Knockknock🚪✊Knock Knock: Get notified when your training ends with only two additional lines of code
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Good PapersI try my best to keep updated cutting-edge knowledge in Machine Learning/Deep Learning and Natural Language Processing. These are my notes on some good papers
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Dat8General Assembly's 2015 Data Science course in Washington, DC
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Deep LyricsLyrics Generator aka Character-level Language Modeling with Multi-layer LSTM Recurrent Neural Network
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Files2rougeCalculating ROUGE score between two files (line-by-line)
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Mobilenet YoloMobileNetV2-YoloV3-Nano: 0.5BFlops 3MB HUAWEI P40: 6ms/img, YoloFace-500k:0.1Bflops 420KB🔥🔥🔥
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PytextrankPython implementation of TextRank for phrase extraction and summarization of text documents
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FlairA very simple framework for state-of-the-art Natural Language Processing (NLP)
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MedquadMedical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites
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Unified SummarizationOfficial codes for the paper: A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss.
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Lingopackage lingo provides the data structures and algorithms required for natural language processing
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PrenlpPreprocessing Library for Natural Language Processing
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Opus MtOpen neural machine translation models and web services
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DanlpDaNLP is a repository for Natural Language Processing resources for the Danish Language.
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Neuro🔮 Neuro.js is machine learning library for building AI assistants and chat-bots (WIP).
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DialoglueDialoGLUE: A Natural Language Understanding Benchmark for Task-Oriented Dialogue
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Cv resumeA latex cv/resume template.
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Fnc 1 BaselineA baseline implementation for FNC-1
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Cogcomp NlpyCogComp's light-weight Python NLP annotators
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ClicrMachine reading comprehension on clinical case reports
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RbertImplementation of BERT in R
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DeclutrThe corresponding code from our paper "DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations". Do not hesitate to open an issue if you run into any trouble!
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CleanlabThe standard package for machine learning with noisy labels, finding mislabeled data, and uncertainty quantification. Works with most datasets and models.
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Neuraldialog LarlPyTorch implementation of latent space reinforcement learning for E2E dialog published at NAACL 2019. It is released by Tiancheng Zhao (Tony) from Dialog Research Center, LTI, CMU
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TextacyNLP, before and after spaCy
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ScattertextBeautiful visualizations of how language differs among document types.
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PapernotesMy personal notes and surveys on DL, CV and NLP papers.
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