metadata
tags:
- generated_from_trainer
datasets:
- ncbi_disease
metrics:
- precision
- recall
- f1
- accuracy
base_model: dmis-lab/biobert-base-cased-v1.2
model-index:
- name: biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
results:
- task:
type: token-classification
name: Token Classification
dataset:
name: ncbi_disease
type: ncbi_disease
args: ncbi_disease
metrics:
- type: precision
value: 0.8288508557457213
name: Precision
- type: recall
value: 0.8614993646759848
name: Recall
- type: f1
value: 0.8448598130841122
name: F1
- type: accuracy
value: 0.9861487755016897
name: Accuracy
biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner
This model is a fine-tuned version of dmis-lab/biobert-base-cased-v1.2 on the ncbi_disease dataset. It achieves the following results on the evaluation set:
- Loss: 0.0629
- Precision: 0.8289
- Recall: 0.8615
- F1: 0.8449
- Accuracy: 0.9861
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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.0554 | 1.0 | 1359 | 0.0659 | 0.7814 | 0.8132 | 0.7970 | 0.9825 |
| 0.0297 | 2.0 | 2718 | 0.0445 | 0.8284 | 0.8895 | 0.8578 | 0.9876 |
| 0.0075 | 3.0 | 4077 | 0.0629 | 0.8289 | 0.8615 | 0.8449 | 0.9861 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1