BERTimbau-large-lener_br Open-Source Model - Free Deployment for Identifying Named Entities in Portuguese Texts

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Bertimbau Large Lener Br

Developed by Luciano

A Portuguese named entity recognition model fine-tuned on the lener_br dataset based on the BERTimbau large model

Sequence Labeling

Transformers

OtherOpen Source License:MIT #Portuguese NER #Legal Entity Recognition #High-precision BERT

Downloads 52

Release Time : 3/2/2022

Model Overview

This model is a BERT model optimized for Portuguese text named entity recognition tasks, performing exceptionally well on the lener_br dataset and suitable for entity recognition tasks in the legal domain.

Model Features

High-precision Portuguese NER

Achieves 98.41% accuracy and 98.91% F1 score on the lener_br test set

Based on BERTimbau large model

Uses neuralmind/bert-large-portuguese-cased as the base model, optimized for Portuguese

Legal domain optimization

Fine-tuned on the lener_br legal text dataset, suitable for legal document processing

Model Capabilities

Portuguese text processing

Named Entity Recognition

Legal text analysis

Use Cases

Legal document processing

Legal document entity extraction

Identify entities such as persons, organizations, and locations from Portuguese legal documents

Test set F1 score reaches 98.91%

Contract analysis

Automatically identify key entity information in contracts

🚀 bertimbau-large-lener_br

This model is a fine - tuned version of neuralmind/bert-large-portuguese-cased on the lener_br dataset. It offers high - performance results in token classification tasks, providing accurate predictions on the lener_br dataset.

📚 Documentation

Model Information

This model is a fine - tuned version of neuralmind/bert-large-portuguese-cased on the lener_br dataset. It achieves the following results on the evaluation set:

Loss: 0.1271
Precision: 0.8965
Recall: 0.9198
F1: 0.9080
Accuracy: 0.9801

Property	Details
Model Type	Fine - tuned version of neuralmind/bert-large-portuguese-cased
Training Data	lener_br dataset

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: 4
eval_batch_size: 4
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
0.0674	1.0	1957	0.1349	0.7617	0.8710	0.8127	0.9594
0.0443	2.0	3914	0.1867	0.6862	0.9194	0.7858	0.9575
0.0283	3.0	5871	0.1185	0.8206	0.8766	0.8477	0.9678
0.0226	4.0	7828	0.1405	0.8072	0.8978	0.8501	0.9708
0.0141	5.0	9785	0.1898	0.7224	0.9194	0.8090	0.9629
0.01	6.0	11742	0.1655	0.9062	0.8856	0.8958	0.9741
0.012	7.0	13699	0.1271	0.8965	0.9198	0.9080	0.9801
0.0091	8.0	15656	0.1919	0.8890	0.8886	0.8888	0.9719
0.0042	9.0	17613	0.1725	0.8977	0.8985	0.8981	0.9744
0.0043	10.0	19570	0.1530	0.8878	0.9034	0.8955	0.9761
0.0042	11.0	21527	0.1635	0.8792	0.9108	0.8947	0.9774
0.0033	12.0	23484	0.2009	0.8155	0.9138	0.8619	0.9719
0.0008	13.0	25441	0.1766	0.8737	0.9135	0.8932	0.9755
0.0005	14.0	27398	0.1868	0.8616	0.9129	0.8865	0.9743
0.0014	15.0	29355	0.1910	0.8694	0.9101	0.8893	0.9746

Framework versions

Transformers 4.8.2
Pytorch 1.9.0+cu102
Datasets 1.9.0
Tokenizers 0.10.3

📄 License

This model is released under the MIT license.

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