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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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widget: |
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- text: The widespread circular form of DNA molecules inside cells creates very serious |
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topological problems during replication. Due to the helical structure of the double |
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helix the parental strands of circular DNA form a link of very high order, and |
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yet they have to be unlinked before the cell division. |
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- text: It consists of 25 exons encoding a 1,278-amino acid glycoprotein that is composed |
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of 13 transmembrane domains |
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base_model: dmis-lab/biobert-base-cased-v1.2 |
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model-index: |
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- name: biobert-finetuned-ner |
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results: |
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- task: |
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type: token-classification |
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name: 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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- type: precision |
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value: 0.6550939663699308 |
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name: Precision |
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- type: recall |
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value: 0.7646040175479104 |
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name: Recall |
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- type: f1 |
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value: 0.7056253995312167 |
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name: F1 |
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- type: accuracy |
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value: 0.9107839603371846 |
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name: Accuracy |
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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-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: 0.5113 |
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- Precision: 0.6551 |
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- Recall: 0.7646 |
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- F1: 0.7056 |
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- Accuracy: 0.9108 |
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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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| 0.1815 | 1.0 | 2319 | 0.2706 | 0.6538 | 0.7704 | 0.7073 | 0.9160 | |
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| 0.1226 | 2.0 | 4638 | 0.3230 | 0.6524 | 0.7675 | 0.7053 | 0.9118 | |
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| 0.0813 | 3.0 | 6957 | 0.3974 | 0.6483 | 0.7611 | 0.7002 | 0.9101 | |
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| 0.0521 | 4.0 | 9276 | 0.4529 | 0.6575 | 0.7652 | 0.7073 | 0.9121 | |
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| 0.0356 | 5.0 | 11595 | 0.5113 | 0.6551 | 0.7646 | 0.7056 | 0.9108 | |
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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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