metadata

license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-uncased-finetuned-ner-lenerBr
    results:
      - task:
          name: Token Classification
          type: token-classification
        dataset:
          name: lener_br
          type: lener_br
          config: lener_br
          split: validation
          args: lener_br
        metrics:
          - name: Precision
            type: precision
            value: 0.7477750426055672
          - name: Recall
            type: recall
            value: 0.8118832236842105
          - name: F1
            type: f1
            value: 0.7785115820601283
          - name: Accuracy
            type: accuracy
            value: 0.9644699967525048

distilbert-base-uncased-finetuned-ner-lenerBr

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

Loss: 0.1546
Precision: 0.7478
Recall: 0.8119
F1: 0.7785
Accuracy: 0.9645

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: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	490	0.2131	0.6201	0.6604	0.6396	0.9359
0.264	2.0	980	0.1828	0.7004	0.7504	0.7246	0.9508
0.0776	3.0	1470	0.1564	0.6582	0.8137	0.7278	0.9537
0.0437	4.0	1960	0.1644	0.7485	0.7623	0.7553	0.9573
0.0288	5.0	2450	0.1555	0.7620	0.7662	0.7641	0.9614
0.0208	6.0	2940	0.1874	0.7530	0.7759	0.7643	0.9550
0.0143	7.0	3430	0.1546	0.7478	0.8119	0.7785	0.9645
0.0117	8.0	3920	0.1717	0.7014	0.7677	0.7330	0.9592
0.0102	9.0	4410	0.1884	0.7734	0.7714	0.7724	0.9613

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1