first-contributions
datasets
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first-contributions | datasets | |
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91 | 15 | |
35,317 | 18,376 | |
0.0% | 1.7% | |
-20.8 | 9.5 | |
9 months ago | 7 days ago | |
Python | ||
MIT License | Apache License 2.0 |
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first-contributions
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Merge Mastery: Elevating Your Pull Request Game in Open Source Projects
GitHub's First Contribution guide: A gentle intro to contributing to open-source.
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First Open-Source Contribution
We will contribute to the repository of "First contributions". You can go to the following link: https://github.com/firstcontributions/first-contributions
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What is Open Source & How to contribute to it?
First Contributions, EddieHub Issue Finder, goodfirstissue.dev, goodfirstissues.com, firsttimersonly.com.
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First Contributions: learn how to contribute to open source projects
First Contributions GitHub Repository
- Show HN: Make your first open source contribution in 5 minutes
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Tublian Internship Journey: Navigating the Internship Landscape in Week One
In the inaugural chapters of my Tublian journey, I found myself immersed in the realms of "First Contributions." True to its name, this project served as a welcoming gateway, designed with the noble purpose of simplifying the often complex landscape of contributing to open source endeavors, particularly for those taking their first steps into this vibrant community.
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Learn open-source contribution.
Recently i found a github repository to learn open-source contribution for beginners. Click here to view the repository.
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Hacktoberfest Pledge 🎉
You'll need to find a first contributions repository (I used this one), and from there it'll be pretty self-explanatory. Good luck - you'll do great!
- Show HN: Hands on tutorial for open source contribution
datasets
- 🐍🐍 23 issues to grow yourself as an exceptional open-source Python expert 🧑💻 🥇
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
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How to Train Large Models on Many GPUs?
https://github.com/huggingface/datasets
https://github.com/huggingface/transformers
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[D] Can we use Ray for distributed training on vertex ai ? Can someone provide me examples for the same ? Also which dataframe libraries you guys used for training machine learning models on huge datasets (100 gb+) (because pandas can't handle huge data).
https://huggingface.co/docs/datasets backed with an Arrow file or buffer
- Need help with a data science project
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Is there a text evaluation metric that does not need reference text?
I'm looking for an automatic evaluation metric that can score the first text higher (since it's more grammatically correct/better for other reasons). All the metrics for NLG I found require some reference text to match the generated text with, which I don't have.
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FauxPilot – an open-source GitHub Copilot server
And then pass that my_code.json as the dataset name.
[1] https://github.com/huggingface/datasets
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Hugging Face Introduces ‘Datasets’: A Lightweight Community Library For Natural Language Processing (NLP)
Code for https://arxiv.org/abs/2109.02846 found: https://github.com/huggingface/datasets
Quick Read | Paper | Github
- Datasets: A Community Library for Natural Language Processing
What are some alternatives?
CodeTriage - Discover the best way to get started contributing to Open Source projects
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
awesome-for-beginners - A list of awesome beginners-friendly projects.
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
good-first-issue - Make your first open-source contribution.
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svelteui - SvelteUI Monorepo
edex-ui - A cross-platform, customizable science fiction terminal emulator with advanced monitoring & touchscreen support.
Blitz - ⚡️ The Missing Fullstack Toolkit for Next.js
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up-for-grabs.net - This is a list of projects which have curated tasks specifically for new contributors. These issues are a great way to get started with a project, or to help share the load of working on open source projects. Jump in!
evaluate - 🤗 Evaluate: A library for easily evaluating machine learning models and datasets.