intfloat

intfloat's Stars

• pwxcoo/chinese-xinhua

  :orange_book: 中华新华字典数据库。包括歇后语，成语，词语，汉字。

  Language:Python11k307602.6k

• cleanlab/cleanlab

  The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

  Language:Python9.9k88366753

• catboost/catboost

  A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

  Language:Python8.2k1912.4k1.2k

• AntixK/PyTorch-VAE

  A Collection of Variational Autoencoders (VAE) in PyTorch.

  Language:Python6.8k43881.1k

• mozillazg/python-pinyin

  汉字转拼音(pypinyin)

  Language:Python4.9k99268616

• facebookresearch/moco

  PyTorch implementation of MoCo: https://arxiv.org/abs/1911.05722

  Language:Python4.8k52135796

• km1994/nlp_paper_study

  该仓库主要记录 NLP 算法工程师相关的顶会论文研读笔记

  Language:C++3.9k718659

• princeton-nlp/SimCSE

  [EMNLP 2021] SimCSE: Simple Contrastive Learning of Sentence Embeddings https://arxiv.org/abs/2104.08821

  Language:Python3.5k30274519

• yuchenlin/rebiber

  A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).

  Language:Python2.7k1531161

• dragen1860/MAML-Pytorch

  Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

  Language:Python2.3k2374423

• facebookresearch/swav

  PyTorch implementation of SwAV https//arxiv.org/abs/2006.09882

  Language:Python2k41116283

• pykeen/pykeen

  🤖 A Python library for learning and evaluating knowledge graph embeddings

  Language:Python1.7k27555192

• DeepGraphLearning/KnowledgeGraphEmbedding

  Language:Python1.3k2554267

• facebookresearch/BLINK

  Entity Linker solution

  Language:Python1.2k3994230

• shangjingbo1226/AutoPhrase

  AutoPhrase: Automated Phrase Mining from Massive Text Corpora

  Language:C++1.2k3982276

• tangjianpku/LINE

  LINE: Large-scale information network embedding

  Language:C++1k4441408

• castorini/anserini

  Anserini is a Lucene toolkit for reproducible information retrieval research

  Language:Java1k38617466

• facebookresearch/KILT

  Library for Knowledge Intensive Language Tasks

  Language:Python921253191

• XiaoMi/MiNLP

  XiaoMi Natural Language Processing Toolkits

  Language:Scala792266889

• facebookresearch/GENRE

  Autoregressive Entity Retrieval

  Language:Python7751996104

• google-research/xtreme

  XTREME is a benchmark for the evaluation of the cross-lingual generalization ability of pre-trained multilingual models that covers 40 typologically diverse languages and includes nine tasks.

  Language:Python6332069111

• princeton-nlp/DensePhrases

  [ACL 2021] Learning Dense Representations of Phrases at Scale; EMNLP'2021: Phrase Retrieval Learns Passage Retrieval, Too https://arxiv.org/abs/2012.12624

  Language:Python605133176

• wmayner/pyemd

  Fast EMD for Python: a wrapper for Pele and Werman's C++ implementation of the Earth Mover's Distance metric

  Language:C++484125062

• bojone/BERT-whitening

  简单的向量白化改善句向量质量

  Language:Python48361366

• ChunyuanLI/Optimus

  Optimus: the first large-scale pre-trained VAE language model

  Language:Python380133037

• alexa/dialoglue

  DialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue

  Language:Python283191727

• facebookresearch/multihop_dense_retrieval

  Multi-hop dense retrieval for question answering

  Language:Python214102122

• Eric-Wallace/interpretability-tutorial-emnlp2020

  Materials for the EMNLP 2020 Tutorial on "Interpreting Predictions of NLP Models"

  1979112

• CZWin32768/XLM-Align

  Language:Python36441

• facebookresearch/bitext-lexind

  Bilingual lexicons map words in one language to their translations in another, and are typically induced by learning linear projections to align monolingual word embedding spaces. In this paper, we show it is possible to produce much higher quality lexicons with methods that combine (1) unsupervised bitext mining and (2) unsupervised word alignment. Directly applying a pipeline that uses recent algorithms for both subproblems significantly improves induced lexicon quality and further gains are possible by learning to filter the resulting lex-ical entries, with both unsupervised and semi-supervised schemes. Our final approach out-performs the state of the art on the BUCC 2020shared task by 14 F1 points averaged over 12 language pairs, while also providing a more interpretable approach that allows for rich reasoning of word meaning in context.

  Language:Python16612