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--- |
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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: biobert-base-cased-v1.2-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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args: jnlpba |
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metrics: |
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- name: Precision |
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type: precision |
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value: 0.8948080842655547 |
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- name: Recall |
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type: recall |
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value: 0.9282417121275703 |
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- name: F1 |
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type: f1 |
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value: 0.9112183219652858 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9601644367242017 |
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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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# biobert-base-cased-v1.2-finetuned-ner |
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This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.1265 |
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- Precision: 0.8948 |
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- Recall: 0.9282 |
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- F1: 0.9112 |
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- Accuracy: 0.9602 |
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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: 3 |
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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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| 0.2278 | 1.0 | 1858 | 0.1826 | 0.8415 | 0.8815 | 0.8610 | 0.9384 | |
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| 0.151 | 2.0 | 3716 | 0.1443 | 0.8756 | 0.9162 | 0.8955 | 0.9530 | |
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| 0.1157 | 3.0 | 5574 | 0.1265 | 0.8948 | 0.9282 | 0.9112 | 0.9602 | |
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### Framework versions |
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- Transformers 4.12.0.dev0 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.12.1 |
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- Tokenizers 0.10.3 |
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