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Semantic Quality Benchmark for Word Embeddings, i.e. Natural Language Models in Python. Acronym `SeaQuBe` or `seaqube`.

License: MIT License

Python 100.00%

augmentation benchmark fasttext gensim nlp spacy spacy-nlp wordembeddings

seaqube's Introduction

👋 Hi, I’m Ben (@bees4ever)
👀 I’m interested in NLP, currently Speech-To-Text and Translations
🌱 I’m currently learning AWS, Computer Vision
📫 Mail: [email protected], Discord: bees4ever
🏃 Jogging, swimming and bicycling
💞️ I’m looking to collaborate on Data Science Projects and dream to collaborate on blender app and ecosystems.

seaqube's People

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seaqube's Issues

Embedding Augmentation

Improvement with lazy shuffle and limit (i.e. sample) the product tuples in beforehand to have even more and nicer augmentations
implement the KNN strategy of the paper?

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