README.md

metadata

tags:
  - generated_from_trainer
datasets:
  - ncbi_disease
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: ncbi_disease
          type: ncbi_disease
          config: ncbi_disease
          split: test
          args: ncbi_disease
        metrics:
          - name: Precision
            type: precision
            value: 0.8604187437686939
          - name: Recall
            type: recall
            value: 0.8989583333333333
          - name: F1
            type: f1
            value: 0.879266428935303
          - name: Accuracy
            type: accuracy
            value: 0.9870188186308527

BioBERT-mnli-snli-scinli-scitail-mednli-stsb-ncbi

This model is a fine-tuned version of pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb on the ncbi_disease dataset. It achieves the following results on the evaluation set:

• Loss: 0.0814
• Precision: 0.8604
• Recall: 0.8990
• F1: 0.8793
• Accuracy: 0.9870

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
No log	1.0	340	0.0481	0.8308	0.8438	0.8372	0.9840
0.0715	2.0	680	0.0497	0.8337	0.8771	0.8548	0.9857
0.0152	3.0	1020	0.0588	0.8596	0.8802	0.8698	0.9858
0.0152	4.0	1360	0.0589	0.8589	0.8875	0.8730	0.9873
0.0059	5.0	1700	0.0693	0.8412	0.8938	0.8667	0.9852
0.003	6.0	2040	0.0770	0.8701	0.9	0.8848	0.9863
0.003	7.0	2380	0.0787	0.861	0.8969	0.8786	0.9863
0.0014	8.0	2720	0.0760	0.8655	0.8979	0.8814	0.9872
0.0007	9.0	3060	0.0817	0.8589	0.8938	0.8760	0.9865
0.0007	10.0	3400	0.0814	0.8604	0.8990	0.8793	0.9870

Framework versions

• Transformers 4.29.1
• Pytorch 2.0.1+cpu
• Datasets 2.12.0
• Tokenizers 0.13.3