🦆 Contextually-keyed word vectors
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Updated
Mar 17, 2024 - Python
🦆 Contextually-keyed word vectors
Amazon SageMaker Local Mode Examples
Using pre trained word embeddings (Fasttext, Word2Vec)
Incremental learning of word embeddings with context informativeness.
Wikidata embedding
ArWordVec is a collection of pre-trained word embedding model built from huge repository of Arabic tweets in different topics. The aim of these models is to support the community in their Arabic NLP-based research.
Ensemble PhoBERT with FastText Embedding to improve performance on Vietnamese Sentiment Analysis tasks.
Code to run LDA algorithm on Twitter/Foursquare scraped data.
A tool to view how Word2Vec represents words in your favourite books.
A web service that exposes semantic similarity search via a web GUI and a RESTful API.
Automnomously attempting a categorical summarization of a sparse, asymmetrical corpus in English language, by performing text classification - which is achieved by our intuitive sentence pair classification scenarios and usecases.
Natural Language Processing (traitement automatique du langage naturel )
Text classification is the task of assigning a set of predefined categories to free text. Text classifiers can be used to organize, structure, and categorize pretty much anything. For example, new articles can be organized by topics, support tickets can be organized by urgency, chat conversations can be organized by language, brand mentions can …
A prototype legal text search engine that uses a semantic search algorithm in order to find related keywords and sort the results by relevance.
Incremental learning of word embeddings with context informativeness
This project implements a semi-supervised approach to classify UN speeches. Utilized BERT, Gensim, Node2Vec and Tensorflow
Api and library for getting related terms from a word embedding
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