transformers safetensors bert token-classification generated_from_trainer dataset:conll2002 base_model:NazaGara/NER-fine-tuned-BETO base_model:finetune:NazaGara/NER-fine-tuned-BETO license:cc-by-4.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: NazaGara/NER-fine-tuned-BETO
datasets:
- conll2002
license: cc-by-4.0
metrics:
- precision
- recall
- f1
- accuracy
tags:
- generated_from_trainer
model-index:
- name: beto-base-cased-finetuned-conll2002
  results:
  - task:
      type: token-classification
      name: Token Classification
    dataset:
      name: conll2002
      type: conll2002
      config: es
      split: validation
      args: es
    metrics:
    - type: precision
      value: 0.8607856650585803
      name: Precision
    - type: recall
      value: 0.8609834558823529
      name: Recall
    - type: f1
      value: 0.860884549109707
      name: F1
    - type: accuracy
      value: 0.9786860155319993
      name: Accuracy
---

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# beto-base-cased-finetuned-conll2002

This model is a fine-tuned version of [NazaGara/NER-fine-tuned-BETO](https://huggingface.co/NazaGara/NER-fine-tuned-BETO) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1281
- Precision: 0.8608
- Recall: 0.8610
- F1: 0.8609
- Accuracy: 0.9787

## Model description

The model described here is a fine-tuned version of the BETO (BERT-based Spanish language model) for Named Entity Recognition (NER) tasks, 
trained on the CoNLL-2002 dataset. BETO is a pre-trained language model specifically designed for the Spanish language, based on the BERT architecture.
By fine-tuning BETO on the CoNLL-2002 dataset, the model has been adapted to recognize and classify named entities such as persons, organizations, 
locations, and other miscellaneous entities within Spanish text. The fine-tuning process involves adjusting the pre-trained model weights to better 
fit the specific task of NER, thereby improving its performance and accuracy on Spanish text.

## Intended uses & limitations

More information needed

## Training and evaluation data

The training was performed using a GPU with 22.5 GB of RAM, 53 GB of system RAM, and 200 GB of disk space. 
This setup ensured efficient handling of the large dataset and the computational demands of fine-tuning the model.

## 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: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.019         | 1.0   | 1041 | 0.1095          | 0.8581    | 0.8628 | 0.8604 | 0.9790   |
| 0.018         | 2.0   | 2082 | 0.1121          | 0.8461    | 0.8589 | 0.8525 | 0.9783   |
| 0.0133        | 3.0   | 3123 | 0.1281          | 0.8608    | 0.8610 | 0.8609 | 0.9787   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
