Search awesome repositories

A Vietnamese natural language processing toolkit (NAACL 2018)

Fast topic modeling platform

The first-ever vast natural language processing benchmark for Indonesian Language. We provide multiple downstream tasks, pre-trained IndoBERT models, and a starter code! (AACL-IJCNLP 2020)

Dataset and codes for ACL 2019 DocRED: A Large-Scale Document-Level Relation Extraction Dataset.

Resources and tools for Indian language Natural Language Processing

Hierarchical unsupervised and semi-supervised topic models for sparse count data with CorEx

SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models

Accurately generate all possible forms of an English word e.g "election" --> "elect", "electoral", "electorate" etc.

Naver sentiment movie corpus

NLP in Python with Deep Learning

📖 Korean NLU Benchmark

A suite of Arabic natural language processing tools developed by the CAMeL Lab at New York University Abu Dhabi.

REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).

The hands-on NLTK tutorial for NLP in Python

Natural Language Processing Pipeline - Sentence Splitting, Tokenization, Lemmatization, Part-of-speech Tagging and Dependency Parsing

Deliver safe & effective language models

Web-Scale Open Information Extraction

A web-based annotation tool for natural language processing (NLP)

BETO - Spanish version of the BERT model

Library for translating between 200 languages. Built on 🤗 transformers.

Translation models for 22 scheduled languages of India

Polyglot: Large Language Models of Well-balanced Competence in Multi-languages

CogComp's Natural Language Processing Libraries and Demos: Modules include lemmatizer, ner, pos, prep-srl, quantifier, question type, relation-extraction, similarity, temporal normalizer, tokenizer, transliteration, verb-sense, and more.

PyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).

**Archived** Epic is a high performance statistical parser written in Scala, along with a framework for building complex structured prediction models.

KSS: Korean String processing Suite

Evaluating Cross-lingual Sentence Representations

Fast and production-ready question answering in Node.js

A package to evaluate factuality of long-form generation. Original implementation of our EMNLP 2023 paper "FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation"

potato: the portable annotation tool