Use Tensorflow2.7.0 Build OpenAI'GPT-2

Overview

TF2_GPT-2

Use Tensorflow2.7.0 Build OpenAI'GPT-2 使用最新tensorflow2.7.0构建openai官方的GPT-2 NLP模型

优点

  • 使用无监督技术
  • 拥有大量词汇量
  • 可实现续写(堪比“xx梦续写”)
  • 实现对话后续将应用于FloatTech的Bot

食用方法

Setting

  • python >= 3.6
  • numpy==1.16.4
  • sentencepiece==0.1.83
  • tensorflow-gpu==2.7.0

Steps

1. git clone https://github.com/Xhs753/TF2_GPT-2
2. $ cd TF2_GPT-2
3. $ pip install -r requirments.txt

  • 你可以使用词仓库提供的sample.py示例数据预训练模型 ##### 对仓库的可用数据进行训练模型
$ pyton pre_process.py --help

可选项:
  --data-dir TEXT        训练数据路径  [默认: /data/scraped]
  --vocab-size INTEGER   词汇大小和字节大小  [默认: 24512]
  --min-seq-len INTEGER  最小词序长度  [默认: 15]
  --max-seq-len INTEGER  最大词序sequence长度  [默认: 512]
  --help                 显示所有信息并退出
  
  
 ==>>python pre_process.py

在任意数据上训练
>> python pre_process.py --data-dir=data_directory --vocab-size=32000

  • 有关模型的命令源码在此
@click.command()
@click.option('--num-layers', type=int, default=8, show_default=True, help="No. of decoder layers")
@click.option('--embedding-size', type=int, default=768, show_default=True, help="Embedding size")
@click.option('--num-heads', type=int, default=8, show_default=True, help="Number of heads")
@click.option('--dff', type=int, default=3072, show_default=True, help="Filter Size")
@click.option('--max-seq-len', type=int, default=515, show_default=True, help="Seq length")
@click.option('--vocab-size', type=int, default=24512, show_default=True, help="Vocab size")
@click.option('--optimizer', type=str, default="adam", show_default=True, help="optimizer type")
@click.option('--batch-size', type=int, default=8, show_default=True, help="optimizer type")
@click.option('--learning-rate', type=float, default=0.001, show_default=True, help="learning rate")
@click.option('--graph-mode', type=bool, default=False, show_default=False, help="TF run mode")
@click.option('--distributed', type=bool, default=False, show_default=False, help="distributed training")

####### 使用GPT-2

>> python train_gpt2.py \
  --num-layers=8 \
  --num-heads=8 \
  --dff=3072 \
  --embedding-size=768 \
  --batch-size=32 \
  --learning-rate=5e-5
  --graph-mode=True


模型架构

/image

Link

Thanks To My Friends

LICENCE

You might also like...
Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

NeuroNER NeuroNER is a program that performs named-entity recognition (NER). Website: neuroner.com. This page gives step-by-step instructions to insta

When doing audio and video sentiment recognition, I found that a lot of code is duplicated, often a function in different time debugging for a long time, based on this problem, I want to manage all the previous work, organized into an open source library can be iterative. For their own use and others.
Easy to use, state-of-the-art Neural Machine Translation for 100+ languages

EasyNMT - Easy to use, state-of-the-art Neural Machine Translation This package provides easy to use, state-of-the-art machine translation for more th

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy This package provides spaCy components and architectures to use tr

Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

NeuroNER NeuroNER is a program that performs named-entity recognition (NER). Website: neuroner.com. This page gives step-by-step instructions to insta

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

spacy-transformers: Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy This package provides spaCy components and architectures to use tr

Named-entity recognition using neural networks. Easy-to-use and state-of-the-art results.

NeuroNER NeuroNER is a program that performs named-entity recognition (NER). Website: neuroner.com. This page gives step-by-step instructions to insta

Code to use Augmented Shapiro Wilks Stopping, as well as code for the paper "Statistically Signifigant Stopping of Neural Network Training"

This codebase is being actively maintained, please create and issue if you have issues using it Basics All data files are included under losses and ea

A collection of Korean Text Datasets ready to use using Tensorflow-Datasets.

tfds-korean A collection of Korean Text Datasets ready to use using Tensorflow-Datasets. TensorFlow-Datasets를 이용한 한국어/한글 데이터셋 모음입니다. Dataset Catalog |

Releases(GPT-2)
Owner
Watermelon
(-^〇^-) A cute watermelon 🍉
Watermelon
Klexikon: A German Dataset for Joint Summarization and Simplification

