1)Topic Modeling and Understanding:
The code begins by loading a pre-trained Top2Vec model, which is used for topic modeling and understanding. This model is trained on a corpus of text data, which could include government documents.
2)UMAP (Uniform Manifold Approximation and Projection) is then used to visualize the high-dimensional topic vectors in a lower-dimensional space, providing insights into the topics present in the documents.
Text Cleaning and Preprocessing:
3)The NLTK library is utilized for text cleaning and preprocessing, which includes tasks like lowercasing, removing punctuation, and stopwords.
This preprocessing step is crucial for improving the quality of topic modeling and subsequent analysis.
4)Topic Visualization:
The code generates a bar chart visualization of the top words in each topic identified by the Top2Vec model. This visualization helps in understanding the key themes present in the government documents.
5)Text Classification:
The code includes a section for text classification using a pre-trained BERT-based classifier. This classifier can be fine-tuned on labeled government documents to classify them into relevant categories or topics.
In the provided example, the classifier is used for zero-shot classification, where it predicts the labels for a given sequence of text.
6)Text Summarization:
BART (Bidirectional and Auto-Regressive Transformers) is used for text summarization. Given a lengthy government document, BART can generate a concise summary, capturing the essential information.
This summarization capability is valuable for distilling key insights from lengthy documents, making them more accessible to users.
7)Document Retrieval:
The code integrates FAISS, a library for similarity search and clustering of dense vectors, to create a vector store of document embeddings.
These embeddings capture the semantic information of the government documents, enabling efficient retrieval of relevant documents based on similarity.
8)The RAG (Retrieval-Augmented Generation) pipeline is constructed using the retrieved documents and a language model (LLM).
This pipeline allows for conversational interaction with the chatbot. Users can ask questions or provide context, and the chatbot retrieves relevant information from the documents and generates informative responses.
Issue #1
1)Topic Modeling and Understanding: The code begins by loading a pre-trained Top2Vec model, which is used for topic modeling and understanding. This model is trained on a corpus of text data, which could include government documents.
2)UMAP (Uniform Manifold Approximation and Projection) is then used to visualize the high-dimensional topic vectors in a lower-dimensional space, providing insights into the topics present in the documents. Text Cleaning and Preprocessing:
3)The NLTK library is utilized for text cleaning and preprocessing, which includes tasks like lowercasing, removing punctuation, and stopwords. This preprocessing step is crucial for improving the quality of topic modeling and subsequent analysis.
4)Topic Visualization: The code generates a bar chart visualization of the top words in each topic identified by the Top2Vec model. This visualization helps in understanding the key themes present in the government documents.
5)Text Classification: The code includes a section for text classification using a pre-trained BERT-based classifier. This classifier can be fine-tuned on labeled government documents to classify them into relevant categories or topics. In the provided example, the classifier is used for zero-shot classification, where it predicts the labels for a given sequence of text.
6)Text Summarization: BART (Bidirectional and Auto-Regressive Transformers) is used for text summarization. Given a lengthy government document, BART can generate a concise summary, capturing the essential information. This summarization capability is valuable for distilling key insights from lengthy documents, making them more accessible to users.
7)Document Retrieval: The code integrates FAISS, a library for similarity search and clustering of dense vectors, to create a vector store of document embeddings. These embeddings capture the semantic information of the government documents, enabling efficient retrieval of relevant documents based on similarity.
8)The RAG (Retrieval-Augmented Generation) pipeline is constructed using the retrieved documents and a language model (LLM). This pipeline allows for conversational interaction with the chatbot. Users can ask questions or provide context, and the chatbot retrieves relevant information from the documents and generates informative responses.