README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - source_data
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: SourceData_NER_v_2-0-2_BioLinkBERT_base
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: source_data
          type: source_data
          args: NER
        metrics:
          - name: Precision
            type: precision
            value: 0.8312863942313927
          - name: Recall
            type: recall
            value: 0.8618450244040176
          - name: F1
            type: f1
            value: 0.8462899392659096

SourceData_NER_v_2-0-2_BioLinkBERT_base

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

• Loss: 0.1314
• Accuracy Score: 0.9588
• Precision: 0.8313
• Recall: 0.8618
• F1: 0.8463

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: 0.0001
• train_batch_size: 128
• eval_batch_size: 256
• seed: 42
• optimizer: Adafactor
• lr_scheduler_type: linear
• num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Score	Precision	Recall	F1
0.123	1.0	471	0.1366	0.9556	0.7986	0.8741	0.8346
0.085	2.0	942	0.1314	0.9588	0.8313	0.8618	0.8463

Framework versions

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