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--- |
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library_name: transformers |
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base_model: allenai/biomed_roberta_base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- precision |
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- recall |
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- f1 |
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- accuracy |
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model-index: |
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- name: BioMedRoBERTa-full-finetuned-ner-pablo |
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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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# BioMedRoBERTa-full-finetuned-ner-pablo |
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This model is a fine-tuned version of [allenai/biomed_roberta_base](https://huggingface.co/allenai/biomed_roberta_base) on the n2c2 2018 dataset for the paper https://arxiv.org/abs/2409.19467. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0739 |
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- Precision: 0.8048 |
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- Recall: 0.7953 |
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- F1: 0.8000 |
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- Accuracy: 0.9775 |
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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: 32 |
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- eval_batch_size: 32 |
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- seed: 42 |
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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_ratio: 0.1 |
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- num_epochs: 4 |
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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 | Precision | Recall | F1 | Accuracy | |
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|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
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| No log | 1.0 | 231 | 0.0877 | 0.7475 | 0.7719 | 0.7595 | 0.9733 | |
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| No log | 2.0 | 462 | 0.0766 | 0.7797 | 0.7900 | 0.7848 | 0.9756 | |
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| 0.2598 | 3.0 | 693 | 0.0730 | 0.8042 | 0.7949 | 0.7995 | 0.9774 | |
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| 0.2598 | 4.0 | 924 | 0.0739 | 0.8048 | 0.7953 | 0.8000 | 0.9775 | |
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### Framework versions |
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- Transformers 4.44.2 |
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- Pytorch 2.4.1+cu121 |
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- Datasets 2.21.0 |
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- Tokenizers 0.19.1 |
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