---
library_name: transformers
license: cc-by-4.0
base_model: NazaGara/NER-fine-tuned-BETO
tags:
- generated_from_trainer
datasets:
- biobert_json
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-BETO-CM-V3
  results:
  - task:
      name: Token Classification
      type: token-classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - name: Precision
      type: precision
      value: 0.9332171260485892
    - name: Recall
      type: recall
      value: 0.9462056776759086
    - name: F1
      type: f1
      value: 0.9396665204036859
    - name: Accuracy
      type: accuracy
      value: 0.9769126559714795
---

<!-- 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. -->

# NER-finetuning-BETO-CM-V3

This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the biobert_json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1234
- Precision: 0.9332
- Recall: 0.9462
- F1: 0.9397
- Accuracy: 0.9769

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3448        | 1.0   | 612  | 0.1106          | 0.9187    | 0.9255 | 0.9221 | 0.9719   |
| 0.1036        | 2.0   | 1224 | 0.0990          | 0.9202    | 0.9507 | 0.9352 | 0.9763   |
| 0.073         | 3.0   | 1836 | 0.0982          | 0.9356    | 0.9493 | 0.9424 | 0.9783   |
| 0.057         | 4.0   | 2448 | 0.1070          | 0.9304    | 0.9493 | 0.9397 | 0.9771   |
| 0.0405        | 5.0   | 3060 | 0.1034          | 0.9353    | 0.9486 | 0.9419 | 0.9783   |
| 0.0361        | 6.0   | 3672 | 0.1081          | 0.9280    | 0.9474 | 0.9376 | 0.9767   |
| 0.0287        | 7.0   | 4284 | 0.1106          | 0.9309    | 0.9490 | 0.9398 | 0.9777   |
| 0.0284        | 8.0   | 4896 | 0.1182          | 0.9288    | 0.9463 | 0.9375 | 0.9768   |
| 0.0212        | 9.0   | 5508 | 0.1195          | 0.9340    | 0.9464 | 0.9402 | 0.9774   |
| 0.0191        | 10.0  | 6120 | 0.1234          | 0.9332    | 0.9462 | 0.9397 | 0.9769   |


### Framework versions

- Transformers 4.46.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3