---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NER-finetuning-BETO-PRO
  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: 0.7017726798748697
    - name: Recall
      type: recall
      value: 0.7732077205882353
    - name: F1
      type: f1
      value: 0.7357603585875151
    - name: Accuracy
      type: accuracy
      value: 0.9536327652922068
---

<!-- 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-PRO

This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1981
- Precision: 0.7018
- Recall: 0.7732
- F1: 0.7358
- Accuracy: 0.9536

## 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: Use OptimizerNames.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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1941        | 1.0   | 1041 | 0.1965          | 0.6201    | 0.6836 | 0.6503 | 0.9422   |
| 0.1276        | 2.0   | 2082 | 0.1843          | 0.6666    | 0.7387 | 0.7008 | 0.9487   |
| 0.0885        | 3.0   | 3123 | 0.1760          | 0.7056    | 0.7601 | 0.7319 | 0.9538   |
| 0.0623        | 4.0   | 4164 | 0.1856          | 0.6982    | 0.7670 | 0.7310 | 0.9532   |
| 0.0485        | 5.0   | 5205 | 0.1981          | 0.7018    | 0.7732 | 0.7358 | 0.9536   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1