Update README.md

README.md

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-# SentimentBot: A Sentiment Analysis Chatbot with BERT 🤖
-
-## Overview
-SentimentBot is a sentiment analysis chatbot built using BERT, fine-tuned on the SST-2 dataset. It predicts whether a given text expresses a positive or negative sentiment and responds interactively. This project was developed to enhance my NLP skills for an AI Engineer internship.
-
-- Model: BERT (bert-base-uncased)
-- Dataset: SST-2 (Stanford Sentiment Treebank)
-- Performance: Achieved 92.3% accuracy and 92.3% F1 score on the validation set after 2 epochs.
-
-## Features
-
-- Analyzes sentiment of user input (positive/negative).
-- Interactive chatbot responses based on predicted sentiment.
-- Fine-tuned BERT model for high accuracy.
-
-## Installation
-To run this project locally, follow these steps:
-
-1. Clone the repository:
-    ```
-    git clone https://github.com/moraix/SentimentBot.git
-    cd SentimentBot
-    ```
-
-
-2. Install dependencies:
-    ```
-    pip install torch transformers datasets sklearn
-    ```
-
-
-## Usage
-
-1. Run the notebook:
-
-- Open SentimentBot.ipynb in Jupyter Notebook.
-- Follow the steps to train the model.
-
-
-2. Test the chatbot:
-
-- Run the last cell in the notebook to interact with the chatbot.
-- Enter a sentence (e.g., "I love this movie!") and the bot will predict the sentiment and respond.
-
-    Example:
-    Text: I love this movie so much!
-    Sentiment: positive (confidence: 0.95)  
-    I'm glad you liked it! 😊 Do you have any other comments?
-
-
-## Results
-After fine-tuning BERT on SST-2 for 2 epochs:
-
-- Eval Loss: 0.311
-- Accuracy: 92.3%
-- F1 Score: 92.3%
-- Eval Runtime: 3.42 seconds
-
-## Future Improvements
-
-- Add support for more complex sentiments (e.g., neutral, angry).
-- Expand the dataset with custom data.
-- Deploy the chatbot as a web app using Flask or Streamlit.
-
-## Contributing
-Feel free to fork this repository, open issues, or submit pull requests. Feedback is always welcome!
-Contact
-For questions, reach out via mohammadmehdiomidi95@gmail.com or connect with me on LinkedIn.
+---
+tags:
+  - sentiment-analysis
+  - bert
+  - pytorch
+  - text-classification
+license: mit
+---
+
+# SentimentBot: A Sentiment Analysis Chatbot with BERT 🤖
+
+## Overview
+SentimentBot is a fine-tuned BERT model (bert-base-uncased) designed for sentiment analysis, trained on the SST-2 (Stanford Sentiment Treebank) dataset. This model predicts whether a given text expresses a positive or negative sentiment with high accuracy, achieving 92.3% accuracy and 92.3% F1 score on the validation set after 2 epochs. It was developed as part of an AI Engineer internship project focusing on NLP.
+
+## Model Details
+- Base Model: bert-base-uncased
+- Dataset: SST-2 (2 classes: 0 = negative, 1 = positive)
+- Training: Fine-tuned for 2 epochs with a batch size of 16
+- Performance:
+    Eval Loss: 0.311
+    Accuracy: 92.3%
+    F1 Score: 92.3%
+
+## Usage
+To use the SentimentBot model in your Python project, install the required libraries and load the model as follows:
+```
+pip install transformers torch
+```
+```
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("moraix/SentimentBot")
+model = AutoModelForSequenceClassification.from_pretrained("moraix/SentimentBot").to("cuda")
+
+# Example input
+text = "I love this movie so much!"
+inputs = tokenizer(text, return_tensors="pt", padding="max_length", truncation=True, max_length=128).to("cuda")
+
+# Predict sentiment
+model.eval()
+with torch.no_grad():
+    outputs = model(**inputs)
+    predictions = torch.softmax(outputs.logits, dim=1)
+    predicted_class = torch.argmax(predictions, dim=1).item()
+    confidence = predictions[0, predicted_class].item()
+
+label_map = {0: "negative", 1: "positive"}
+sentiment = label_map[predicted_class]
+print(f"Text: {text}")
+print(f"Sentiment: {sentiment} (Confidence: {confidence:.2f})")
+```
+## Intended Uses
+- Primary Use: Analyzing sentiment in English text (e.g., movie reviews, social media posts).
+- Out-of-Scope: Multi-class sentiment (e.g., neutral) or non-English text (requires further fine-tuning).
+## Limitations
+- Trained only on SST-2, so performance may vary on other datasets.
+- Limited to binary classification (positive/negative).
+## Future Improvements
+- dd support for more complex sentiments (e.g., neutral, angry).
+- Expand the dataset with custom data.
+- Deploy the chatbot as a web app using Flask or Streamlit.
+## Contributing
+Feel free to fork this repository, open issues, or submit pull requests. Feedback is always welcome! Contact For questions, reach out via mohammadmehdiomidi95@gmail.com or connect with me on LinkedIn.