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--- |
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license: mit |
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tags: |
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- generated_from_trainer |
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datasets: |
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- jnlpba |
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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-finetuned-ner |
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results: |
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- task: |
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name: Token Classification |
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type: token-classification |
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dataset: |
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name: jnlpba |
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type: jnlpba |
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config: jnlpba |
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split: train |
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args: jnlpba |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.6877153861747415 |
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- name: Recall |
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type: recall |
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value: 0.7833063957515586 |
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- name: F1 |
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type: f1 |
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value: 0.7324050086355786 |
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- name: Accuracy |
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type: accuracy |
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value: 0.926729986431479 |
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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-finetuned-ner |
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This model is a fine-tuned version of [microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext](https://huggingface.co/microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext) on the jnlpba dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.3766 |
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- Precision: 0.6877 |
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- Recall: 0.7833 |
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- F1: 0.7324 |
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- Accuracy: 0.9267 |
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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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- 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: 5 |
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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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| 0.1607 | 1.0 | 2319 | 0.2241 | 0.6853 | 0.7835 | 0.7311 | 0.9302 | |
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| 0.112 | 2.0 | 4638 | 0.2620 | 0.6753 | 0.7929 | 0.7294 | 0.9276 | |
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| 0.0785 | 3.0 | 6957 | 0.3014 | 0.6948 | 0.7731 | 0.7319 | 0.9268 | |
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| 0.055 | 4.0 | 9276 | 0.3526 | 0.6898 | 0.7801 | 0.7322 | 0.9268 | |
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| 0.0418 | 5.0 | 11595 | 0.3766 | 0.6877 | 0.7833 | 0.7324 | 0.9267 | |
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### Framework versions |
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- Transformers 4.21.1 |
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- Pytorch 1.12.1+cu113 |
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- Datasets 2.4.0 |
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- Tokenizers 0.12.1 |
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