| license: mit | |
| language: | |
| - pt | |
| metrics: | |
| accuracy: | |
| Neutral: 0.99 | |
| Positive: 0.97 | |
| Negative: 0.98 | |
| base_model: neuralmind/bert-base-portuguese-cased | |
| library_name: transformers | |
| tags: | |
| - sentiment analysis | |
| - nlp | |
| - glassdoor | |
| pipeline_tag: text-classification | |
| # BERTimbau for Sentiment Analysis of Glassdoor Reviews | |
| ## Introduction | |
| This model fine-tunes [neuralmind/bert-base-portuguese-cased](https://huggingface.co./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](https://github.com/stevillis/glassdoor-reviews-analysis-nlp). | |
| ### Example Usage | |
| ```python | |
| 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}] | |
| ``` |