Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.

This is an implementation of the LexVec word embedding model (similar to word2vec and GloVe) that achieves state of the art results in multiple NLP tasks

Oxford Deep NLP 2017 course - Practical 1: word2vec

结合python一起学习自然语言处理 (nlp): 语言模型、HMM、PCFG、Word2vec、完形填空式阅读理解任务、朴素贝叶斯分类器、TFIDF、PCA、SVD

各种nlp 框架（自然语言处理）集成以及使用包括 word2vec nltk textblob crf++ 等

NLP pipeline using word2vec (preprocessing/embedding/prediction/clustering)

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NLP文本增强的两种方式：同义词替换（利用word2vec词表）和回译

A small, interpretable codebase containing the re-implementation of a few "deep" NLP models in PyTorch. Colab notebooks to run with GPUs. Models: word2vec, CNNs, transformer, gpt.

📝Natural language processing (NLP) utils: word embeddings (Word2Vec, GloVe, FastText, ...) and preprocessing transformers, compatible with scikit-learn Pipelines. 🛠

In this repository you will find code for our capstone project "How to Predict Stock Movements Using NLP Techniques". The code has been adapted in the first 3 pairs to illustrate how to scrape the EDGAR webpage. However, the word2vec analysis and FinBERT analysis use as data the data uploaded in this repository. Get in touch with me if you have any questions or if you find any mistakes.

Preprocessing of the dataset of 347 subtitles for the TV series (thanks to Taiga Corpus) to build a word2vec model, JamSpell model, neural network training, chat bot training or in any other NLP task.

This is a small NLP project "E-commerce Title Data Similarity Matching System". The usage methods are: tfidf+word bag model, cosine similarity, word2vec

A detailed educational guide explaining two essential NLP techniques, TF-IDF and Word2Vec. Learn how text is transformed into numerical vectors, compare their mathematical foundations, explore real-world use cases, and implement both methods in Python for text analysis and machine learning.

使用 CNN 进行中文 NLP 分类

七月算法 - NPL课程 - NLP到Word2vec实战班

Various applications uisng word2vec.

NLP for classifying text. Using word Word2Vec word embedding and a neural net with bidirectional LSTM to categorize sentences provided by the user 🤔

Deep learning (DL) approaches use various processing layers to learn hierarchical representations of data. Recently, many methods and designs of natural language processing (NLP) models have shown significant development, especially in text mining and analysis. For learning vector-space representations of text, there are famous models like Word2vec, GloVe, and fastText. In fact, NLP took a big step forward when BERT and recently GTP-3 came out. Deep Learning algorithms are unable to deal with textual data in their natural language data form which is typically unstructured information; they require special representation of data as inputs instead. Usually, natural language text data needs to be converted into internal representations form that DL algorithms can read such as feature vectors, hence the necessity to use representation learning models. These models have shown a big leap during the last years. Their set ranges from the methods that embed words into distributed representations and use the language modeling objective to adjust them as model parameters (like Word2vec, fastText, and GloVe), to recently transfer learning models (like ELMo, BERT, ULMFiT, XLNet, ALBERT, RoBERTa, and GPT-2). These last use larger corpora, more parameters, more computing resources, and instead of assigning each word with a fixed vector, they use multilayer neural networks to calculate dynamic representations for the words according to their context, which is especially useful for the words with multiple meanings.

Word2Vec: Optimal Hyper-Parameters and Their Impact on NLP Downstream Tasks

Kaggle word2vec NLP tutorial

Uses Word2Vec/Doc2Vec and traditional NLP techniques

NLP, Classification, Topic Modeling, and Auto-Tagging of Stack Exchange with Word2Vec, LDA, and Machine Learning.

NLP 모델 정리 : word2vec,fasttext,bert...

Word2Vec sikp-gram model with negative sampling implementation with python3

word2vec, doc2vec testing for Japanese, using Ansible/Docker. See https://github.com/vochicong/datalab-nlp for a Datalab version.

ATRI是一只基于word2vec指令匹配，用seq2seq聊天的，以及对接多方NLP平台智能ai聊天机器人

Amharic-Word Embedding-Word2vec is a pre-trained distributed word representation (word embedding) which aims to provide the Amharic NLP researcher with free to use.

Hate-Speech-Detection-in-Social-Media This project detect hate speech and classify twitter texts using NLP techniques-SpaCy, TF-IDF,Word2vec and Machine Learning techniques in Python.

NLP project detecting gender bias in text. The project is still ongoing as our team is trying to create an implicit gender bias data set. In this repository you will find the web scrapper, the front end, the data architecture as well as the NLP model which is ready to train. The library used was Keras, we are switching from word2vec with glove for our word embeddings.