fasttext-embeddings

Classification of acronyms and their long forms using an RNN (LSTM), CNN, and FFNN model. The experiments focused on the RNN and used different vectorisation methods and hyperparameters. Models were built with Keras and the notebook code runs on Google Colab.

deep-learning keras cnn ner ffnn fasttext-embeddings rnn-lstm word2vec-embeddings

Updated Apr 30, 2024
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amirR01 / Modern-Information-Retrieval

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Modern Information Retrieval Project

python search-engine crawler information-retrieval neural-network clustering tensorflow naive-bayes classification mir kmeans-clustering ir page-rank fasttext-embeddings pythorch crossentropyloss hirarchical-clustering modern-information-retrieval

Updated Apr 3, 2024
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dimits-ts / text_analytics

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Language Modelling (text generation, spell correction) and Sentiment Analysis / POS Tagging with MLP, RNN, CNN and BERT models and LLM prompting

sentiment-analysis tensorflow sklearn transformers text-generation language-modeling cnn pytorch rnn bert pos-tagging mlp-classifier baseline-model fasttext-embeddings transformers-library spell-correction llm prompting

Updated Mar 31, 2024
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mariagabv / lasolana-embeddings

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Word Embeddings for the town of La Solana (Ciudad Real)

wordembeddings fasttext-embeddings lasolana

Updated Mar 20, 2024
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faezeh-gholamrezaie / Vectorization-Techniques-tutorial

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Vectorization Techniques in Natural Language Processing Tutorial for Deep Learning Researchers

nlp text-classification word2vec text-analysis tf-idf bow vectorization cbow skipgram doc2vec doc2vec-word2vec glove-embeddings sentence-embeddings infersent fasttext-embeddings universal-sentence-encoder bert-embeddings sentence-bert

Updated Mar 16, 2024
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cja5553 / attention-driven-imitation-in-consumer-reviews

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Codes for manuscript titled "Attention-driven imitation in consumer reviews" by Charles Alba, Mikhail Spektor and Lukasz Walasek

steam text-mining video-game text-similarity steam-api fasttext fasttext-embeddings

Updated Mar 6, 2024
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Vidhi1290 / Word2Vec-and-FastText-Word-Embedding-with-Gensim-in-Python

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This project explores the realm of Natural Language Processing (NLP) using Word2Vec and FastText models. Dive into domain-specific embeddings, analyze clinical trials data related to Covid-19, and uncover the power of AI and ML in understanding textual data.🌟

python nlp machine-learning numpy word2vec plotly jupyter-notebook pandas nltk matplotlib genism fasttext-embeddings streamlit

Updated Mar 4, 2024
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songye38 / 2024_embedding_study

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한국어 임베딩 책을 바탕으로 임베딩 모델에 대한 공부

embedding-models glove-embeddings fasttext-embeddings word2vec-embeddinngs bert-embeddings elmo-embedding

Updated Feb 8, 2024

MeysamAmirsardari / socio-sent-sighter

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Persian Social Media Sentiment Analyzer

sentiment-analysis lstm lstm-model persian-twitter fasttext geometric-deep-learning fasttext-embeddings graph-neural-network persian-sentiment-analysis

Updated Feb 8, 2024
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Baragouine / radsum

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This repository presents and compares HeterSUMGraph and variants doing extractive summarization, named entity recognition or both. HeterSUMGraph and variants use GATv2Conv (from torch_geometric).

natural-language-processing torch named-entity-recognition extractive-text-summarization fasttext-embeddings gatv2 torchgeometric hetersumgraph

Updated Jan 15, 2024
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BlackKakapo / Romanian-Word-Embeddings

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Romanian Word Embeddings. Here you can find pre-trained corpora of word embeddings. Current methods: CBOW, Skip-Gram, Fast-Text (from Gensim library). The .vec and .model files are available for download (all in one archive).