create-tf-app
datasets
| create-tf-app | datasets | |
|---|---|---|
| 3 | 15 | |
| 4 | 18,647 | |
| - | 1.3% | |
| 10.0 | 9.5 | |
| over 1 year ago | 1 day ago | |
| Python | Python | |
| MIT License | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
create-tf-app
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[P] create-tf-app: Set up and maintain a machine learning project with a single script.
Check it out on: https://github.com/radi-cho/create-tf-app/ I am open to feedback and discussions. Contributions are also appreciated.
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create-tf-app: TensorFlow template + shell script to manage environments and initialize projects
Hello, I am currently setting up a simple TensorFlow template + shell script to manage environments and initialize projects. WIP: https://github.com/radi-cho/create-tf-app. I wanted to survey the community on whether such a tool would be useful and if you can provide feedback on the implementation:)
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?
Keras - Deep Learning for humans
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
d2l-en - Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.
datumaro - Dataset Management Framework, a Python library and a CLI tool to build, analyze and manage Computer Vision datasets.
Ray - Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
cypress-realworld-app - A payment application to demonstrate real-world usage of Cypress testing methods, patterns, and workflows.
data-science-ipython-notebooks - Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
edex-ui - A cross-platform, customizable science fiction terminal emulator with advanced monitoring & touchscreen support.
first-contributions - 🚀✨ Help beginners to contribute to open source projects
frankmocap - A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator
evaluate - 🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
starter-workflows - Accelerating new GitHub Actions workflows