#自然语言处理#A very simple framework for state-of-the-art Natural Language Processing (NLP)
翻译 - 最先进的自然语言处理（NLP）框架
#自然语言处理#Kashgari is a production-level NLP Transfer learning framework built on top of tf.keras for text-labeling and text-classification, includes Word2Vec, BERT, and GPT2 Language Embedding.
翻译 - Kashgari是用于文本标签和文本分类的可立即投入生产的NLP Transfer学习框架，其中包括Word2Vec，BERT和GPT2语言嵌入。
#自然语言处理#中文命名实体识别（包括多种模型：HMM，CRF，BiLSTM，BiLSTM+CRF的具体实现）
#自然语言处理#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.
#自然语言处理#Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition, Part-of-Speech Tagging and so on.
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
#自然语言处理#NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
翻译 - NLP DNN工具包-像玩乐高游戏一样建立NLP DNN模型
#自然语言处理#A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)
#自然语言处理#Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
Empower Sequence Labeling with Task-Aware Language Model
#自然语言处理#The BiLSTM-CRF model implementation in Tensorflow, for sequence labeling tasks.
word2vec, sentence2vec, machine reading comprehension, dialog system, text classification, pretrained language model (i.e., XLNet, BERT, ELMo, GPT), sequence labeling, information retrieval, informati...
A TensorFlow implementation of Recurrent Neural Networks for Sequence Classification and Sequence Labeling
Learning Named Entity Tagger from Domain-Specific Dictionary
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 ident...
#自然语言处理#Deep neural models for core NLP tasks (Pytorch version)
#自然语言处理#AdaSeq: An All-in-One Library for Developing State-of-the-Art Sequence Understanding Models
slot filling, intent detection, joint training, ATIS & SNIPS datasets, the Facebook’s multilingual dataset, MIT corpus, E-commerce Shopping Assistant (ECSA) dataset, CoNLL2003 NER, ELMo, BERT, XLNet
#自然语言处理#A Japanese tokenizer based on recurrent neural networks