Integrated BERT with Elasticsearch for Text Classification

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Integrated BERT with Elasticsearch for Text Classification

  • Day: 2024-07-11
  • Time: 21:20 to 23:30
  • Project: Dev
  • Workspace: WP 2: Operational
  • Status: Completed
  • Priority: MEDIUM
  • Assignee: Matías Nehuen Iglesias
  • Tags: BERT, Elasticsearch, Text Classification, Openai Api, Error Handling

Description

Session Goal

The primary goal of this session was to integrate a BERT-based text classification model with Elasticsearch and improve the robustness of a URL classification system using OpenAI’s API.

Key Activities

  • BERT and Elasticsearch Integration: Detailed steps were provided to train a BERT model, set up Elasticsearch, ingest classified data, and perform searches and analytics.
  • Knowledge Web Workflow: A comprehensive workflow was outlined for creating a knowledge web from URLs, including data collection, processing, entity recognition, categorization, indexing, and visualization.
  • AI Agent for Dataset Labeling: Implemented an AI agent using GPT-4 for generating labels for URLs, which were used to fine-tune a BERT model.
  • URL Classification with OpenAI API: Developed a Python implementation for classifying URLs using the OpenAI API, involving the URLClassifier class and its methods.
  • Error Handling Enhancements: Addressed errors in the URLClassifier class, including argument errors and handling of null values in input data.
  • HTML Cleaning with BeautifulSoup: Enhanced the clean_html function to improve text extraction quality, which was applied to URL classification.
  • Entity Recognition with spaCy: Improved entity recognition processes using spaCy, including filtering irrelevant HTML content.

Achievements

  • Successfully integrated BERT with Elasticsearch for text classification.
  • Developed a robust workflow for URL classification using OpenAI’s API.
  • Enhanced error handling and robustness in the URL classification system.
  • Improved HTML content cleaning and entity recognition processes.

Pending Tasks

  • Further testing and validation of the integrated systems.
  • Optimization of the BERT model for specific use cases.
  • Exploration of additional enhancements for entity recognition.

Evidence

  • source_file=2024-07-11.sessions.jsonl, line_number=2, event_count=0, session_id=c464553cbacc43b4d392d8bb84545f60f4a819a256122f9f7f7fed8831b7e93a
  • event_ids: []

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