Text Classification
Transformers
PyTorch
Safetensors
Portuguese
bert
sentiment analysis
nlp
glassdoor
text-embeddings-inference
Instructions to use stevillis/bertimbau-finetuned-glassdoor-reviews with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stevillis/bertimbau-finetuned-glassdoor-reviews with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="stevillis/bertimbau-finetuned-glassdoor-reviews")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("stevillis/bertimbau-finetuned-glassdoor-reviews") model = AutoModelForSequenceClassification.from_pretrained("stevillis/bertimbau-finetuned-glassdoor-reviews") - Notebooks
- Google Colab
- Kaggle
BERTimbau for Sentiment Analysis of Glassdoor Reviews
Introduction
This model fine-tunes neuralmind/bert-base-portuguese-cased for sentiment analysis of Glassdoor reviews about IT companies in Cuiabá.
The dataset used to train the model consists of 2,532 reviews sourced from Glassdoor.
For more detail about the project, follow my GitHub.
Example Usage
from transformers import pipeline
pipe = pipeline("text-classification", model="stevillis/bertimbau-finetuned-glassdoor-reviews")
result = pipe("Empresa boa para trabalhar")
print(result) # Expected output: [{'label': 'positive', 'score': 0.9993522763252258}]
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Model tree for stevillis/bertimbau-finetuned-glassdoor-reviews
Base model
neuralmind/bert-base-portuguese-cased