Model Card: NLP-Based Chatbot
Overview
The NLP-Based Chatbot is designed to explore Science & Technology topics. It utilizes a combination of semantic search and summarization techniques to provide relevant and concise responses to user queries.
Model Details
- Model Name: NLP-Based Chatbot
- Model Type: Natural Language Processing (NLP) Chatbot
- Framework: Gradio Blocks Interface, spaCy, Transformers
Components
- Semantic Search
The chatbot employs semantic search to retrieve relevant information from a preprocessed dataset (Dronealexa.csv). The search is based on a TF-IDF vectorizer and cosine similarity calculations.
- Summarization
A summarization pipeline is used to generate concise summaries of the retrieved information. The Hugging Face Transformers library is utilized for summarization tasks.
- Custom Embeddings
The model incorporates custom text embeddings using spaCy and pre-trained word embeddings. These embeddings enhance the understanding of user queries and contribute to the semantic search.
- Gradio Blocks Interface
The chatbot's frontend is built using Gradio Blocks Interface, providing an interactive and user-friendly platform for users to input queries and receive responses.
- Model Card Generation
The model card generation involves constructing prompts based on search results and utilizing a summarization pipeline to produce model card content.
Intended Use
The NLP-Based Chatbot is intended for users interested in exploring Science & Technology topics. It can be used to obtain information from the provided dataset, and users are encouraged to provide feedback for continuous improvement.
Training Data
The model is trained on a custom dataset (Dronealexa.csv) containing Science & Technology-related information. The dataset has been preprocessed to handle missing values and ensure efficient semantic search.
Evaluation Metrics
- Semantic Search: TF-IDF Vectorizer, Cosine Similarity
- Summarization: Hugging Face Transformers Pipeline
Ethical Considerations
The chatbot aims to provide accurate and relevant information. However, users are advised to critically evaluate the responses and understand that the model's knowledge is based on the training data.
Usage Instructions
- Input your query in the provided textbox.
- Click the "Send" button to receive a response.
- Optionally, submit feedback using the "Submit Feedback" button.
License
This model is released under the Apache 2.0 License.
Contact Information
For inquiries or issues, please contact
varsagupta07@gmail.com.
