AI Code Completions
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Updated
Jun 23, 2022 - Shell
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AI Code Completions
An implementation of model parallel GPT-2 and GPT-3-style models using the mesh-tensorflow library.
Chinese version of GPT2 training code, using BERT tokenizer.
Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
Toolkit for Machine Learning, Natural Language Processing, and Text Generation, in TensorFlow. This is part of the CASL project: http://casl-project.ai/
Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo
GPT2 for Chinese chitchat/用于中文闲聊的GPT2模型(实现了DialoGPT的MMI思想)
This repository contains demos I made with the Transformers library by HuggingFace.
Large-scale pretraining for dialogue
GPT2 for Multiple Languages, including pretrained models. GPT2 多语言支持, 15亿参数中文预训练模型
Visual Studio Code client for Tabnine. https://marketplace.visualstudio.com/items?itemName=TabNine.tabnine-vscode
Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
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Guide to using pre-trained large language models of source code
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation
setting pretrained_model_name will not only define the model arch but also load the pre-trained checkpoint. We should have another hparam to control whether to load pre-trained checkpoint or not.
A curated list of NLP resources focused on BERT, attention mechanism, Transformer networks, and transfer learning.
Vim client for TabNine. https://vimawesome.com/plugin/tabnine-vim
Medical Q&A with Deep Language Models
TextBox is an open-source library for building text generation system.
✍🏻 gpt2-client: Easy-to-use TensorFlow Wrapper for GPT-2 117M, 345M, 774M, and 1.5B Transformer Models
Jetbrains IDEs client for TabNine. Compatible with all IntelliJ-based IDEs. https://plugins.jetbrains.com/plugin/12798-tabnine
Your new Telegram buddy powered by transformers
Text-generation API via GPT-2 for Cloud Run
EMNLP 2020: "Dialogue Response Ranking Training with Large-Scale Human Feedback Data"
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
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I'm playing around with this wonderful code but I'm running into a curious issue when I try to train the model with my own data.
I replicated the
personachat_self_original.jsonfile structure and added my own data. I deleteddataset_cache_OpenAIGPTTokenizerfile but when I try to train, I get this error: