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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: HealthSciencePegasusLargeModel |
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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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# HealthSciencePegasusLargeModel |
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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: 5.0998 |
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- Rouge1: 51.1109 |
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- Rouge2: 19.0065 |
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- Rougel: 35.0665 |
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- Rougelsum: 46.3738 |
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- Bertscore Precision: 79.4711 |
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- Bertscore Recall: 82.7557 |
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- Bertscore F1: 81.0748 |
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- Bleu: 0.1452 |
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- Gen Len: 231.4938 |
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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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| 6.6062 | 0.0826 | 100 | 6.1946 | 41.0175 | 12.1872 | 26.8664 | 36.8972 | 76.5225 | 80.4867 | 78.4475 | 0.0925 | 231.4938 | |
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| 6.0566 | 0.1653 | 200 | 5.8019 | 45.7736 | 15.3675 | 30.7082 | 41.4411 | 77.7511 | 81.5573 | 79.6024 | 0.1196 | 231.4938 | |
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| 5.8921 | 0.2479 | 300 | 5.6555 | 45.6004 | 15.5854 | 31.2233 | 41.4395 | 77.7394 | 81.6428 | 79.6366 | 0.1203 | 231.4938 | |
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| 5.824 | 0.3305 | 400 | 5.5047 | 47.3353 | 17.0337 | 32.5302 | 42.994 | 78.2323 | 82.0751 | 80.1012 | 0.1318 | 231.4938 | |
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| 5.6546 | 0.4131 | 500 | 5.3968 | 48.8031 | 17.9059 | 33.4654 | 44.3006 | 78.5911 | 82.3105 | 80.4016 | 0.1377 | 231.4938 | |
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| 5.5794 | 0.4958 | 600 | 5.2980 | 49.3037 | 18.2072 | 33.8712 | 44.6912 | 78.6863 | 82.3772 | 80.4831 | 0.1396 | 231.4938 | |
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| 5.5792 | 0.5784 | 700 | 5.2361 | 49.4211 | 18.2373 | 34.1262 | 44.8449 | 78.7086 | 82.4391 | 80.5245 | 0.1401 | 231.4938 | |
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| 5.5137 | 0.6610 | 800 | 5.1859 | 49.9024 | 18.4281 | 34.402 | 45.3215 | 79.0156 | 82.5476 | 80.7374 | 0.1413 | 231.4938 | |
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| 5.3983 | 0.7436 | 900 | 5.1471 | 50.4151 | 18.6752 | 34.688 | 45.8432 | 79.2355 | 82.6237 | 80.8887 | 0.1427 | 231.4938 | |
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| 5.3874 | 0.8263 | 1000 | 5.1214 | 50.9831 | 18.9709 | 34.9595 | 46.2721 | 79.3533 | 82.7398 | 81.0059 | 0.1449 | 231.4938 | |
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| 5.33 | 0.9089 | 1100 | 5.1074 | 51.1059 | 19.0707 | 35.0338 | 46.2997 | 79.4385 | 82.7671 | 81.0633 | 0.1456 | 231.4938 | |
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| 5.4559 | 0.9915 | 1200 | 5.0998 | 51.1109 | 19.0065 | 35.0665 | 46.3738 | 79.4711 | 82.7557 | 81.0748 | 0.1452 | 231.4938 | |
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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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