reviews sentiment analysis

Introduction
The Reviews Sentiment Analysis project utilizes a fine-tuned DistilBERT model to perform sentiment analysis on English-language product reviews. This model is designed to classify text, specifically focusing on understanding sentiment from user-generated product review content.

Architecture
The model architecture is based on DistilBERT, a smaller and faster variant of BERT that retains high performance. The model has been fine-tuned specifically for the task of sentiment analysis on a dataset of generated English reviews.

Training
This model was trained using a dataset of generated product reviews, focusing on the English language. The training process involved fine-tuning DistilBERT to enhance its ability to identify and classify sentiments expressed in text data.

Guide: Running Locally
To run the sentiment analysis model locally, follow these steps:

1. Clone the repository to your local machine.
2. Ensure you have Python and PyTorch installed.
3. Install the Hugging Face Transformers library.
4. Load the model using the Transformers library and input your text data for sentiment analysis.

For improved performance, especially on large datasets, consider using cloud GPUs such as those available on Amazon SageMaker, which has a pre-prepared notebook in the repository's 'code' subfolder.

License
The specific license governing the use and distribution of this model and associated code is not detailed in the provided information. Users should refer to the repository or contact the author for licensing details.