The SINr approach to train interpretable word and graph embeddings
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Updated
Jun 7, 2024 - Jupyter Notebook
The SINr approach to train interpretable word and graph embeddings
My Bachelor's Thesis
Pre-trained models and language resources for Natural Language Processing in Polish
This project build a classification model for topics of news. With the target is automatically recognize suitable topic (class) to a random article. There are two architectures implemented which are LSTM and Hybrid models
Dictionary processing library
Dictionary processing library
Dictionary processing library
Dictionary processing library
Dictionary processing library
Dictionary processing library
Dictionary processing library
Word Embeding with Simple model, w2v, Simple RNN, LSTM
This project aims to perform sentiment analysis on a Twitter dataset using Convolutional Neural Networks (CNNs). The goal is to classify tweets into positive, negative, or neutral sentiments.
Word Embedding + LSTM + FC
Generating quote-like text with Recurrent Neural Networks (RNNs)
This repository serves as as a practical guide for understanding (NLP) through a Lab. It consists of two Jupyter notebooks, each dedicated to a specific part of the lab.
Official Code Repository for LM-Steer Paper: "Word Embeddings Are Steers for Language Models"
Yet another word2vec implementation from scratch
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