metadata

library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-cased
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: 0.7331490537954497
          - name: Recall
            type: recall
            value: 0.7922794117647058
          - name: F1
            type: f1
            value: 0.7615681943677526
          - name: Accuracy
            type: accuracy
            value: 0.9655162373585419

NER-finetuning-BETO-PRO

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

Loss: 0.1391
Precision: 0.7331
Recall: 0.7923
F1: 0.7616
Accuracy: 0.9655

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 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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.1028	1.0	1041	0.1424	0.7051	0.7603	0.7317	0.9618
0.0678	2.0	2082	0.1391	0.7331	0.7923	0.7616	0.9655

Framework versions

Transformers 4.46.2
Pytorch 2.5.1+cu121
Datasets 3.1.0
Tokenizers 0.20.3