---
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-fine-tuned-BETO-finetuned-ner
  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: 0.9501046193465315
    - name: Recall
      type: recall
      value: 0.9622364703325365
    - name: F1
      type: f1
      value: 0.9561320627378993
    - name: Accuracy
      type: accuracy
      value: 0.9770695187165775
---

<!-- 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-fine-tuned-BETO-finetuned-ner

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: 0.1398
- Precision: 0.9501
- Recall: 0.9622
- F1: 0.9561
- Accuracy: 0.9771

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1451        | 1.0   | 1224  | 0.1089          | 0.9356    | 0.9579 | 0.9466 | 0.9726   |
| 0.0963        | 2.0   | 2448  | 0.1152          | 0.9362    | 0.9701 | 0.9528 | 0.9751   |
| 0.0658        | 3.0   | 3672  | 0.1096          | 0.9485    | 0.9620 | 0.9552 | 0.9763   |
| 0.0481        | 4.0   | 4896  | 0.1050          | 0.9516    | 0.9683 | 0.9598 | 0.9785   |
| 0.0389        | 5.0   | 6120  | 0.1098          | 0.9535    | 0.9636 | 0.9585 | 0.9779   |
| 0.0348        | 6.0   | 7344  | 0.1205          | 0.9502    | 0.9631 | 0.9566 | 0.9772   |
| 0.0274        | 7.0   | 8568  | 0.1250          | 0.9512    | 0.9629 | 0.9570 | 0.9776   |
| 0.024         | 8.0   | 9792  | 0.1359          | 0.9499    | 0.9624 | 0.9561 | 0.9770   |
| 0.0188        | 9.0   | 11016 | 0.1350          | 0.9507    | 0.9626 | 0.9566 | 0.9772   |
| 0.0186        | 10.0  | 12240 | 0.1398          | 0.9501    | 0.9622 | 0.9561 | 0.9771   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 3.0.1
- Tokenizers 0.20.0