746 Open source projects that are alternatives of or similar to Cookiecutter Spacy Fastapi

R package providing annotators and a normalized data model for natural language processing

spaCy REST API, wrapped in a Docker container.

🔮 A refreshing functional take on deep learning, compatible with your favorite libraries

💫 REST microservices for various spaCy-related tasks

This repository provides everything to get started with Python for Text Mining / Natural Language Processing (NLP)

💥 displaCy.js: An open-source NLP visualiser for the modern web

Python implementation of TextRank for phrase extraction and summarization of text documents

🧬 A JupyterLab extension for annotating data with Prodigy

💫 Industrial-strength Natural Language Processing (NLP) in Python

Learn how to process, classify, cluster, summarize, understand syntax, semantics and sentiment of text data with the power of Python! This repository contains code and datasets used in my book, "Text Analytics with Python" published by Apress/Springer.

🪐 End-to-end NLP workflows from prototype to production

👩‍🏫 Advanced NLP with spaCy: A free online course

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

Named Entity Recognition based on dictionaries

💙 Emoji handling and meta data for spaCy with custom extension attributes

💫 Models for the spaCy Natural Language Processing (NLP) library

🦆 Contextually-keyed word vectors

💥 displaCy-ent.js: An open-source named entity visualiser for the modern web

🏥 Medical Text Mining and Information Extraction with spaCy

👑 spaCy building blocks and visualizers for Streamlit apps

NLP in Python with Deep Learning

📂 Additional lookup tables and data resources for spaCy

💥 Use the latest Stanza (StanfordNLP) research models directly in spaCy

🎀 JavaScript API for spaCy with Python REST API

💫 Scripts, tools and resources for developing spaCy

ADAM - A Question Answering System. Inspired from IBM Watson

💬 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

🏖TagEditor - Annotation tool for spaCy

🍳 Recipes for the Prodigy, our fully scriptable annotation tool

🤹‍♀️ Query spaCy's linguistic annotations using GraphQL

NLP, before and after spaCy

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

Collection of stats, modeling, and data science tools in Python and R.

Basic Utilities for PyTorch Natural Language Processing (NLP)

《机器翻译：基础与模型》肖桐 朱靖波 著 - Machine Translation: Foundations and Models

Attention-based multimodal fusion for sentiment analysis

Simple State-of-the-Art BERT-Based Sentence Classification with Keras / TensorFlow 2. Built with HuggingFace's Transformers.

Mishkal is an arabic text vocalization software

Python library for Natural Language Preprocessing (NLPre)

Data and software for building the ACL Anthology.

Topic Modelling for Humans

📖 A curated list of resources dedicated to Natural Language Processing (NLP)

🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code

Text vectorization tool to outperform TFIDF for classification tasks

A comprehensive list of pytorch related content on github,such as different models,implementations,helper libraries,tutorials etc.

SLING - A natural language frame semantics parser

⚡️A Lightweight NLP Data Loader for All Deep Learning Frameworks in Python

PyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning

spacy-wordnet creates annotations that easily allow the use of wordnet and wordnet domains by using the nltk wordnet interface

State of the Art Natural Language Processing

It is a question-generator model. It takes text and an answer as input and outputs a question.

📅🇨🇳 中国法定节假日数据 自动每日抓取国务院公告

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].

🐍 Turkish Language Stemmer for Python

Repository for paper "SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference"

How to build RNNs and LSTMs from scratch with NumPy.

Entity linking framework

Web scrapping and related analytics using Python tools

本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。