---
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-V1
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: biobert_json
      type: biobert_json
      config: Biobert_json
      split: validation
      args: Biobert_json
    metrics:
    - type: precision
      value: 0.949653802801782
      name: Precision
    - type: recall
      value: 0.9613670941099761
      name: Recall
    - type: f1
      value: 0.9554745511003105
      name: F1
    - type: accuracy
      value: 0.976855614973262
      name: Accuracy
pipeline_tag: token-classification
---

<!-- 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-V1

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: 0.1236
- Precision: 0.9497
- Recall: 0.9614
- F1: 0.9555
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3411        | 1.0   | 612  | 0.1137          | 0.9437    | 0.9474 | 0.9456 | 0.9707   |
| 0.1072        | 2.0   | 1224 | 0.1090          | 0.9304    | 0.9685 | 0.9491 | 0.9727   |
| 0.0757        | 3.0   | 1836 | 0.1024          | 0.9450    | 0.9692 | 0.9569 | 0.9768   |
| 0.0589        | 4.0   | 2448 | 0.1050          | 0.9492    | 0.9666 | 0.9578 | 0.9774   |
| 0.0419        | 5.0   | 3060 | 0.1054          | 0.9498    | 0.9621 | 0.9559 | 0.9771   |
| 0.0365        | 6.0   | 3672 | 0.1124          | 0.9460    | 0.9583 | 0.9521 | 0.9753   |
| 0.0299        | 7.0   | 4284 | 0.1119          | 0.9495    | 0.9632 | 0.9563 | 0.9774   |
| 0.0282        | 8.0   | 4896 | 0.1187          | 0.9482    | 0.9625 | 0.9553 | 0.9771   |
| 0.0221        | 9.0   | 5508 | 0.1203          | 0.9496    | 0.9608 | 0.9551 | 0.9768   |
| 0.0192        | 10.0  | 6120 | 0.1236          | 0.9497    | 0.9614 | 0.9555 | 0.9769   |


### Framework versions

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