lfcc / bert-portuguese-ner

bert-portuguese-ner-archive

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

• Loss: 0.1140
• Precision: 0.9147
• Recall: 0.9483
• F1: 0.9312
• Accuracy: 0.9700

Model description

This model was fine-tunned on token classification task (NER) on Portuguese archival documents. The annotated labels are: Date, Profession, Person, Place, Organization

Datasets

All the training and evaluation data is available at: http://ner.epl.di.uminho.pt/

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: 4

Training results

Framework versions

• Transformers 4.10.0.dev0
• Pytorch 1.9.0+cu111
• Datasets 1.10.2
• Tokenizers 0.10.3

Citation

@Article{make4010003,
AUTHOR = {Cunha, Luís Filipe and Ramalho, José Carlos},
TITLE = {NER in Archival Finding Aids: Extended},
JOURNAL = {Machine Learning and Knowledge Extraction},
VOLUME = {4},
YEAR = {2022},
NUMBER = {1},
PAGES = {42--65},
URL = {https://www.mdpi.com/2504-4990/4/1/3},
ISSN = {2504-4990},
ABSTRACT = {The amount of information preserved in Portuguese archives has increased over the years. These documents represent a national heritage of high importance, as they portray the country’s history. Currently, most Portuguese archives have made their finding aids available to the public in digital format, however, these data do not have any annotation, so it is not always easy to analyze their content. In this work, Named Entity Recognition solutions were created that allow the identification and classification of several named entities from the archival finding aids. These named entities translate into crucial information about their context and, with high confidence results, they can be used for several purposes, for example, the creation of smart browsing tools by using entity linking and record linking techniques. In order to achieve high result scores, we annotated several corpora to train our own Machine Learning algorithms in this context domain. We also used different architectures, such as CNNs, LSTMs, and Maximum Entropy models. Finally, all the created datasets and ML models were made available to the public with a developed web platform, NER@DI.},
DOI = {10.3390/make4010003}
}