estamos/word2vec-thesis
🎓 Diploma Thesis | A Word2vec comparative study of CBOW and Skipgram
This tool helps researchers and data scientists compare the performance of Word2vec's CBOW and Skipgram architectures for natural language processing tasks. It takes text data, processes it through both models, and outputs metrics on training time and effective words processed. This allows users to understand which model might be more suitable for their specific textual analysis needs.
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Use this if you are a researcher or data scientist evaluating the efficiency and characteristics of different Word2vec models for your text data.
Not ideal if you are looking for a ready-to-use application for general text analysis or a production-ready system for word embeddings without needing to compare model architectures.
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MIT
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Last pushed
Jan 31, 2025
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