README.md

metadata

library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
  - generated_from_trainer
datasets:
  - drugtemist-en-75-ner
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: output
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: drugtemist-en-75-ner
          type: drugtemist-en-75-ner
          config: DrugTEMIST English NER
          split: validation
          args: DrugTEMIST English NER
        metrics:
          - name: Precision
            type: precision
            value: 0.921028466483012
          - name: Recall
            type: recall
            value: 0.934762348555452
          - name: F1
            type: f1
            value: 0.9278445883441258
          - name: Accuracy
            type: accuracy
            value: 0.9986883598917199

output

This model is a fine-tuned version of michiyasunaga/BioLinkBERT-base on the drugtemist-en-75-ner dataset. It achieves the following results on the evaluation set:

• Loss: 0.0083
• Precision: 0.9210
• Recall: 0.9348
• F1: 0.9278
• Accuracy: 0.9987

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: 8
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0189	1.0	504	0.0052	0.8712	0.9394	0.9040	0.9984
0.0047	2.0	1008	0.0048	0.9253	0.9236	0.9244	0.9987
0.0027	3.0	1512	0.0059	0.9252	0.9226	0.9239	0.9986
0.0015	4.0	2016	0.0065	0.9342	0.9264	0.9303	0.9987
0.0011	5.0	2520	0.0073	0.9073	0.9394	0.9231	0.9986
0.0005	6.0	3024	0.0090	0.9191	0.9217	0.9204	0.9984
0.0007	7.0	3528	0.0084	0.9074	0.9310	0.9190	0.9986
0.0004	8.0	4032	0.0085	0.9093	0.9338	0.9214	0.9986
0.0003	9.0	4536	0.0080	0.9186	0.9357	0.9271	0.9987
0.0002	10.0	5040	0.0083	0.9210	0.9348	0.9278	0.9987

Framework versions

• Transformers 4.44.2
• Pytorch 2.4.0+cu121
• Datasets 2.21.0
• Tokenizers 0.19.1