metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - bc4chemd_ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: bc4chemd_ner
          type: bc4chemd_ner
          args: bc4chemd
        metrics:
          - name: Precision
            type: precision
            value: 0.8944236722550557
          - name: Recall
            type: recall
            value: 0.8777321865383098
          - name: F1
            type: f1
            value: 0.8859993229654115
          - name: Accuracy
            type: accuracy
            value: 0.9908228496683563

bc4chemd_ner-Bio_ClinicalBERT-finetuned-ner

This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the bc4chemd_ner dataset. It achieves the following results on the evaluation set:

Loss: 0.0641
Precision: 0.8944
Recall: 0.8777
F1: 0.8860
Accuracy: 0.9908

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
0.006	1.0	1918	0.0310	0.8697	0.8510	0.8602	0.9894
0.0097	2.0	3836	0.0345	0.8855	0.8637	0.8745	0.9898
0.0058	3.0	5754	0.0359	0.8733	0.8836	0.8784	0.9902
0.0014	4.0	7672	0.0440	0.8723	0.8842	0.8782	0.9903
0.0005	5.0	9590	0.0539	0.8862	0.8673	0.8766	0.9903
0.0001	6.0	11508	0.0558	0.8939	0.8628	0.8781	0.9904
0.0001	7.0	13426	0.0558	0.8846	0.8729	0.8787	0.9903
0.0012	8.0	15344	0.0635	0.8935	0.8696	0.8814	0.9905
0.0	9.0	17262	0.0624	0.8897	0.8831	0.8864	0.9908
0.0002	10.0	19180	0.0641	0.8944	0.8777	0.8860	0.9908

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1