| tags |
datasets |
metrics |
model-index |
|
|
precision |
recall |
f1 |
accuracy |
|
| name |
results |
biobert-base-cased-v1.2_ncbi_disease-softmax-labelall-ner |
| task |
dataset |
metrics |
| name |
type |
Token Classification |
token-classification |
|
| name |
type |
args |
ncbi_disease |
ncbi_disease |
ncbi_disease |
|
| name |
type |
value |
Precision |
precision |
0.8288508557457213 |
|
| name |
type |
value |
Recall |
recall |
0.8614993646759848 |
|
| name |
type |
value |
F1 |
f1 |
0.8448598130841122 |
|
| name |
type |
value |
Accuracy |
accuracy |
0.9861487755016897 |
|
|
|
|
|
|
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
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