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PythonrougePython wrapper for evaluating summarization quality by ROUGE package
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Cookiecutter Spacy FastapiCookiecutter API for creating Custom Skills for Azure Search using Python and Docker
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FixyAmacımız Türkçe NLP literatüründeki birçok farklı sorunu bir arada çözebilen, eşsiz yaklaşımlar öne süren ve literatürdeki çalışmaların eksiklerini gideren open source bir yazım destekleyicisi/denetleyicisi oluşturmak. Kullanıcıların yazdıkları metinlerdeki yazım yanlışlarını derin öğrenme yaklaşımıyla çözüp aynı zamanda metinlerde anlamsal analizi de gerçekleştirerek bu bağlamda ortaya çıkan yanlışları da fark edip düzeltebilmek.
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Dgcnn.pytorchA PyTorch implementation of Dynamic Graph CNN for Learning on Point Clouds (DGCNN)
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Spark NlpState of the Art Natural Language Processing
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Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
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UdpipeR package for Tokenization, Parts of Speech Tagging, Lemmatization and Dependency Parsing Based on the UDPipe Natural Language Processing Toolkit
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Spacy Course👩🏫 Advanced NLP with spaCy: A free online course
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ImgclsmobSandbox for training deep learning networks
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Tree TransformerImplementation of the paper Tree Transformer
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Bert Sklearna sklearn wrapper for Google's BERT model
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Words countedA Ruby natural language processor.
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VntkVietnamese NLP Toolkit for Node
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DgmDirect Graphical Models (DGM) C++ library, a cross-platform Conditional Random Fields library, which is optimized for parallel computing and includes modules for feature extraction, classification and visualization.
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Nlp profilerA simple NLP library allows profiling datasets with one or more text columns. When given a dataset and a column name containing text data, NLP Profiler will return either high-level insights or low-level/granular statistical information about the text in that column.
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NeusumCode for the ACL 2018 paper "Neural Document Summarization by Jointly Learning to Score and Select Sentences"
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GensimTopic Modelling for Humans
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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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Visdial RlPyTorch code for Learning Cooperative Visual Dialog Agents using Deep Reinforcement Learning
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Sentence SimilarityThis repository contains various ways to calculate sentence vector similarity using NLP models
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