Implementation of BERT for text classification

The objective here is to study the plausibility of attention mechanisms in automatic language processing on an NLI (natural naguage inference) task, in transformers (BERT) architecture

This code demonstrates how to automatically generate concise summaries from a given text using advanced natural language processing techniques. By utilizing the powerful "transformers" library, it employs a pre-trained model to break down input text and distill its essential points into succinct summaries.

Data and code for the machine learning exam assignment of MA Digital Text Analysis (2023).

Reproducing GPT2 - Model, Optimizations, training and inference

In this we explore detailed architecture of Transformer

Vaswani Paper analysis for personal Transformer studies

Implementations and resources related to Attention Mechanisms in Natural Language Processing (NLP)

🚀Transformer Model by Pytorch

Educational code for understanding attention mechanisms. You will build a good intuition to K, Q, and V, key in modern Transformer architectures.

An introduction to attention mechanisms and the vision transformer

Importance score calculation for lines of text

Transformer implementation in tensorflow

Experiments in the field of Sentiment Analysis using ML Algorithms namely Logistic Regression, Naive Bayes along with tfidf, one hot encoding, bag of words vectorization. Different MLP and RNN models viz. LSTM, GRU, Bidirectional LSTM. Lastly, state of the art BERT model

Collection of assignments for CS388 to write different NLP tasks mostly from scratch.

A highly-annotated custom Transformer model implementation

Repository for Yowlumne data and scripts for WIELD

Counting currency from video using RepNet as a base model.

Capstone Project related to Transformer based Machine Translation