---
pipeline_tag: text-classification
metrics:
- accuracy
license: mit
datasets:
- mteb/twentynewsgroups-clustering
language:
- en
library_name: sklearn
---
# BERT Text Classification Model

This is a simple model for text classification using BERT.

## Usage

To use the model, you can call the `classify_text` function with a text input, and it will return the predicted class label.

```python
text = "This is a positive review."
predicted_class = classify_text(text)
print("Predicted class:", predicted_class)

from transformers import BertTokenizer, BertForSequenceClassification

# Load pre-trained BERT tokenizer and model
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')

# Define a function to classify text
def classify_text(text):
    inputs = tokenizer(text, return_tensors='pt', padding=True, truncation=True)
    outputs = model(**inputs)
    logits = outputs.logits
    probabilities = logits.softmax(dim=1)
    predicted_class = probabilities.argmax(dim=1).item()
    return predicted_class

# Example usage
text = "This is a positive review."
predicted_class = classify_text(text)
print("Predicted class:", predicted_class)