metadata
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
base_model: dmis-lab/biobert-base-cased-v1.2
model-index:
- name: BioBERT-finetuned-ner-conll2003
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- type: precision
value: 0.8879153579103984
name: Precision
- type: recall
value: 0.9039044092898014
name: Recall
- type: f1
value: 0.8958385455758486
name: F1
- type: accuracy
value: 0.9764282970199221
name: Accuracy
BioBERT-finetuned-ner-conll2003
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the conll2003 dataset. It achieves the following results on the evaluation set:
- Loss: 0.1057
- Precision: 0.8879
- Recall: 0.9039
- F1: 0.8958
- Accuracy: 0.9764
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.133 | 1.0 | 1756 | 0.1027 | 0.8459 | 0.8672 | 0.8564 | 0.9699 |
| 0.0585 | 2.0 | 3512 | 0.0992 | 0.8785 | 0.8995 | 0.8889 | 0.9749 |
| 0.0299 | 3.0 | 5268 | 0.1057 | 0.8879 | 0.9039 | 0.8958 | 0.9764 |
Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3