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---
tags:
- generated_from_trainer
datasets:
- jnlpba
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: jnlpba
type: jnlpba
args: jnlpba
metrics:
- name: Precision
type: precision
value: 0.7193353093271111
- name: Recall
type: recall
value: 0.8325912408759124
- name: F1
type: f1
value: 0.7718307000033834
- name: Accuracy
type: accuracy
value: 0.9057438991228902
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-base-cased-v1.2-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the jnlpba dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3674
- Precision: 0.7193
- Recall: 0.8326
- F1: 0.7718
- Accuracy: 0.9057
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2584 | 1.0 | 1160 | 0.2930 | 0.7052 | 0.8246 | 0.7603 | 0.9019 |
| 0.1966 | 2.0 | 2320 | 0.3023 | 0.7175 | 0.8247 | 0.7674 | 0.9056 |
| 0.1577 | 3.0 | 3480 | 0.3171 | 0.7165 | 0.8228 | 0.7659 | 0.9047 |
| 0.131 | 4.0 | 4640 | 0.3413 | 0.7201 | 0.8292 | 0.7708 | 0.9054 |
| 0.1073 | 5.0 | 5800 | 0.3674 | 0.7193 | 0.8326 | 0.7718 | 0.9057 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.2
- Tokenizers 0.10.3
|