Experiments on Neural Language Embeddings
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Updated
Sep 21, 2017 - Python
Experiments on Neural Language Embeddings
Container-first, JSON-configurable, NLP REST service based on Flair
An open-source framework to create and test document embeddings using topic models.
Improving document embedding with weighted average of word embedding through topic modeling
Applying NLP to understand people's sentiment about Covid-19 and Government actions in Italy, conditional on their political affiliation.
Telegram Data Clustering Contest (Bossy Gnu's submission )
Word embedding in Java
LD Connect: A Linked Data Portal for IOS Press Scientometrics
🍊 PAUSE (Positive and Annealed Unlabeled Sentence Embedding), accepted by EMNLP'2021 🌴
Expose a Top2Vec model with a REST API.
Service for producing text representations via word embeddings
Content-based book recommendation system
Document chatbot — multiple files, topics, chat windows and chat history. Powered by GPT.
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.
Dive into the world of Word2Vec and Doc2Vec models to uncover insights and applications.
Top2Vec learns jointly embedded topic, document and word vectors.
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