Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
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Updated
May 15, 2020 - Python
Transformers for Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
Detection of Emotion and its cause from text
This is a simple repository on how to get started with the transformer model translation task. It uses the pre-defined models of the Transformer model and It's a simple guide on how to make your own version of Google Translate
The project trains google T5 on ANLG and COS-E datasets and use these pretrained models to generates explanations for ReColr contexts which along with the context and question are passed to ALBERT for prediction.
The repository contains model implementations and data described in the paper: From Dataset Recycling to Multi-Property Extraction and Beyond.
A text generation library to paraphrase image captions using back translations or transfer learning.
[제 11회 투빅스 컨퍼런스] AM I OK ? - 전문의 답변 기반 심리진단 AI
[제 11회 투빅스 컨퍼런스] AM I OK ? - 전문의 답변 기반 심리진단 AI
[제 11회 투빅스 컨퍼런스] AM I OK ? - 전문의 답변 기반 심리진단 AI
👨🎓 This repo is a supplement to my video on Transformers and Text Summarization as part of my series AI does AI (https://youtu.be/p_6xgrykPMQ)
This project is about end-to-end implementation of sentence paraphrasing model using the docker
A package for fine-tuning Transformers with TPUs, written in Tensorflow2.0+
Supervised text summarization (title generation/recommendation) based on academic paper abstracts, with Seq2Seq LSTM and T5.
Generate abstractive summarisation from input text using transformer models
Codes for our paper "JointGT: Graph-Text Joint Representation Learning for Text Generation from Knowledge Graphs" (ACL 2021 Findings)
Conversational T5 model, finetuned for long-context conversational tasks
Codes to pre-train Japanese T5 models
Python package to generate MCQ Questions and Answers.
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