1318 Open source projects that are alternatives of or similar to Transformers.jl

Deep Learning based Automatic Speech Recognition with attention for the Nvidia Jetson.

👩‍🏫 Advanced NLP with spaCy: A free online course

Data and software for building the ACL Anthology.

Fully featured implementation of Routing Transformer

This is a JSON driven angular x based dashboard framework that is inspired by JIRA's dashboard implementation and https://github.com/raulgomis/angular-dashboard-framework

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Transformer implementation in PyTorch.

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Basic Utilities for PyTorch Natural Language Processing (NLP)

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

Unidirectional data flow architecture implementation for iOS, macOS, tvOS and watchOS

集成了Tensorflow 2版本的端到端语音识别模型，并且RTF(实时率)在0.1左右/Mandarin State-of-the-art Automatic Speech Recognition in Tensorflow 2

This repository is for my students of Udemy. You can find all lecture codes along with mentioned files for reading in here. So, feel free to clone it and if you have any problem just raise a question.

🐤 The Simplest Redux

Mishkal is an arabic text vocalization software

This is a repository with the code for the ACL 2019 paper "Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned" and the paper "Analyzing Source and Target Contributions to NMT Predictions".

阿里天池首届中文NL2SQL挑战赛top6

Python library for Natural Language Preprocessing (NLPre)

R client for the Google Translation API, Google Cloud Natural Language API and Google Cloud Speech API

Machine learning models to automatically summarise scientific papers

A privacy preserving NLP framework

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互联网大厂面试经验

This repository contains landmark research papers in Natural Language Processing that came out in this century.

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

Worth-reading papers and related awesome resources on aspect-based sentiment analysis (ABSA). 值得一读的方面级情感分析论文与相关资源集合

InfluxDB 2.0 python client

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

Go Event Sourcing made easy

Official Python client for the MonkeyLearn API. Build and consume machine learning models for language processing from your Python apps.

SLING - A natural language frame semantics parser

Code for the ACL 2018 paper "Neural Document Summarization by Jointly Learning to Score and Select Sentences"

Pre-Trained Models for ToD-BERT

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Transformer with Untied Positional Encoding (TUPE). Code of paper "Rethinking Positional Encoding in Language Pre-training". Improve existing models like BERT.

Summarization, translation, sentiment-analysis, text-generation and more at blazing speed using a T5 version implemented in ONNX.

📅🇨🇳 中国法定节假日数据 自动每日抓取国务院公告

Stanford NLP group's shared Python tools.

🗃 Offload your store management to a worker easily.

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

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 research：基于tensorflow的nlp深度学习项目，支持文本分类/句子匹配/序列标注/文本生成 四大任务

📚Survey of previous research and related works on machine learning (especially Deep Learning) in Japanese

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

Data augmentation for NLP

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.

Vietnamese NLP Toolkit for Node

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How to build RNNs and LSTMs from scratch with NumPy.

LexPredict ContraxSuite

Code for Learning to select data for transfer learning with Bayesian Optimization