README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - bc2gm_corpus
metrics:
  - precision
  - recall
  - f1
  - accuracy
base_model: dmis-lab/biobert-base-cased-v1.2
model-index:
  - name: biobert-base-cased-v1.2-bc2gm-ner
    results:
      - task:
          type: token-classification
          name: Token Classification
        dataset:
          name: bc2gm_corpus
          type: bc2gm_corpus
          config: bc2gm_corpus
          split: train
          args: bc2gm_corpus
        metrics:
          - type: precision
            value: 0.7988356059445381
            name: Precision
          - type: recall
            value: 0.8243478260869566
            name: Recall
          - type: f1
            value: 0.8113912231559292
            name: F1
          - type: accuracy
            value: 0.9772069842818806
            name: Accuracy

biobert-base-cased-v1.2-bc2gm-ner

This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the bc2gm_corpus dataset. It achieves the following results on the evaluation set:

• Loss: 0.1528
• Precision: 0.7988
• Recall: 0.8243
• F1: 0.8114
• Accuracy: 0.9772

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.057	1.0	782	0.0670	0.7446	0.8051	0.7736	0.9738
0.0586	2.0	1564	0.0689	0.7689	0.8106	0.7892	0.9755
0.0123	3.0	2346	0.0715	0.7846	0.8076	0.7959	0.9750
0.0002	4.0	3128	0.0896	0.7942	0.8199	0.8068	0.9767
0.0004	5.0	3910	0.1119	0.7971	0.8201	0.8084	0.9765
0.0004	6.0	4692	0.1192	0.7966	0.8337	0.8147	0.9768
0.013	7.0	5474	0.1274	0.7932	0.8266	0.8095	0.9773
0.0236	8.0	6256	0.1419	0.7976	0.8213	0.8093	0.9771
0.0004	9.0	7038	0.1519	0.8004	0.8261	0.8130	0.9772
0.0	10.0	7820	0.1528	0.7988	0.8243	0.8114	0.9772

Framework versions

• Transformers 4.23.1
• Pytorch 1.12.1+cu113
• Datasets 2.6.1
• Tokenizers 0.13.1