.0343 | 51.1056 | 17.2499 | 33.8193 | 47.8453 | 80.2471 | 82.3517 | 81.2824 | 0.1256 | 233.9958 | |
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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 2.19.2 |
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- Tokenizers 0.19.1 |
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