Github repo with tutorials to fine tune transformers for diff NLP tasks

Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.

Sentiment analysis neural network trained by fine-tuning BERT, ALBERT, or DistilBERT on the Stanford Sentiment Treebank.

A collection of resources on using BERT (https://arxiv.org/abs/1810.04805 ) and related Language Models in production environments.

reference pytorch code for named entity tagging

reference pytorch code for intent classification

Pytorch-Named-Entity-Recognition-with-transformers

Distillation of BERT model with catalyst framework

DistilBERT Question Answering model using SQuAD served with Flask, deployed on Google Cloud Run

finetune with keras

BERT for sequence classification (sentiment analysis) served with Flask, deployed on Google Cloud Run

Models to perform neural summarization (extractive and abstractive) using machine learning transformers and a tool to convert abstractive summarization datasets to the extractive task.

This project used in my dissertation for detecting sarcasm in twitter dataset using Sate-of-the-Art Transformer

revising reddit troll hunting with some new nlp techniques (DistilBERT, Multi-Sample Dropout, OOF, Group K-fold on subreddit and some fancy pre-processing)

NLP Service to perform text classification. This is the first part of Project Jarvis. This service integrates to the chat-bot service

Reddit Flair Detector

EmotionalAI is a machine learning model which classify the given text into positive or negative sentiment.

Online Toxic Comment Detector web application: https://toxic-comment-detector.herokuapp.com/

NLP deep learning model for multilingual toxicity detection in text 📚

Language Detection using DistilBERT

A question-answering app, which can answer (almost) all of your questions

Transformers

My final bachelor's project is dedicated to studying multi-language models based on the transformer architecture. [IN PROGRESS]

A wrapper for Huggingface's Tokenizers library , for it to be used along with existing version of Huggingface Transformers library.

training sentence sentiment classification with Bert

BERT (Bidirectional Encoder Representations from Transformers) is a transformer-based method of learning language representations. It is a bidirectional transformer pre-trained model developed using a combination of two tasks namely: masked language modeling objective and next sentence prediction on a large corpus.

The aims this program is to doing Multi class classification for FNC-1 Dataset. I built in simple way.