615 Open source projects that are alternatives of or similar to lstm-crf-tagging

NCRF++, a Neural Sequence Labeling Toolkit. Easy use to any sequence labeling tasks (e.g. NER, POS, Segmentation). It includes character LSTM/CNN, word LSTM/CNN and softmax/CRF components.

LSTM-CRF for NER with ConLL-2002 dataset

KU_NERDY 이동엽, 임희석 (2017 국어 정보 처리 시스템경진대회 금상) - 한글 및 한국어 정보처리 학술대회

datagrand 2019 information extraction competition rank9

keras attentional bi-LSTM-CRF for Joint NLU (slot-filling and intent detection) with ATIS

An easy-to-use named entity recognition (NER) toolkit, implemented the Bi-LSTM+CRF model in tensorflow.

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Documents, papers and codes related to Natural Language Processing, including Topic Model, Word Embedding, Named Entity Recognition, Text Classificatin, Text Generation, Text Similarity, Machine Translation)，etc. All codes are implemented intensorflow 2.0.

Chinese & English Cws Pos Ner Entity Recognition implement using CNN bi-directional lstm and crf model with char embedding.基于字向量的CNN池化双向BiLSTM与CRF模型的网络，可能一体化的完成中文和英文分词，词性标注，实体识别。主要包括原始文本数据，数据转换,训练脚本,预训练模型,可用于序列标注研究.注意：唯一需要实现的逻辑是将用户数据转化为序列模型。分词准确率约为93%，词性标注准确率约为90%，实体标注（在本样本上）约为85%。

Keras solution of Chinese NER task using BiLSTM-CRF/BiGRU-CRF/IDCNN-CRF model with Pretrained Language Model: supporting BERT/RoBERTa/ALBERT

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RNNSharp is a toolkit of deep recurrent neural network which is widely used for many different kinds of tasks, such as sequence labeling, sequence-to-sequence and so on. It's written by C# language and based on .NET framework 4.6 or above versions. RNNSharp supports many different types of networks, such as forward and bi-directional network, sequence-to-sequence network, and different types of layers, such as LSTM, Softmax, sampled Softmax and others.

Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services

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中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）

reference tensorflow code for named entity tagging

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End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF implement in pyotrch

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中文NER的那些事儿

reference pytorch code for named entity tagging

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中文自然语言的实体抽取和意图识别（Natural Language Understanding），可选Bi-LSTM + CRF 或者 IDCNN + CRF

keras+bi-lstm+crf，中文命名实体识别

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This library provides a set of basic functions for different type of deep learning (and other) algorithms in C.This deep learning library will be constantly updated

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Pure Rust port of CRFsuite: a fast implementation of Conditional Random Fields (CRFs)

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Deep Convolutional Bidirectional LSTM for Complex Activity Recognition with Missing Data. Human Activity Recognition Challenge. Springer SIST (2020)

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PyTorch implementation of the paper: Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding. Su Zhu, Ruisheng Cao, Lu Chen and Kai Yu.

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The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

NER（命名实体识别）中文语料，一站式获取

This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.

Artificially intelligent typing assistant that suggest the next word using user typing history and full sentence using LSTM . Works with any environment (MS word, notepad, coding IDEs or anything)

Keras and PyTorch implementations of the MaLSTM model for computing Semantic Similarity.

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A simple zero-dependency React component for tagging user-defined entities within a block of text.