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116 search results found

Quora_test ⭐ 6

Quick Pytorch attempt at the quora kaggle challende

Repeval_rivercorners ⭐ 6

Code developed for the RepEval 2017 Shared Task by the the team Rivercorners

fBGD: Learning Embeddings From Positive Unlabeled Data with BGD

Semantic Text Segmentation With Embeddings ⭐ 6

Uses GloVe embeddings and greedy sequence segmentation to semantically segment a text document into any number of k segments.

This repository contains the source code of the EMNLP 2020 paper Interactive Fiction Game Playing as Multi-Paragraph Reading Comprehension with Reinforcement Learning.

Word_mover_distance ⭐ 5

Compute Word Mover's Distance using a generic word embedding model

Tokenization And Word Embedding Compatibility ⭐ 5

The Quora Insincere Question Classification competition allows us to use the four embeddings: glove.840B.300d (GloVe), paragram_300_sl999 (paragram), wiki-news-300d-1M (wiki) and GoogleNews-vectors-negative300 (GoogleNews). In a kernel titled: "How to: Preprocessing when Using Embeddings", the author raises the issue of tokenization and its effect on how much of the training vocabulary is covered by words in an embedding. The author uses Google news embeddings to illustrate this point. In this k

Sentiment Analysis With Word Embeddings ⭐ 5

A Sentiment Analysis model in keras to analyse toxic comments online . The Corpus is preprocessed using Glove Word Embeddings .

Updated code for abhipec/fnet

A Word-level Recurrent Neural Network Generative Model

Repository for Configurable Semantic and Sentiment Emotion Detector (ConSSED).

Word2vecpm ⭐ 5

code for paper "Investigating Different Context Types and Representations for Learning Word Embeddings"

A package for reading/writing files containing pre-trained word embeddings and building "embedding matrices".

Gp_debias ⭐ 5

Embedding Visualization ⭐ 5

This is a project for visualizing word embeddings based on the work of Andrei Kashcha (@anvaka).

Quora Question Similarity Using Bert Embeddings With Siamese Networks As Feature Extractors ⭐ 5

Proposed a model architecture which learns to classify duplicate question pairs based on highly contextualized sentence representations. Achieved by using both individual and combined sentence embeddings based on Word2Vec, GloVe and FastText word embeddings with CNN(convolutional neural networks) and DNN(Dense neural network) Siamese networks and an array of features engineered using sentence structure, fuzzy logic , BERT sentence embeddings and BERT [CLS] token.

Easyembed ⭐ 5

downloading pre-trained embedding easily and keeping only the necessary amount of embedding while training

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