metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - conll2002
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-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: 0.6434803981811479
          - name: Recall
            type: recall
            value: 0.6306913996627319
          - name: F1
            type: f1
            value: 0.6370217166494313
          - name: Accuracy
            type: accuracy
            value: 0.9318683268102846

distilbert-base-uncased-finetuned-ner

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

Loss: 0.2428
Precision: 0.6435
Recall: 0.6307
F1: 0.6370
Accuracy: 0.9319

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: 2

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.3631	1.0	521	0.2718	0.5847	0.5858	0.5852	0.9219
0.2056	2.0	1042	0.2428	0.6435	0.6307	0.6370	0.9319

Framework versions

Transformers 4.44.2
Pytorch 2.5.0+cu121
Datasets 3.0.2
Tokenizers 0.19.1