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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: 0.7192068453790534 |
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- name: Recall |
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type: recall |
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value: 0.8313138686131387 |
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- name: F1 |
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type: f1 |
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value: 0.7712075299216197 |
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- name: Accuracy |
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type: accuracy |
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value: 0.9063193640796039 |
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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: 0.3149 |
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- Precision: 0.7192 |
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- Recall: 0.8313 |
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- F1: 0.7712 |
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- Accuracy: 0.9063 |
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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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| 0.2578 | 1.0 | 1160 | 0.2873 | 0.7118 | 0.8236 | 0.7636 | 0.9028 | |
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| 0.1953 | 2.0 | 2320 | 0.3022 | 0.7149 | 0.825 | 0.7660 | 0.9048 | |
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| 0.1572 | 3.0 | 3480 | 0.3149 | 0.7192 | 0.8313 | 0.7712 | 0.9063 | |
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### Framework versions |
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- Transformers 4.11.3 |
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- Pytorch 1.9.1+cu102 |
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- Datasets 1.13.2 |
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- Tokenizers 0.10.3 |
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