metadata

license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
  - generated_from_trainer
datasets:
  - lener_br
metrics:
  - precision
  - recall
  - f1
  - accuracy
model-index:
  - name: bert-base-multilingual-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: 0.8678256070640177
          - name: Recall
            type: recall
            value: 0.8758006126427179
          - name: F1
            type: f1
            value: 0.8717948717948718
          - name: Accuracy
            type: accuracy
            value: 0.9706569722150091

bert-base-multilingual-uncased-finetuned-ner-lenerBR

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

Loss: 0.1568
Precision: 0.8678
Recall: 0.8758
F1: 0.8718
Accuracy: 0.9707

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: 32
eval_batch_size: 32
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	245	0.1819	0.7691	0.8118	0.7899	0.9585
No log	2.0	490	0.1487	0.7383	0.8098	0.7724	0.9586
0.1325	3.0	735	0.1532	0.8662	0.8777	0.8719	0.9683
0.1325	4.0	980	0.1470	0.8770	0.8800	0.8785	0.9698
0.0233	5.0	1225	0.1155	0.8493	0.8839	0.8663	0.9750
0.0233	6.0	1470	0.1727	0.8874	0.8822	0.8848	0.9701
0.0126	7.0	1715	0.1698	0.8890	0.8853	0.8871	0.9710
0.0126	8.0	1960	0.1687	0.8651	0.8783	0.8716	0.9702
0.0076	9.0	2205	0.1593	0.8077	0.8797	0.8422	0.9668
0.0076	10.0	2450	0.1568	0.8678	0.8758	0.8718	0.9707

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1