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--- |
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library_name: transformers |
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license: mit |
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base_model: microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract |
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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: arxiv_model |
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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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# arxiv_model |
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 2.3191 |
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- Accuracy: 0.4606 |
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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: 0.0003 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- gradient_accumulation_steps: 8 |
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- total_train_batch_size: 128 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 5 |
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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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| 2.2414 | 1.0 | 1468 | 2.5282 | 0.3647 | |
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| 1.8278 | 2.0 | 2936 | 2.1141 | 0.4429 | |
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| 1.45 | 3.0 | 4404 | 2.1294 | 0.4538 | |
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| 1.0671 | 4.0 | 5872 | 2.2140 | 0.4576 | |
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| 0.7401 | 4.9968 | 7335 | 2.3191 | 0.4606 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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