---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2002
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner-cfv
  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: 0.807683615819209
    - name: Recall
      type: recall
      value: 0.8212316176470589
    - name: F1
      type: f1
      value: 0.8144012760624361
    - name: Accuracy
      type: accuracy
      value: 0.974075543714453
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# bert-finetuned-ner-cfv

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2002 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1851
- Precision: 0.8077
- Recall: 0.8212
- F1: 0.8144
- Accuracy: 0.9741

## 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: 4e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 17

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 347  | 0.1278          | 0.7284    | 0.7475 | 0.7378 | 0.9646   |
| 0.1176        | 2.0   | 694  | 0.1212          | 0.7509    | 0.7806 | 0.7654 | 0.9681   |
| 0.0453        | 3.0   | 1041 | 0.1156          | 0.8062    | 0.8116 | 0.8089 | 0.9730   |
| 0.0453        | 4.0   | 1388 | 0.1270          | 0.8081    | 0.8031 | 0.8056 | 0.9720   |
| 0.0233        | 5.0   | 1735 | 0.1298          | 0.8145    | 0.8231 | 0.8187 | 0.9746   |
| 0.0145        | 6.0   | 2082 | 0.1431          | 0.7950    | 0.8091 | 0.8020 | 0.9728   |
| 0.0145        | 7.0   | 2429 | 0.1501          | 0.8103    | 0.8166 | 0.8135 | 0.9734   |
| 0.009         | 8.0   | 2776 | 0.1553          | 0.8118    | 0.8157 | 0.8138 | 0.9738   |
| 0.0061        | 9.0   | 3123 | 0.1572          | 0.7891    | 0.8084 | 0.7986 | 0.9720   |
| 0.0061        | 10.0  | 3470 | 0.1589          | 0.8142    | 0.8196 | 0.8169 | 0.9739   |
| 0.005         | 11.0  | 3817 | 0.1671          | 0.8092    | 0.8148 | 0.8120 | 0.9733   |
| 0.0032        | 12.0  | 4164 | 0.1716          | 0.8066    | 0.8139 | 0.8102 | 0.9733   |
| 0.0031        | 13.0  | 4511 | 0.1767          | 0.8025    | 0.8169 | 0.8096 | 0.9731   |
| 0.0031        | 14.0  | 4858 | 0.1756          | 0.8096    | 0.8217 | 0.8156 | 0.9741   |
| 0.0023        | 15.0  | 5205 | 0.1845          | 0.8109    | 0.8157 | 0.8133 | 0.9739   |
| 0.0018        | 16.0  | 5552 | 0.1850          | 0.8090    | 0.8203 | 0.8146 | 0.9739   |
| 0.0018        | 17.0  | 5899 | 0.1851          | 0.8077    | 0.8212 | 0.8144 | 0.9741   |


### Framework versions

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