README.md

---
license: mit
datasets:
- Aditya1010/17k-hotel-reviews-dataset
metrics:
- accuracy
base_model:
- distilbert/distilbert-base-uncased
pipeline_tag: text-classification
library_name: transformers
tags:
- Sentiment Analysis
- DistilBERT
- Text Classification
- Hotel Reviews
---
# Hotel Review Classifier

This model is a sentiment classification model for hotel reviews, trained to predict whether a review is **positive** or **negative**. The model was fine-tuned using the `distilbert-base-uncased` model architecture, based on the [DistilBERT model](https://huggingface.co/distilbert/distilbert-base-uncased) from Hugging Face, and trained on the [17k Hotel Reviews Dataset](https://huggingface.co/datasets/Aditya1010/17k-hotel-reviews-dataset).

## Model Details
- **Model Type**: DistilBERT-based model for sequence classification
- **Model Architecture**: `distilbert-base-uncased`
- **Number of Parameters**: Approximately 66M parameters
- **Training Dataset**: The model was trained on the `17k-hotel-reviews-dataset`, which contains 17,000 hotel reviews with labels for sentiment (positive/negative).
- **Fine-Tuning Task**: Sentiment analysis for hotel reviews (positive or negative sentiment)

## Training Data
- **Dataset**: [17k Hotel Reviews Dataset](https://huggingface.co/datasets/Aditya1010/17k-hotel-reviews-dataset)
- **Data Description**: The dataset consists of 17,000 hotel reviews, each labeled with a sentiment (positive/negative).
- **Preprocessing**: The dataset was preprocessed by cleaning the reviews to remove unwanted characters and URLs.

## Training Details
- **Training Framework**: Hugging Face Transformers and PyTorch
- **Learning Rate**: 2e-5
- **Epochs**: 3
- **Batch Size**: 16
- **Optimizer**: AdamW
- **Training Time**: Approximately 2 hours on a GPU

## Usage
To use the model for inference, you can use the following code:

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch

# Load the fine-tuned model and tokenizer
model = AutoModelForSequenceClassification.from_pretrained("kmack/HotelReviewClassifier")
tokenizer = AutoTokenizer.from_pretrained("kmack/HotelReviewClassifier")

# Example review for prediction
review = "This is the best hotel I've ever stayed in!"

# Tokenize the input text
inputs = tokenizer(review, return_tensors="pt", padding=True, truncation=True)

# Get predictions
with torch.no_grad():
    outputs = model(**inputs)

# Get the predicted label (0 for negative, 1 for positive)
prediction = torch.argmax(outputs.logits, dim=-1)
print(f"Predicted sentiment: {'Positive' if prediction == 1 else 'Negative'}")
```

## Citation

If you use this model in your research, please cite the following:

```@misc{hotel_review_classifier,
  author = {Kmack},
  title = {Hotel Review Classifier},
  year = {2024},
  url = {https://huggingface.co/kmack/HotelReviewClassifier}
}
```