https://github.com/hariprasath-v/machinehack_intel_oneapi_hackathon_the_llm_challenge

Generate a response for the question from pre-defined text using LLM(Extracted Question-Answering(QA) Model).
https://github.com/hariprasath-v/machinehack_intel_oneapi_hackathon_the_llm_challenge

accuracy exploratory-data-analysis extractive-question-answering huggingface machine-learning matplotlib nlp nltk numpy pandas python seaborn sklearn spacy spellchecker wordcloud

Last synced: 6 days ago
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Generate a response for the question from pre-defined text using LLM(Extracted Question-Answering(QA) Model).

• Host: GitHub
• URL: https://github.com/hariprasath-v/machinehack_intel_oneapi_hackathon_the_llm_challenge
• Owner: hariprasath-v
• License: apache-2.0
• Created: 2023-10-31T14:27:14.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2023-11-07T15:43:53.000Z (about 1 year ago)
• Last Synced: 2024-01-25T12:09:16.201Z (10 months ago)
• Topics: accuracy, exploratory-data-analysis, extractive-question-answering, huggingface, machine-learning, matplotlib, nlp, nltk, numpy, pandas, python, seaborn, sklearn, spacy, spellchecker, wordcloud
• Language: HTML
• Homepage:
• Size: 2.72 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machinehack_Intel_oneapi_hackathon_the_llm_challenge

### Competition hosted on Machinehack

# About

### Generate a response for the question from pre-defined text using LLM(Extracted Question-Answering(QA) Model).

### The Final Competition score is 0.25114

### Final Leaderboard Rank is 9/35

### The Evaluation Metric is Accuracy.

### File information

 

 * mh-intel-oneapi-hackathon-the-llm-challenge-eda.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/mh-intel-oneapi-hackathon-the-llm-challenge-eda)

    #### Basic Exploratory Data Analysis

    #### Packages Used,

        * seaborn 

        * Pandas

        * Numpy

        * Matplotlib

        * nltk

        * spacy

        * wordcloud

        * spellchecker

        * sklearn

* mh-intel-oneapi-hackathon-the-llm-challenge-model.ipynb [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/code/hari141v/mh-intel-oneapi-hackathon-the-llm-challenge-model2)

    #### I have directly used a pre-trained model without fine-tuning it on the training data, primarily due to my limited knowledge in NLP-QA tasks. I loaded and predicted the test data using the transformers inference pipeline.

    #### Packages Used, 

        * Pandas

        * Huggingface