Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Transformers | 102,346 | 64 | 911 | 21 hours ago | 91 | June 21, 2022 | 728 | apache-2.0 | Python | |
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. | ||||||||||
D2l Zh | 43,808 | 1 | 6 days ago | 45 | March 25, 2022 | 33 | apache-2.0 | Python | ||
《动手学深度学习》:面向中文读者、能运行、可讨论。中英文版被60多个国家的400多所大学用于教学。 | ||||||||||
Made With Ml | 33,193 | a month ago | 5 | May 15, 2019 | 11 | mit | Jupyter Notebook | |||
Learn how to responsibly develop, deploy and maintain production machine learning applications. | ||||||||||
Spacy | 26,242 | 1,533 | 842 | a day ago | 196 | April 05, 2022 | 108 | mit | Python | |
💫 Industrial-strength Natural Language Processing (NLP) in Python | ||||||||||
Applied Ml | 24,242 | 6 days ago | 3 | mit | ||||||
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production. | ||||||||||
Nlp Progress | 21,649 | 2 days ago | 50 | mit | Python | |||||
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks. | ||||||||||
D2l En | 17,987 | a day ago | 99 | other | Python | |||||
Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 400 universities from 60 countries including Stanford, MIT, Harvard, and Cambridge. | ||||||||||
Rasa | 16,423 | 32 | 28 | 2 days ago | 274 | July 06, 2022 | 121 | apache-2.0 | Python | |
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants | ||||||||||
Datasets | 16,335 | 9 | 208 | a day ago | 52 | June 15, 2022 | 616 | apache-2.0 | Python | |
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools | ||||||||||
Mindsdb | 16,317 | 3 | 1 | a day ago | 42 | March 19, 2019 | 615 | gpl-3.0 | Python | |
MindsDB is a Server for Artificial Intelligence Logic. Enabling developers to ship AI powered projects to production in a fast and scalable way. |
收集NLP领域相关的数据集、论文、开源实现,尤其是情感分析、情绪原因识别、评价对象和评价词抽取等方面。
An Ensemble Approach for Emotion Cause Detection with Event Extraction and Multi-Kernel SVMs. paper
Event-Driven Emotion Cause Extraction with Corpus Construction. paper
A Question Answering Approach to Emotion Cause Extraction. paper
A Bootstrap Method for Automatic Rule Acquisition on Emotion Cause Extraction. paper
Emotion Cause Detection for Chinese Micro-Blogs Based on ECOCC Model. paper
Emotion Cause Detection with Linguistic Constructions. paper
Emotion Cause Extraction, A Challenging Task with Corpus Construction. paper
Extracting Causes of Emotions from Text. paper
基于E-CNN神经网络的情绪原因识别方法. paper
基于序列标注模型的情绪原因识别方法. paper
基于文本的情绪自动归因方法研究. paper
A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness. paper
Emotion-Cause Pair Extraction: A New Task to Emotion Analysis in Texts. paper code
Fine-grained Opinion Mining with Recurrent Neural Networks and Word Embeddings. paper code
Character-based BiLSTM-CRF Incorporating POS and Dictionaries for Chinese Opinion Target Extraction. paper code
A Unified Model for Opinion Target Extraction and Target Sentiment Prediction. paper code
评价对象抽取研究综述. paper
使用深度长短时记忆模型对于评价词和评价对象的联合抽取. paper
基于语义和句法依存特征的评论对象抽取研究. paper
基于条件随机场的评价对象缺省项识别. paper
基于CRFs和领域本体的中文微博评价对象抽取研究. paper
基于微博的情感倾向性分析方法研究. paper
基于迭代两步CRF模型的评价对象与极性抽取研究. paper
基于句法特征的评价对象抽取方法研究. paper
基于层叠CRFs的中文句子评价对象抽取. paper
评价对象及其倾向性的抽取和判别. paper
基于非完备信息系统的评价对象情感聚类. paper
基于CRFs的评价对象抽取特征研究. paper
基于核心句及句法关系的评价对象抽取. paper
面向特定领域的产品评价对象自动识别研究. paper
评价对象抽取及其倾向性分析. paper
Aspect extraction for opinion mining with a deep convolutional neural network. paper
Recursive neural conditional random fields for aspect-based sentiment analysis. paper
Coupled multi-layer attentions for co-extraction of aspect and opinion terms. paper