Distilbert Imdb Positive

🚀 DistilBERT Model for Positive IMDb Reviews

A DistilBERT model trained specifically on positive reviews from IMDb, which can be used for sentiment analysis and related natural language processing tasks.

🚀 Quick Start

This DistilBERT model is trained on positive IMDb reviews. It can be used to quickly analyze the sentiment of movie reviews and other text data.

# Here you can add actual code examples if there are any in the original more detailed version. For now, this is just a placeholder.
# Assume we have a simple sentiment analysis code using the model
from transformers import AutoTokenizer, AutoModelForSequenceClassification

tokenizer = AutoTokenizer.from_pretrained('your-distilbert-model-name')
model = AutoModelForSequenceClassification.from_pretrained('your-distilbert-model-name')

text = "This movie is really amazing!"
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)

## ✨ Features
- **Trained on Positive Data**: The model is trained on positive IMDb reviews, which makes it more accurate in identifying positive sentiment.
- **Based on DistilBERT**: Leveraging the efficient DistilBERT architecture, it provides a good balance between performance and speed.

```markdown
## 📦 Installation
If you want to use this model, you need to install the `transformers` library. You can install it using the following command:
```bash
pip install transformers