metadata

license: apache-2.0
base_model: bert-base-cased
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-cased-finetuned-conll2002
    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: 0.8175829168559745
          - name: Recall
            type: recall
            value: 0.8269761029411765
          - name: F1
            type: f1
            value: 0.8222526844870915
          - name: Accuracy
            type: accuracy
            value: 0.9739999622092474

bert-base-cased-finetuned-conll2002

This model is a fine-tuned version of bert-base-cased on the conll2002 dataset. It achieves the following results on the evaluation set:

Loss: 0.1670
Precision: 0.8176
Recall: 0.8270
F1: 0.8223
Accuracy: 0.9740

Model description

The model described here is a fine-tuned version of the BERT (Bidirectional Encoder Representations from Transformers) base cased model for Named Entity Recognition (NER) tasks, trained on the CoNLL-2002 dataset. BERT is a pre-trained language model based on the transformer architecture, designed to understand and process text by considering the context of each word from both directions (left-to-right and right-to-left).

By fine-tuning the BERT base cased model on the CoNLL-2002 dataset, this 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
0.0248	1.0	1041	0.1439	0.8066	0.8155	0.8110	0.9732
0.0141	2.0	2082	0.1569	0.8108	0.8182	0.8145	0.9728
0.0109	3.0	3123	0.1670	0.8176	0.8270	0.8223	0.9740

Framework versions

Transformers 4.41.2
Pytorch 2.3.0+cu121
Datasets 2.19.2
Tokenizers 0.19.1