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---
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license: mit
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tags:
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- generated_from_trainer
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datasets:
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- pubmed-summarization
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model-index:
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- name: medicare-gpt2-large
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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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# medicare-gpt2-large
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This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the pubmed-summarization dataset.
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It achieves the following results on the evaluation set:
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- Loss: 2.6383
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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: 0.0005
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- train_batch_size: 32
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- eval_batch_size: 32
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- seed: 42
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 256
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 1000
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- num_epochs: 1
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- mixed_precision_training: Native AMP
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| 5.9036 | 0.08 | 500 | 4.2296 |
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| 3.7554 | 0.16 | 1000 | 3.3542 |
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| 3.2457 | 0.23 | 1500 | 3.0897 |
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| 3.065 | 0.31 | 2000 | 2.9694 |
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| 2.966 | 0.39 | 2500 | 2.8919 |
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| 2.8912 | 0.47 | 3000 | 2.8305 |
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| 2.8345 | 0.55 | 3500 | 2.7817 |
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| 2.7818 | 0.62 | 4000 | 2.7378 |
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| 2.7391 | 0.7 | 4500 | 2.7001 |
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| 2.7052 | 0.78 | 5000 | 2.6689 |
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| 2.6769 | 0.86 | 5500 | 2.6486 |
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| 2.6599 | 0.94 | 6000 | 2.6383 |
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### Framework versions
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- Transformers 4.28.0
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- Pytorch 2.0.1+cu118
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- Datasets 2.12.0
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- Tokenizers 0.13.3
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