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BERT Chatbot Conversational AI Open Source

BERT-based chatbot system fine-tuned for conversational AI with bidirectional context understanding. The system uses transformer architecture to understand context from both directions, making it excellent for natural conversations. Complete implementation with PyTorch, Transformers (Hugging Face), including semantic similarity search, intent classification, sentiment analysis, REST API, and comprehensive evaluation tools.

BERT PyTorch Transformers NLP Download Now Jupyter Notebook REST API Get Started
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BERT Chatbot Project - RSK World
BERT Chatbot Project - RSK World
Natural Language Processing Conversational AI Python PyTorch Transformers BERT

This project implements a BERT-based chatbot system fine-tuned for conversational AI. BERT (Bidirectional Encoder Representations from Transformers) uses bidirectional context to understand language, making it excellent for chatbots that need to understand conversation context. Perfect for natural language understanding and response generation, featuring PyTorch implementation, Transformers (Hugging Face), semantic similarity search, intent classification, sentiment analysis, REST API, and comprehensive evaluation tools.

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BERT Transformer Architecture

BERT (Bidirectional Encoder Representations from Transformers) architecture with bidirectional context understanding designed for conversational AI. Uses transformer encoder layers for natural language understanding.

  • Bidirectional context encoding
  • Transformer encoder architecture
  • Pre-trained BERT base model
  • Fine-tuning for conversational AI

Context-Aware Conversations

Multi-turn conversation support with context management, session handling, and sliding window context for natural dialogue flow.

  • Multi-turn conversation support
  • Context management system
  • Session-based conversations
  • Sliding window context

Semantic Similarity Search

BERT-based embedding search using cosine similarity to find relevant responses from conversation database.

  • BERT embedding-based search
  • Cosine similarity matching
  • Top-K response retrieval
  • Response database indexing

Intent Classification

Automatically detect user intent (greeting, question, request, etc.) with confidence scoring for intent-aware responses.

  • 10 intent categories
  • Keyword-based classification
  • Confidence scoring
  • Intent-aware response selection

Sentiment Analysis

Analyze user sentiment (positive, negative, neutral) with sentiment scoring for sentiment-aware response generation.

  • Positive/Negative/Neutral detection
  • Sentiment scoring
  • Sentiment-aware responses
  • Real-time sentiment analysis

Jupyter Notebook

Interactive Jupyter Notebook for BERT chatbot training, evaluation, fine-tuning, and demonstration.

  • BERT Chatbot demo notebook
  • Fine-tuning examples
  • Evaluation metrics demonstration
  • Interactive tutorials

Evaluation Metrics

Comprehensive evaluation with BLEU, METEOR, ROUGE-L scores, and perplexity calculation for response quality assessment.

  • BLEU scores (1-4 grams)
  • METEOR score
  • ROUGE-L score
  • Perplexity calculation

Data Augmentation

Enhance training data with synonym replacement, paraphrasing, and noise injection for improved model generalization.

  • Synonym replacement
  • Paraphrasing techniques
  • Noise injection
  • Automated dataset expansion

REST API Server

Full REST API with multiple endpoints for chat, intent classification, sentiment analysis, similarity search, and evaluation.

  • Flask-based REST API
  • Multiple API endpoints
  • CORS enabled
  • Request logging and monitoring

Conversation Analytics

Comprehensive analytics including dataset statistics, word frequency analysis, topic distribution, and conversation length analysis.

  • Dataset statistics
  • Word frequency analysis
  • Topic distribution
  • Conversation length analysis

Export Utilities

Export conversations to CSV, plain text, and training formats with statistics export capabilities.

  • CSV export
  • Plain text export
  • Training format export
  • Statistics export

Response Ranking System

Multi-factor response ranking using similarity (50%), intent confidence (20%), and sentiment matching (30%) for optimal response selection.

  • Multi-factor ranking
  • Similarity-based ranking
  • Intent confidence weighting
  • Sentiment matching

Training & Fine-tuning

Complete training pipeline with fine-tuning script, data loading, validation loop, checkpointing, and learning rate scheduling.

  • Fine-tuning script
  • Training loop with validation
  • Model checkpointing
  • Learning rate scheduling

Utility Functions

Helper functions for logging, file management, text preprocessing, model utilities, and common development tasks.

  • Logging setup and management
  • Text preprocessing utilities
  • Model utility functions
  • Common development helpers

Requirements

The following are the technical requirements for this project:

  • Python 3.8+
  • PyTorch 2.0+
  • Transformers 4.30+
  • NLTK 3.8+
  • spaCy 3.6+
  • Flask 2.3+
  • Jupyter Notebook 1.0.0+

Credits & Acknowledgments

This project is developed for educational purposes and utilizes the following resources:

  • Python - PSF License
  • PyTorch - BSD License
  • TensorFlow - Apache 2.0 License
  • Keras - Apache 2.0 License
  • RSK World - Project Inspiration
  • GitHub Repository - Source code and documentation

Support & Contact

For paid applications, please contact us for integration help or feedback.

  • Support Email: help@rskworld.in
  • Contact Number: +91 9330539277
  • Website: RSKWORLD.in
  • GitHub Project
  • Join Our Discord
  • Slack Support Channel
  • BERT Chatbot Documentation
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Categories

Natural Language Processing Conversational AI Python PyTorch Transformers BERT

Technologies

Python 3.8+
Transformers
PyTorch
NLP
BERT

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About RSK World

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Founder: Molla Samser
Designer & Tester: Rima Khatun

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