#自然语言处理# Natural Language Processing for the next decade. Tokenization, Part-of-Speech Tagging, Named Entity Recognition, Syntactic & Semantic Dependency Parsing, Document Classification
#自然语言处理# 该仓库用于跟进然语言处理领域最新进展,包括最常见的 NLP 任务的数据集和当前的最新技术。
#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# CoreNLP: A Java suite of core NLP tools for tokenization, sentence segmentation, NER, parsing, coreference, sentiment analysis, etc.
翻译 - Stanford CoreNLP:核心NLP工具的Java套件。
#自然语言处理# Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
翻译 - 斯坦福NLP官方Python语言库,支持多种人类语言
#自然语言处理# An open source library for deep learning end-to-end dialog systems and chatbots.
翻译 - 一个用于深度学习端到端对话系统和聊天机器人的开源库。
Tensorflow solution of NER task Using BiLSTM-CRF model with Google BERT Fine-tuning And private Server services
翻译 - NER任务的Tensorflow解决方案将BiLSTM-CRF模型与Google BERT微调和私有服务器服务结合使用
Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI
翻译 - 用于分类,NER,QA,语言建模,语言生成,T5,多模式和会话式AI的变压器
#自然语言处理# State of the Art Natural Language Processing
翻译 - 最先进的自然语言处理
[EMNLP 2022] An Open Toolkit for Knowledge Graph Extraction and Construction
CLUENER2020 中文细粒度命名实体识别 Fine Grained Named Entity Recognition
#自然语言处理# Generalist and Lightweight Model for Named Entity Recognition (Extract any entity types from texts) @ NAACL 2024
#自然语言处理# An elegent pytorch implement of transformers
Pytorch-Named-Entity-Recognition-with-BERT
翻译 - 用BERT的Pytorch命名实体识别
knowledge graph知识图谱,从零开始构建知识图谱
#自然语言处理# 🦙 Integrating LLMs into structured NLP pipelines
#自然语言处理# 👑 spaCy building blocks and visualizers for Streamlit apps
翻译 - Stream适用于Streamlit应用程序的spaCy构建块和可视化工具
Fast transformer inference for Ruby
翻译 - Ruby的最新自然语言处理
multi_task_NLP is a utility toolkit enabling NLP developers to easily train and infer a single model for multiple tasks.
翻译 - multi_task_NLP是一个实用工具包,使NLP开发人员可以轻松地为多个任务训练和推断单个模型。