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--- |
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license: apache-2.0 |
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language: |
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- bzs |
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metrics: |
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- accuracy |
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- f1 |
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pipeline_tag: text-classification |
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tags: |
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- news |
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- health |
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- classification |
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model-index: |
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- name: raphaelfontes/HealthNewsBRT |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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metrics: |
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- type: accuracy |
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value: 0.95 |
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name: Accuracy |
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verified: false |
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- type: f1 |
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value: 0.95 |
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name: F1 Score |
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verified: false |
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--- |
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# HealthNewsBRT - BERT Classification Model for Brazilian Portuguese News Articles |
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## Introduction |
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This repository contains a BERT-based classification model for categorizing news articles in Portuguese (pt-br) into two categories: Health News (LABEL_0) and Non-Health News (LABEL_1). This model is designed to help classify news articles and identify whether they pertain to health-related topics or not. |
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### Pretrained Model (BERTimbau) |
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For this project, we used the [BERTimbau model](https://huggingface.co/neuralmind/bert-base-portuguese-cased), which is a Portuguese variant of BERT fine-tuned for natural language understanding tasks. |
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## Classification report |
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| | Precision | Recall | F1-Score | Support | |
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|-----------|-----------|--------|----------|---------| |
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| LABEL_0 | 0.96 | 0.95 | 0.95 | 14000 | |
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| LABEL_1 | 0.95 | 0.96 | 0.96 | 14000 | |
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| Accuracy | 0.95 | | | 28000 | |
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| Macro Avg| 0.96 | 0.95 | 0.95 | 28000 | |
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|Weighted Avg| 0.96 | 0.95 | 0.95 | 28000 | |
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## Dataset |
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For training and evaluation, we used a dataset consisting of 28,000 labeled news articles in Portuguese. The dataset is divided as follows: |
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- **14,000 samples of Health News (LABEL_0)**: These articles are related to various health topics, such as medical discoveries, healthcare policies, and wellness. |
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- **14,000 samples of Non-Health News (LABEL_1)**: These articles cover a wide range of subjects that do not fall under the health category, including politics, sports, entertainment, and more. |
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The dataset was collected and preprocessed to ensure consistency and quality in labeling and text formatting. |
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## Data Splitting |
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To assess the model's performance, we split the dataset into training and testing subsets. We used an 80-20 split, with 80% of the data used for training and 20% for testing. This split helps us evaluate how well the model generalizes to new, unseen data. |
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## Usage |
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``` |
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from transformers import BertTokenizer, BertForSequenceClassification |
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import torch |
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# Load the pretrained model and tokenizer |
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tokenizer = BertTokenizer.from_pretrained('raphaelfontes/HealthNewsBRT') |
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model = BertForSequenceClassification.from_pretrained('raphaelfontes/HealthNewsBRT') |
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# Define a news article |
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news_article = "This is a news article in Portuguese about a health-related topic." |
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# Tokenize and encode the news article |
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inputs = tokenizer(news_article, return_tensors='pt', padding=True, truncation=True) |
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# Make predictions |
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with torch.no_grad(): |
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outputs = model(**inputs) |
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# Get predicted label |
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predicted_label = torch.argmax(outputs.logits).item() |
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# Map label to human-readable category |
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if predicted_label: |
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category = "Health News" |
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else: |
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category = "Non-Health News" |
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print(f"The article is categorized as: {category}") |
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``` |
