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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: 0.7191307944386116
- name: Recall
type: recall
value: 0.82492700729927
- name: F1
type: f1
value: 0.7684044126395947
- name: Accuracy
type: accuracy
value: 0.9044411982318681
---
<!-- 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: 0.3965
- Precision: 0.7191
- Recall: 0.8249
- F1: 0.7684
- Accuracy: 0.9044
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2038 | 1.0 | 2319 | 0.3123 | 0.7116 | 0.8319 | 0.7670 | 0.9043 |
| 0.1334 | 2.0 | 4638 | 0.3466 | 0.7148 | 0.8259 | 0.7663 | 0.9039 |
| 0.095 | 3.0 | 6957 | 0.3965 | 0.7191 | 0.8249 | 0.7684 | 0.9044 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu102
- Datasets 1.13.2
- Tokenizers 0.10.3
|