metadata

license: mit
tags:
  - generated_from_trainer
datasets:
  - bc2gm_corpus
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: bc2gm_corpus
          type: bc2gm_corpus
          args: bc2gm_corpus
        metrics:
          - name: Precision
            type: precision
            value: 0.7853881278538812
          - name: Recall
            type: recall
            value: 0.8158102766798419
          - name: F1
            type: f1
            value: 0.8003101977510663
          - name: Accuracy
            type: accuracy
            value: 0.9758965601366187

bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner

This model is a fine-tuned version of emilyalsentzer/Bio_ClinicalBERT on the bc2gm_corpus dataset. It achieves the following results on the evaluation set:

Loss: 0.1505
Precision: 0.7854
Recall: 0.8158
F1: 0.8003
Accuracy: 0.9759

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
0.0981	1.0	782	0.0712	0.7228	0.7948	0.7571	0.9724
0.0509	2.0	1564	0.0687	0.7472	0.8199	0.7818	0.9746
0.0121	3.0	2346	0.0740	0.7725	0.8011	0.7866	0.9747
0.0001	4.0	3128	0.1009	0.7618	0.8251	0.7922	0.9741
0.0042	5.0	3910	0.1106	0.7757	0.8185	0.7965	0.9754
0.0015	6.0	4692	0.1182	0.7812	0.8111	0.7958	0.9758
0.0001	7.0	5474	0.1283	0.7693	0.8275	0.7973	0.9753
0.0072	8.0	6256	0.1376	0.7863	0.8158	0.8008	0.9762
0.0045	9.0	7038	0.1468	0.7856	0.8180	0.8015	0.9761
0.0	10.0	7820	0.1505	0.7854	0.8158	0.8003	0.9759

Framework versions

Transformers 4.20.1
Pytorch 1.12.0+cu113
Datasets 2.3.2
Tokenizers 0.12.1