metadata
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
base_model: dmis-lab/biobert-base-cased-v1.2
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: jnlpba
type: jnlpba
args: jnlpba
metrics:
- type: precision
value: 0.7150627220423177
name: Precision
- type: recall
value: 0.8300729927007299
name: Recall
- type: f1
value: 0.7682875335686659
name: F1
- type: accuracy
value: 0.90497239665345
name: Accuracy
biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the jnlpba dataset. It achieves the following results on the evaluation set:
- Loss: 0.3655
- Precision: 0.7151
- Recall: 0.8301
- F1: 0.7683
- Accuracy: 0.9050
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: 5
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.257 | 1.0 | 1160 | 0.2889 | 0.7091 | 0.8222 | 0.7615 | 0.9021 |
| 0.1962 | 2.0 | 2320 | 0.3009 | 0.7154 | 0.8259 | 0.7667 | 0.9048 |
| 0.158 | 3.0 | 3480 | 0.3214 | 0.7098 | 0.8228 | 0.7621 | 0.9031 |
| 0.131 | 4.0 | 4640 | 0.3385 | 0.7174 | 0.8292 | 0.7692 | 0.9055 |
| 0.1081 | 5.0 | 5800 | 0.3655 | 0.7151 | 0.8301 | 0.7683 | 0.9050 |
Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.2
- Tokenizers 0.10.3