metadata

license: apache-2.0
base_model: distilbert/distilbert-base-multilingual-cased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: distilbert-base-multilingual-cased-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: 0.7959714100064977
          - name: Recall
            type: recall
            value: 0.7847533632286996
          - name: F1
            type: f1
            value: 0.7903225806451614
          - name: Accuracy
            type: accuracy
            value: 0.959060823521215

distilbert-base-multilingual-cased-finetuned-ner-lenerBr

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

Loss: 0.1904
Precision: 0.7960
Recall: 0.7848
F1: 0.7903
Accuracy: 0.9591

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	0.2124	0.6794	0.6842	0.6818	0.9363
0.2601	2.0	980	0.1744	0.701	0.7485	0.7239	0.9486
0.0688	3.0	1470	0.1653	0.7344	0.7598	0.7469	0.9522
0.0375	4.0	1960	0.1868	0.7764	0.7429	0.7593	0.9546
0.0229	5.0	2450	0.1844	0.7748	0.7854	0.7801	0.9560
0.0162	6.0	2940	0.2072	0.6896	0.7929	0.7377	0.9462
0.0123	7.0	3430	0.1941	0.7612	0.7704	0.7658	0.9548
0.0078	8.0	3920	0.1900	0.7701	0.7909	0.7804	0.9581
0.0068	9.0	4410	0.1884	0.8000	0.7822	0.7910	0.9593
0.0045	10.0	4900	0.1904	0.7960	0.7848	0.7903	0.9591

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1