
	
		
	
	
		Fine-Tuned Distilled BERT Model for Sentiment Analysis on Amazon Reviews
	


This repository contains a fine-tuned Distilled BERT (Bidirectional Encoder Representations from Transformers) model for sentiment analysis on Amazon reviews. The base model used is the distilbert-base-uncased model, which is a smaller and faster version of the original BERT model, pre-trained on a large corpus of text data.


	
		
	
	
		Model Details
	


The fine-tuned Distilled BERT model is based on the transformers library by Hugging Face, which provides pre-trained language models that can be fine-tuned on specific tasks. The model architecture used in this repository is the distilbert-base-uncased model, which is a lightweight version of the BERT model with uncased text input. The model is fine-tuned using a binary classification approach, where the goal is to predict whether a given Amazon review is positive or negative based on the text of the review.


	
		
	
	
		Dataset
	


The model is trained on a dataset of Amazon reviews, which is preprocessed to remove any personally identifiable information (PII) and other irrelevant information. The dataset is split into training, validation, and test sets, with an 80/10/10 split ratio. The training set is used for fine-tuning the model, the validation set is used for hyperparameter tuning, and the test set is used for evaluating the model's performance.


	
		
	
	
		Deployment on Hugging Face
	


The fine-tuned Distilled BERT model is deployed on Hugging Face's model hub, a platform for hosting and sharing NLP models. The model is available for download and inference through the Hugging Face Transformers library. To use the deployed model, you need to install the transformers library by Hugging Face and load the model using the provided Hugging Face model name or model checkpoint URL.


	
		
	
	
		Here's an example code snippet to load and use the fine-tuned Distilled BERT model for sentiment analysis from Hugging Face:
	



	
		
	
	
		Evaluation Metrics
	


The performance of the fine-tuned Distilled BERT model can be evaluated using various evaluation metrics, such as accuracy, precision, recall, and F1 score. These metrics can be calculated on the test set of the Amazon reviews dataset to assess the model's accuracy and effectiveness in predicting sentiment.


	
		
	
	
		Conclusion
	


The fine-tuned Distilled BERT model in this repository, deployed on Hugging Face, provides an accurate and efficient way to perform sentiment analysis on Amazon reviews. It can be used in various applications, such as customer feedback analysis, market research, and sentiment monitoring. Please refer to the Hugging Face Transformers documentation for more details on how to use and fine-tune the Distilled BERT model.



This model is a fine-tuned Distilled BERT model for sentiment analysis on Amazon reviews. It is based on the distilbert-base-uncased model and uses a binary classification approach to predict whether a review is positive or negative. The model is trained on a dataset of Amazon reviews and evaluated using metrics such as accuracy, precision, recall, and F1 score. It is deployed on Hugging Face's model hub and can be downloaded and used through the Transformers library. The model provides an accurate and efficient way to perform sentiment analysis on Amazon reviews for tasks such as customer feedback analysis and sentiment monitoring.

Sohan-ai

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