README.md

metadata

library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: ner_bert_model
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: test
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.828094932649134
          - name: Recall
            type: recall
            value: 0.8532716457369465
          - name: F1
            type: f1
            value: 0.8404947916666666
          - name: Accuracy
            type: accuracy
            value: 0.9840912469998513

ner_bert_model

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

• Loss: 0.0922
• Precision: 0.8281
• Recall: 0.8533
• F1: 0.8405
• Accuracy: 0.9841

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: Use OptimizerNames.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: 20

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	490	0.0971	0.6373	0.7607	0.6936	0.9706
0.2449	2.0	980	0.0820	0.6916	0.8063	0.7446	0.9760
0.0634	3.0	1470	0.0750	0.7106	0.8473	0.7730	0.9778
0.0352	4.0	1960	0.0707	0.7690	0.8361	0.8011	0.9799
0.0226	5.0	2450	0.0812	0.8063	0.8394	0.8225	0.9821
0.0157	6.0	2940	0.0779	0.7931	0.8486	0.8199	0.9826
0.0105	7.0	3430	0.0958	0.7314	0.8586	0.7899	0.9779
0.0082	8.0	3920	0.0810	0.8158	0.8460	0.8306	0.9829
0.0067	9.0	4410	0.0830	0.8190	0.8526	0.8355	0.9832
0.0054	10.0	4900	0.0810	0.8165	0.8500	0.8329	0.9833
0.0051	11.0	5390	0.0855	0.8180	0.8493	0.8333	0.9832
0.0037	12.0	5880	0.0862	0.8195	0.8519	0.8354	0.9841
0.0034	13.0	6370	0.0867	0.8165	0.8586	0.8370	0.9833
0.0027	14.0	6860	0.0922	0.8214	0.8420	0.8316	0.9832
0.0024	15.0	7350	0.0910	0.8147	0.8486	0.8313	0.9836
0.002	16.0	7840	0.0928	0.8191	0.8559	0.8371	0.9840
0.0018	17.0	8330	0.0928	0.8119	0.8559	0.8333	0.9834
0.0017	18.0	8820	0.0920	0.8228	0.8592	0.8406	0.9838
0.0015	19.0	9310	0.0919	0.8242	0.8553	0.8394	0.9837
0.0011	20.0	9800	0.0922	0.8281	0.8533	0.8405	0.9841

Framework versions

• Transformers 4.52.4
• Pytorch 2.6.0+cu124
• Datasets 3.6.0
• Tokenizers 0.21.1