Create README.md

README.md

@@ -0,0 +1,77 @@
+---
+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}
+}
+```