Instructions to use turing-usp/FinBertPTBR with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use turing-usp/FinBertPTBR with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="turing-usp/FinBertPTBR")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR") model = AutoModelForSequenceClassification.from_pretrained("turing-usp/FinBertPTBR", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from turing-usp/FinBertPTBR: direct link, hf CLI and curl.
- Browser
- Download file 1.34 kB
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https://huggingface.co/turing-usp/FinBertPTBR/resolve/main/README.md
- Command line
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hf download hf://turing-usp/FinBertPTBR/README.md
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curl -L -o README.md https://huggingface.co/turing-usp/FinBertPTBR/resolve/main/README.md
1.34 kB
metadata
language: pt
license: apache-2.0
widget:
- text: O futuro de DI caiu 20 bps nesta manhã
example_title: Example 1
- text: >-
O Nubank decidiu cortar a faixa de preço da oferta pública inicial (IPO)
após revés no humor dos mercados internacionais com as fintechs.
example_title: Example 2
- text: O Ibovespa acompanha correção do mercado e fecha com alta moderada
example_title: Example 3
FinBertPTBR : Financial Bert PT BR (Depreciated model)
Info Newer version available on https://huggingface.co/lucas-leme/FinBERT-PT-BR
FinBertPTBR is a pre-trained NLP model to analyze sentiment of Brazilian Portuguese financial texts. It is built by further training the BERTimbau language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification.
Usage
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("turing-usp/FinBertPTBR")
model = AutoModel.from_pretrained("turing-usp/FinBertPTBR")