Abstractive text summarization by fine-tuning seq2seq models.

Train a T5 model to generate simple Fake News and use a RoBERTa model to classify what's fake and what's real.

The task is to generate paraphrased questions, that is questions that have the same meaning but are different in terms of the vocabulary and grammar of the sentence The training data is derived from the Datasets of Paraphrased SQuAD Questions and consists of 1,118 paraphrased questions. The evaluation consists of 100 questions also derived from …

Implementation of state-of-the-art NLP models using transformers for tasks including machine translation, text-summarization, chatbots, and question answering.

End-to-End Conditional Poetry Generation

Training T5 to perform numerical reasoning.

Text summarization algorithms using PySpark

This repository contains the final group project my team and I worked on for the COMP0087 module (UCL).

This repo contain An IMDB reviews sentiment classification implementation using a classifier trained on machine summarized text with T5.

NLP model zoo for Russian

A python script to fine-tune pre-trained models for text generation

Bachelor Project - Generating questions from a piece of text using Natural Language Processingl. Ionic React frontend + Node.js backend.

A Searching and Summarizing Engine leveraging a custom-built search engine for news keyword searching, and a pre-trained transformers-based T5 Model, fine-tuned on news text and summary data to achieve state-of-the-art results on text summarization

MOM is a create chrome extension to integrate with online meetings in order to produce multilingual text and voice summarizer for taking Minutes of Meeting

Summarizing_given_text_using_T5(Transformers).

Tutorial for text classification with fine tuning of a T5 model on TPUs.

End-to-End Model - Finetuned T5 for Text-to-SPARQL Task