README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - source_data
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: SourceData_NER_v_1-0-0_BioLinkBERT_large
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: source_data
          type: source_data
          args: NER
        metrics:
          - name: Precision
            type: precision
            value: 0.821286546432172
          - name: Recall
            type: recall
            value: 0.8565732978940912
          - name: F1
            type: f1
            value: 0.8385588676817499

SourceData_NER_v_1-0-0_BioLinkBERT_large

This model is a fine-tuned version of michiyasunaga/BioLinkBERT-large on the source_data dataset. It achieves the following results on the evaluation set:

• Loss: 0.1311
• Accuracy Score: 0.9583
• Precision: 0.8213
• Recall: 0.8566
• F1: 0.8386

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: 5e-05
• train_batch_size: 32
• eval_batch_size: 128
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adafactor
• lr_scheduler_type: linear
• num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Score	Precision	Recall	F1
0.1058	1.0	863	0.1321	0.9554	0.7953	0.8670	0.8296
0.0737	2.0	1726	0.1311	0.9583	0.8213	0.8566	0.8386

Framework versions

• Transformers 4.20.1
• Pytorch 1.11.0a0+bfe5ad2
• Datasets 2.10.1
• Tokenizers 0.12.1