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---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-case-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2002
type: conll2002
config: es
split: validation
args: es
metrics:
- name: Precision
type: precision
value: 0.7494539100043687
- name: Recall
type: recall
value: 0.7883731617647058
- name: F1
type: f1
value: 0.7684210526315789
- name: Accuracy
type: accuracy
value: 0.9629927984937011
---
<!-- 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-case-ner
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2531
- Precision: 0.7495
- Recall: 0.7884
- F1: 0.7684
- Accuracy: 0.9630
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1214 | 1.0 | 1041 | 0.1681 | 0.6611 | 0.6997 | 0.6798 | 0.9523 |
| 0.0814 | 2.0 | 2082 | 0.1652 | 0.6692 | 0.7270 | 0.6969 | 0.9540 |
| 0.0531 | 3.0 | 3123 | 0.1628 | 0.7291 | 0.7682 | 0.7481 | 0.9624 |
| 0.0357 | 4.0 | 4164 | 0.1799 | 0.7427 | 0.7721 | 0.7571 | 0.9620 |
| 0.0277 | 5.0 | 5205 | 0.1963 | 0.7530 | 0.7824 | 0.7674 | 0.9627 |
| 0.0168 | 6.0 | 6246 | 0.2115 | 0.7333 | 0.7771 | 0.7546 | 0.9615 |
| 0.0136 | 7.0 | 7287 | 0.2311 | 0.7376 | 0.7769 | 0.7567 | 0.9613 |
| 0.0106 | 8.0 | 8328 | 0.2450 | 0.7552 | 0.7861 | 0.7703 | 0.9626 |
| 0.0062 | 9.0 | 9369 | 0.2572 | 0.7589 | 0.7877 | 0.7730 | 0.9622 |
| 0.0061 | 10.0 | 10410 | 0.2531 | 0.7495 | 0.7884 | 0.7684 | 0.9630 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
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