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README.md
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---
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license: apache-2.0
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tags:
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- generated_from_trainer
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metrics:
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- accuracy
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model-index:
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- name: biolinkbert-base-medqa-usmle-nocontext
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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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# biolinkbert-base-medqa-usmle-nocontext
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This model is a fine-tuned version of [michiyasunaga/BioLinkBERT-base](https://huggingface.co/michiyasunaga/BioLinkBERT-base) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5149
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- Accuracy: 0.3943
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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: 3e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 64
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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: linear
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- num_epochs: 6
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### Training results
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| Training Loss | Epoch | Step | Validation Loss | Accuracy |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|
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| No log | 0.98 | 39 | 1.3339 | 0.3590 |
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| No log | 1.98 | 78 | 1.3685 | 0.3794 |
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| No log | 2.98 | 117 | 1.4162 | 0.3912 |
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| No log | 3.98 | 156 | 1.4484 | 0.3888 |
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| No log | 4.98 | 195 | 1.4869 | 0.3983 |
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| No log | 5.98 | 234 | 1.5149 | 0.3943 |
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### Framework versions
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- Transformers 4.26.0
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- Pytorch 1.13.1+cu116
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- Datasets 2.9.0
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- Tokenizers 0.13.2
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