852 Open source projects that are alternatives of or similar to Spacy Models

🖋️ Fast and safe spellchecking C++ library

Statistics and accepted paper list of NLP conferences with arXiv link

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Huggingface Transformers + Adapters = ❤️

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读过的CV方向的一些论文，图像生成文字、弱监督分割等

OpenGL C++ Graphics Engine

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

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Natural Language Processing using NLTK and Spacy

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spaCy plugin for Transformers , Udify, ELmo, etc.

TensorFlow implementation of Hierarchical Attention Networks for Document Classification and some extension

Fuzzy matching and more functionality for spaCy.

😡😇 Stanford Sentiment Treebank loader in Python

This repositary is a combination of different resources lying scattered all over the internet. The reason for making such an repositary is to combine all the valuable resources in a sequential manner, so that it helps every beginners who are in a search of free and structured learning resource for Data Science. For Constant Updates Follow me in …

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This is a list of open-source projects at Microsoft Research NLP Group

🏇 聊天机器人，自然语言理解，语义理解

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color field for django models with a nice color-picker in the admin. 🎨

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

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Python wrapper for evaluating summarization quality by ROUGE package

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Quiz & Assignment of Coursera

Visualize and explore NLP language models. Ecco creates interactive visualizations directly in Jupyter notebooks explaining the behavior of Transformer-based language models (like GPT2).