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raulgdp
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bert-finetuned-ner

Token Classification
Transformers
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roberta
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bert-finetuned-ner
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---

library_name: transformers
license: apache-2.0
base_model: BSC-LT/roberta-base-bne-capitel-ner
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
  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: 0.8599099099099099
    - name: Recall
      type: recall
      value: 0.8772977941176471
    - name: F1
      type: f1
      value: 0.8685168334849864
    - name: Accuracy
      type: accuracy
      value: 0.978701639744725
---


<!-- 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. -->

# bert-finetuned-ner

This model is a fine-tuned version of [BSC-LT/roberta-base-bne-capitel-ner](https://huggingface.co/BSC-LT/roberta-base-bne-capitel-ner) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0950
- Precision: 0.8599
- Recall: 0.8773
- F1: 0.8685
- Accuracy: 0.9787

## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1045        | 1.0   | 521  | 0.0932          | 0.8593    | 0.8704 | 0.8648 | 0.9764   |
| 0.0343        | 2.0   | 1042 | 0.0870          | 0.8616    | 0.8757 | 0.8686 | 0.9781   |
| 0.019         | 3.0   | 1563 | 0.0950          | 0.8599    | 0.8773 | 0.8685 | 0.9787   |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.0
- Datasets 2.20.0
- Tokenizers 0.20.0