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: 0.7845931433292028
          - name: Recall
            type: recall
            value: 0.7810444078947368
          - name: F1
            type: f1
            value: 0.7828147537605605
          - name: Accuracy
            type: accuracy
            value: 0.9671762427683093

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: 0.1555
Precision: 0.7846
Recall: 0.7810
F1: 0.7828
Accuracy: 0.9672

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: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	490	0.1861	0.6380	0.6661	0.6518	0.9446
0.2629	2.0	980	0.1618	0.7063	0.7303	0.7181	0.9537
0.0756	3.0	1470	0.1299	0.7299	0.8010	0.7638	0.9645
0.0443	4.0	1960	0.1422	0.7634	0.7708	0.7671	0.9643
0.0279	5.0	2450	0.1508	0.7870	0.7679	0.7773	0.9648
0.0203	6.0	2940	0.1457	0.7693	0.7815	0.7753	0.9681
0.0143	7.0	3430	0.1508	0.7767	0.7714	0.7740	0.9663
0.0105	8.0	3920	0.1537	0.7812	0.7669	0.7739	0.9671
0.0085	9.0	4410	0.1564	0.7809	0.7681	0.7745	0.9669
0.0064	10.0	4900	0.1555	0.7846	0.7810	0.7828	0.9672

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1