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---
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
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-multilingual-uncased-finetuned-ner-lenerBR
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/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
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