Popular GitHub Repositories for Conll

Utilizing Spacy and Tensorflow to train custom Named Entity Recognizers.

Use Google's BERT for named entity recognition （CoNLL-2003 as the dataset）.

Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data

This is the template code to use BERT for sequence lableing and text classification, in order to facilitate BERT for more tasks. Currently, the template code has included conll-2003 named entity identification, Snips Slot Filling and Intent Prediction.

BERT-NER (nert-bert) with google bert https://github.com/google-research.

Named-Entity-Recognition-with-Bidirectional-LSTM-CNNs

a sklearn wrapper for Google's BERT model

R package for Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing Based on the UDPipe Natural Language Processing Toolkit

Neural network models for joint POS tagging and dependency parsing (CoNLL 2017-2018)

A Simple but Powerful SOTA NER Model | Official Code For Label Supervised LLaMA Finetuning

reference tensorflow code for named entity tagging

An NLP library for Uralic languages such as Finnish, Skolt Sami, Moksha and so on. Also supporting some non-Uralic languages such as Spanish, French, Arabic, Swedish, Norwegian, Russian and English. LLMs, FSTs and More!

Reimplementation of a BERT based model (Shi et al, 2019), currently the state-of-the-art for English SRL. This model implements also predicate disambiguation.

LSTM-CRF for NER with ConLL-2002 dataset

This repository contains the code for the CONLL 2019 paper "Investigating Entity Knowledge in BERT with Simple Neural End-To-End Entity Linking". The code is provided as a documentation for the paper and also for follow-up research.

Deep-Atrous-CNN-NER: Word level model for Named Entity Recognition

The code base for the SCL implementation used in "Neural Structural Correspondence Learning for Domain Adaptation", CoNLL 2017 and in "Pivot Based Language Modeling for Improved Neural Domain Adaptation", NAACL 2018

This repository tries to implement BERT for NER by trying to follow the paper using transformers library

Source code for CoNLL 2021 paper by Huebner et al. 2021

Code for the paper "The Surprising Computational Power of Nondeterministic Stack RNNs" (DuSell and Chiang, 2023)