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accelerate

🚀 A simple way to train and use PyTorch models with multi-GPU, TPU, mixed-precision

algorithm_interview_notes-chinese

2018/2019/校招/春招/秋招/自然语言处理(NLP)/深度学习(Deep Learning)/机器学习(Machine Learning)/C/C++/Python/面试笔记，此外，还包括创建者看到的所有机器学习/深度学习面经中的问题。 除了其中 DL/ML 相关的，其他与算法岗相关的计算机知识也会记录。 但是不会包括如前端/测试/JAVA/Android等岗位中有关的问题。

bert

TensorFlow code and pre-trained models for BERT

bert-chinese-ner

使用预训练语言模型BERT做中文NER

bert_chinesewordsegment

A Chinese word segment model based on BERT, F1-Score 97%

bert_squad_embedding

Word embeddings for SQuAD 1.0 dataset extract from Bert

correlation

Correlation.py - calculates Pearson Rho, Spearman Rho, and Kendall Tau for determining correlation.

damo-convai

DAMO-ConvAI: The official repository which contains the codebase for Alibaba DAMO Conversational AI.

datasets

🤗 The largest hub of ready-to-use NLP datasets for ML models with fast, easy-to-use and efficient data manipulation tools

eqg-race

Data and code for paper "EQG-RACE: Examination-Type Question Generation" at AAAI2021.

few-shot-nlg

Code and Data for ACL 2020 paper "Few-Shot NLG with Pre-Trained Language Model" https://arxiv.org/abs/1904.09521

fine-tuning-gpt2

Codebase for the Medium Article on Fine-tuning GPT2 for Text Generation

jemmryx.github.io

name-entity-recognition

Lstm-crf,Lattice-CRF,bert-ner及近年ner相关论文follow

nlper-interview

该仓库主要记录 NLP 算法工程师相关的面试题

nmt

TensorFlow Neural Machine Translation Tutorial

pkuautosubmit

PKU一键出入校备案小工具

prophetnet

ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training https://arxiv.org/pdf/2001.04063.pdf

pytorch-attention

pytorch neural network attention mechanism

qg

Implementation of the paper "Learning to Generate Questions by Learning What not to Generate"

qgforqa

refnet-qg

RefNet for Question Generation

sentencoding

Sentence encoder and training code for Mean-Max AAE

transformer

A TensorFlow Implementation of the Transformer: Attention Is All You Need

transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.