README.md

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-finetuned-ner-1
    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: 0.7836761778367618
          - name: Recall
            type: recall
            value: 0.8141084558823529
          - name: F1
            type: f1
            value: 0.7986025019722754
          - name: Accuracy
            type: accuracy
            value: 0.9686511248714017

bert-finetuned-ner-1

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: 0.2526
• Precision: 0.7837
• Recall: 0.8141
• F1: 0.7986
• Accuracy: 0.9687

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.003	1.0	1041	0.2526	0.7837	0.8141	0.7986	0.9687

Framework versions

• Transformers 4.41.0
• Pytorch 2.3.0+cu121
• Datasets 2.19.1
• Tokenizers 0.19.1