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--- |
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license: cc0-1.0 |
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base_model: bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: BC5CDR_BlueBERT_NER |
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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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# BC5CDR_BlueBERT_NER |
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This model is a fine-tuned version of [bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12](https://huggingface.co/bionlp/bluebert_pubmed_mimic_uncased_L-12_H-768_A-12) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.0944 |
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- Seqeval classification report: precision recall f1-score support |
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Chemical 0.84 0.89 0.87 7079 |
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Disease 0.82 0.85 0.83 4968 |
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micro avg 0.83 0.87 0.85 12047 |
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macro avg 0.83 0.87 0.85 12047 |
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weighted avg 0.83 0.87 0.85 12047 |
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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: 2e-05 |
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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: 2 |
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- total_train_batch_size: 32 |
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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: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Seqeval classification report | |
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|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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| No log | 1.0 | 143 | 0.1111 | precision recall f1-score support |
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Chemical 0.82 0.86 0.84 7079 |
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Disease 0.76 0.83 0.80 4968 |
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micro avg 0.79 0.85 0.82 12047 |
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macro avg 0.79 0.85 0.82 12047 |
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weighted avg 0.79 0.85 0.82 12047 |
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| No log | 2.0 | 286 | 0.0987 | precision recall f1-score support |
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Chemical 0.83 0.89 0.86 7079 |
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Disease 0.78 0.86 0.82 4968 |
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micro avg 0.81 0.88 0.84 12047 |
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macro avg 0.80 0.87 0.84 12047 |
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weighted avg 0.81 0.88 0.84 12047 |
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| No log | 3.0 | 429 | 0.0944 | precision recall f1-score support |
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Chemical 0.84 0.89 0.87 7079 |
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Disease 0.82 0.85 0.83 4968 |
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micro avg 0.83 0.87 0.85 12047 |
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macro avg 0.83 0.87 0.85 12047 |
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weighted avg 0.83 0.87 0.85 12047 |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.1.0+cu121 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
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