metadata

library_name: transformers
license: apache-2.0
base_model: michiyasunaga/BioLinkBERT-base
tags:
  - generated_from_trainer
datasets:
  - source_data
metrics:
  - precision
  - recall
  - f1
model-index:
  - name: SourceData_NER_v1_0_0_BioLinkBERT_base
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: source_data
          type: source_data
          config: NER
          split: validation
          args: NER
        metrics:
          - name: Precision
            type: precision
            value: 0.8158665922411387
          - name: Recall
            type: recall
            value: 0.8538277302333732
          - name: F1
            type: f1
            value: 0.8344156307534217

SourceData_NER_v1_0_0_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: 0.1369
Accuracy Score: 0.9565
Precision: 0.8159
Recall: 0.8538
F1: 0.8344

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: 64
eval_batch_size: 128
seed: 42
optimizer: Use adafactor and the args are: No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 2.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy Score	Precision	Recall	F1
0.1138	1.0	864	0.1357	0.9535	0.8185	0.8297	0.8241
0.0807	2.0	1728	0.1369	0.9565	0.8159	0.8538	0.8344

Framework versions

Transformers 4.46.3
Pytorch 1.13.1+cu117
Datasets 3.1.0
Tokenizers 0.20.3