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--- |
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language: pt |
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license: apache-2.0 |
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widget: |
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- text: "O futuro de DI caiu 20 bps nesta manhã" |
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example_title: "Example 1" |
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- text: "O Nubank decidiu cortar a faixa de preço da oferta pública inicial (IPO) após revés no humor dos mercados internacionais com as fintechs." |
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example_title: "Example 2" |
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- text: "O Ibovespa acompanha correção do mercado e fecha com alta moderada" |
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example_title: "Example 3" |
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--- |
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# FinBERT-PT-BR : Financial BERT PT BR |
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FinBERT-PT-BR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. |
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The model was trained in two main stages: language modeling and sentiment modeling. In the first stage, a language model was trained with more than 1.4 million texts of financial news in Portuguese. |
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From this first training, it was possible to build a sentiment classifier with few labeled texts (500) that presented a satisfactory convergence. |
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At the end of the work, a comparative analysis with other models and the possible applications of the developed model are presented. |
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In the comparative analysis, it was possible to observe that the developed model presented better results than the current models in the state of the art. |
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Among the applications, it was demonstrated that the model can be used to build sentiment indices, investment strategies and macroeconomic data analysis, such as inflation. |
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## Applications |
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### Sentiment Index |
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## Usage |
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#### BertForSequenceClassification |
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```python |
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from transformers import AutoTokenizer, BertForSequenceClassification |
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import numpy as np |
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pred_mapper = { |
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0: "POSITIVE", |
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1: "NEGATIVE", |
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2: "NEUTRAL" |
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} |
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tokenizer = AutoTokenizer.from_pretrained("lucas-leme/FinBERT-PT-BR") |
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finbertptbr = BertForSequenceClassification.from_pretrained("lucas-leme/FinBERT-PT-BR") |
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tokens = tokenizer(["Hoje a bolsa caiu", "Hoje a bolsa subiu"], return_tensors="pt", |
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padding=True, truncation=True, max_length=512) |
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finbertptbr_outputs = finbertptbr(**tokens) |
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preds = [pred_mapper[np.argmax(pred)] for pred in finbertptbr_outputs.logits.cpu().detach().numpy()] |
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``` |
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#### Pipeline |
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```python |
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from transformers import ( |
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AutoTokenizer, |
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BertForSequenceClassification, |
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pipeline, |
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) |
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finbert_pt_br_tokenizer = AutoTokenizer.from_pretrained("lucas-leme/FinBERT-PT-BR") |
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finbert_pt_br_model = BertForSequenceClassification.from_pretrained("lucas-leme/FinBERT-PT-BR") |
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finbert_pt_br_pipeline = pipeline(task='text-classification', model=finbert_pt_br_model, tokenizer=finbert_pt_br_tokenizer) |
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finbert_pt_br_pipeline(['Hoje a bolsa caiu', 'Hoje a bolsa subiu']) |
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``` |
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## Author |
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- [Lucas Leme](https://www.linkedin.com/in/lucas-leme-santos/) - lucaslssantos99@gmail.com |
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## Citation |
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```latex |
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@inproceedings{santos2023finbert, |
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title={FinBERT-PT-BR: An{\'a}lise de Sentimentos de Textos em Portugu{\^e}s do Mercado Financeiro}, |
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author={Santos, Lucas L and Bianchi, Reinaldo AC and Costa, Anna HR}, |
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booktitle={Anais do II Brazilian Workshop on Artificial Intelligence in Finance}, |
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pages={144--155}, |
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year={2023}, |
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organization={SBC} |
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} |
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``` |
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## Paper |
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- Paper: [FinBERT-PT-BR: Sentiment Analysis of Texts in Portuguese from the Financial Market](https://sol.sbc.org.br/index.php/bwaif/article/view/24960) |
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- Undergraduate thesis: [FinBERT-PT-BR: Análise de sentimentos de textos em português referentes ao mercado financeiro](https://pcs.usp.br/pcspf/wp-content/uploads/sites/8/2022/12/Monografia_PCS3860_COOP_2022_Grupo_C12.pdf) |
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