Klexikon: A German Dataset for Joint Summarization and Simplification Dennis Aumiller and Michael Gertz Heidelberg University Under submission at LREC

Dennis Aumiller 8 Jan 03, 2023
Code for paper: An Effective, Robust and Fairness-awareHate Speech Detection Framework

BiQQLSTM_HS Code and data for paper: Title: An Effective, Robust and Fairness-awareHate Speech Detection Framework. Authors: Guanyi Mou and Kyumin Lee

Guanyi Mou 2 Dec 27, 2022
Rhyme with AI

Local development Create a conda virtual environment and activate it: conda env create --file environment.yml conda activate rhyme-with-ai Install the

GoDataDriven 28 Nov 21, 2022
Code associated with the "Data Augmentation using Pre-trained Transformer Models" paper

Data Augmentation using Pre-trained Transformer Models Code associated with the Data Augmentation using Pre-trained Transformer Models paper Code cont

44 Dec 31, 2022
🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models.

In recent years, the dense retrievers based on pre-trained language models have achieved remarkable progress. To facilitate more developers using cutt

475 Jan 04, 2023
Convolutional Neural Networks for Sentence Classification

Convolutional Neural Networks for Sentence Classification Code for the paper Convolutional Neural Networks for Sentence Classification (EMNLP 2014). R

Yoon Kim 2k Jan 02, 2023
基于百度的语音识别,用python实现,pyaudio+pyqt

Speech-recognition 基于百度的语音识别,python3.8(conda)+pyaudio+pyqt+baidu-aip 百度有面向python

J-L 1 Jan 03, 2022
test

Lidar-data-decode In this project, you can decode your lidar data frame(pcap file) and make your own datasets(test dataset) in Windows without any hug

46 Dec 05, 2022
Fastseq 基于ONNXRUNTIME的文本生成加速框架

Fastseq 基于ONNXRUNTIME的文本生成加速框架

Jun Gao 9 Nov 09, 2021
An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

An easy-to-use Python module that helps you to extract the BERT embeddings for a large text dataset (Bengali/English) efficiently.

Khalid Saifullah 37 Sep 05, 2022
👑 spaCy building blocks and visualizers for Streamlit apps

spacy-streamlit: spaCy building blocks for Streamlit apps This package contains utilities for visualizing spaCy models and building interactive spaCy-

Explosion 620 Dec 29, 2022
Chatbot for the Chatango messaging platform

BroiestBot The baddest bot in the game right now. Uses the ch.py framework for joining Chantango rooms and responding to user messages. Commands If a

Todd Birchard 3 Jan 17, 2022
PyTorch implementation of Microsoft's text-to-speech system FastSpeech 2: Fast and High-Quality End-to-End Text to Speech.

An implementation of Microsoft's "FastSpeech 2: Fast and High-Quality End-to-End Text to Speech"

Chung-Ming Chien 1k Dec 30, 2022
Awesome-NLP-Research (ANLP)

Awesome-NLP-Research (ANLP)

Language, Information, and Learning at Yale 72 Dec 19, 2022
Rhasspy 673 Dec 28, 2022
MASS: Masked Sequence to Sequence Pre-training for Language Generation

MASS: Masked Sequence to Sequence Pre-training for Language Generation

Microsoft 1.1k Dec 17, 2022
Princeton NLP's pre-training library based on fairseq with DeepSpeed kernel integration 🚃

This repository provides a library for efficient training of masked language models (MLM), built with fairseq. We fork fairseq to give researchers mor

Princeton Natural Language Processing 92 Dec 27, 2022
UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language

UA-GEC: Grammatical Error Correction and Fluency Corpus for the Ukrainian Language This repository contains UA-GEC data and an accompanying Python lib

Grammarly 227 Jan 02, 2023
A python framework to transform natural language questions to queries in a database query language.

__ _ _ _ ___ _ __ _ _ / _` | | | |/ _ \ '_ \| | | | | (_| | |_| | __/ |_) | |_| | \__, |\__,_|\___| .__/ \__, | |_| |_| |___/

Machinalis 1.2k Dec 18, 2022
[Preprint] Escaping the Big Data Paradigm with Compact Transformers, 2021

Compact Transformers Preprint Link: Escaping the Big Data Paradigm with Compact Transformers By Ali Hassani[1]*, Steven Walton[1]*, Nikhil Shah[1], Ab

SHI Lab 367 Dec 31, 2022