Based on 125GB of data leaked from Twitch, you can see their monthly revenues from 2019-2021

Last update: Nov 11, 2021

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Overview

Twitch Revenues

Bu script'i kullanarak istediğiniz yayıncıların, Twitch'den sızdırılan 125 GB'lik veriye dayanarak, 2019-2021 arası aylık gelirlerini görebilirsiniz.

Kullanmak için yapılması gerekenler:

Bilgisayarınızda Python kurulu olduğuna emin olun. Kurulu değil ise buradan indirebilirsiniz.
Twitch'ten sızan datayı indirmeniz gerekmektedir.
2019/2020/2021 ve onların içerisindeki 06/07 gibi isimlendirilmiş klasörlerdeki .rar dosyalarını çıkartmanız gerekmektedir.

Yukarıdaki dosyaları indirin ve Twitch datası içerisinde twitch-payouts/all_revenues klasörüne atın.
install dosyasını çalıştırın
program dosyasına çift tıklayarak programı çalıştırın.

Twitch ID bulmak için bu siteye gidebilirsiniz.

Owner

GitHub Repository

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Based on 125GB of data leaked from Twitch, you can see their monthly revenues from 2019-2021

Related tags

Overview

Twitch Revenues

Kullanmak için yapılması gerekenler:

Owner

Fast, general, and tested differentiable structured prediction in PyTorch

Tools for curating biomedical training data for large-scale language modeling

Ptorch NLU, a Chinese text classification and sequence annotation toolkit, supports multi class and multi label classification tasks of Chinese long text and short text, and supports sequence annotation tasks such as Chinese named entity recognition, part of speech tagging and word segmentation.

A PyTorch-based model pruning toolkit for pre-trained language models

This repository contains the code for "Exploiting Cloze Questions for Few-Shot Text Classification and Natural Language Inference"

TFIDF-based QA system for AIO2 competition

A design of MIDI language for music generation task, specifically for Natural Language Processing (NLP) models.

MEDIALpy: MEDIcal Abbreviations Lookup in Python

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Practical Machine Learning with Python

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Findings of ACL 2021

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Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition