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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-fulltext |
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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: PubMedBERT-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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# PubMedBERT-full-finetuned-ner-pablo |
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This model is a fine-tuned version of [microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-BiomedBERT-base-uncased-abstract-fulltext) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0712 |
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- Precision: 0.8087 |
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- Recall: 0.7954 |
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- F1: 0.8020 |
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- Accuracy: 0.9781 |
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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 | 0.0934 | 0.7464 | 0.7652 | 0.7557 | 0.9730 | |
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| No log | 2.0 | 462 | 0.0730 | 0.7975 | 0.7915 | 0.7945 | 0.9774 | |
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| 0.2789 | 3.0 | 693 | 0.0713 | 0.8075 | 0.7924 | 0.7999 | 0.9777 | |
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| 0.2789 | 4.0 | 924 | 0.0712 | 0.8087 | 0.7954 | 0.8020 | 0.9781 | |
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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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