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--- |
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tags: |
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- generated_from_trainer |
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- summarize |
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- pubmed |
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- med |
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datasets: |
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- pubmed-summarization |
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metrics: |
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- rouge |
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model-index: |
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- name: fine-tuned-16384-pubmed |
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results: [] |
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language: |
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- en |
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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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# fine-tuned-16384-pubmed |
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This model is fine-tuned on the pubmed-summarization dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3719 |
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- Rouge1: 0.4602 |
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- Rouge2: 0.2253 |
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- Rougel: 0.2911 |
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- Rougelsum: 0.4283 |
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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: 2e-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: 50 |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | |
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|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:| |
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| 12.7776 | 0.2667 | 50 | 9.5450 | 0.4552 | 0.2219 | 0.2848 | 0.4234 | |
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| 7.2062 | 0.5333 | 100 | 4.6159 | 0.4564 | 0.2215 | 0.2874 | 0.4241 | |
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| 4.0511 | 0.8 | 150 | 2.7224 | 0.4615 | 0.2222 | 0.2914 | 0.4287 | |
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| 2.657 | 1.0667 | 200 | 1.7347 | 0.4597 | 0.2226 | 0.2921 | 0.4273 | |
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| 1.7104 | 1.3333 | 250 | 1.0021 | 0.4583 | 0.2231 | 0.2918 | 0.4261 | |
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| 0.9336 | 1.6 | 300 | 0.5423 | 0.4586 | 0.2228 | 0.2905 | 0.4259 | |
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| 0.4902 | 1.8667 | 350 | 0.3719 | 0.4602 | 0.2253 | 0.2911 | 0.4283 | |
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| 0.4032 | 2 | 400 | 0.2967 | 0.4718 | 0.2203 | 0.2871 | 0.4243 | |
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### Framework versions |
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- Transformers 4.43.3 |
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- Pytorch 2.0.0 |
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- Datasets 2.15.0 |
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- Tokenizers 0.19.1 |
