Malaya Natural Language Toolkit for bahasa Malaysia, https://malaya.readthedocs.io/
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KeitaMy personal toolkit for PyTorch development.
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Dialog corpus用于训练中英文对话系统的语料库 Datasets for Training Chatbot System
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Textblob ArArabic support for textblob
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Colibri CoreColibri core is an NLP tool as well as a C++ and Python library for working with basic linguistic constructions such as n-grams and skipgrams (i.e patterns with one or more gaps, either of fixed or dynamic size) in a quick and memory-efficient way. At the core is the tool ``colibri-patternmodeller`` whi ch allows you to build, view, manipulate and query pattern models.
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PynlplPyNLPl, pronounced as 'pineapple', is a Python library for Natural Language Processing. It contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. There are also more complex data types and algorithms. Moreover, there are parsers for file formats common in NLP (e.g. FoLiA/Giza/Moses/ARPA/Timbl/CQL). There are also clients to interface with various NLP specific servers. PyNLPl most notably features a very extensive library for working with FoLiA XML (Format for Linguistic Annotation).
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Dialogue UnderstandingThis repository contains PyTorch implementation for the baseline models from the paper Utterance-level Dialogue Understanding: An Empirical Study
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Mams For AbsaA Multi-Aspect Multi-Sentiment Dataset for aspect-based sentiment analysis.
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Python QuickuiScientific One-Liner Interactive GUI Library
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HttpimportModule for remote in-memory Python package/module loading through HTTP/S
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Quickdraw AppendixDataset of 25k penises: an appendix to the Quick, Draw! Dataset
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Ndashyour npm dashboard! (react-native app)
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Visdial RlPyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning
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Maskedface NetMaskedFace-Net is a dataset of human faces with a correctly and incorrectly worn mask based on the dataset Flickr-Faces-HQ (FFHQ).
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Php Ip ToolsUseful tools for IP manipulations
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Crf Layer On The Top Of BilstmThe CRF Layer was implemented by using Chainer 2.0. Please see more details here: https://createmomo.github.io/2017/09/12/CRF_Layer_on_the_Top_of_BiLSTM_1/
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Omr DatasetsCollection of datasets used for Optical Music Recognition
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Gta Im Dataset[ECCV-20] 3D human scene interaction dataset: https://people.eecs.berkeley.edu/~zhecao/hmp/index.html
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DegateOpen source software for chip reverse engineering.
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Speech signal processing and classificationFront-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].
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Spacymoji💙 Emoji handling and meta data for spaCy with custom extension attributes
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