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sentence-embeddings

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hierarchical-language-modeling

We address the task of learning contextualized word, sentence and document representations with a hierarchical language model by stacking Transformer-based encoders on a sentence level and subsequently on a document level and performing masked token prediction.

  • Updated Jul 25, 2023
  • Jupyter Notebook

Three different methods namely TFIDF, word average embedding method and inverse document frequency method were used to build a text matching system. The systems were tested on the first 100 questions which were duplicate. A maximum accuracy score of 77% and 67% in top5 and top 2 matches was obtained using average word model.

  • Updated Aug 27, 2021
  • Jupyter Notebook

The project's goal is to help job seekers understand the basic qualifications for specific jobs and evaluate the suitability of their skills for those positions. Additionally, the program aims to assist recruiters in enhancing their resume selection processes by analyzing and understanding job advertisements ....

  • Updated May 26, 2024
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