adaptnlp
ML-Workspace
| adaptnlp | ML-Workspace | |
|---|---|---|
| 2 | 7 | |
| 414 | 3,324 | |
| 0.0% | 0.4% | |
| 0.0 | 2.7 | |
| over 2 years ago | 6 months ago | |
| Jupyter Notebook | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
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adaptnlp
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Tools to use for Semantic-searching Question Answering System
Check out adaptnlp
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Case Sensitivity using HuggingFace & Google's T5 model (base)
Yes, there are capitals in the tokenizer vocabulary of t5-base and t5-small, so both support capitalization. A few days ago I was using t5-small through adaptnlp for extractive summarization and capitalization was working fine (https://github.com/Novetta/adaptnlp). AdaptNLP is basically just a transformers wrapper, so if you can't figure out a solution, you could just dissect their source code.
ML-Workspace
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[D] I recently quit my job to start a ML company. Would really appreciate feedback on what we're working on.
Also check out: https://github.com/ml-tooling/ml-workspace, it a nice open source project with lots of packages ready to use.
- ML-Workspace
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Coding for machine learning on Tab S8?
The other option - no reason why you couldn't host something on the desktop machine - web based IDE like R-Studio or Python - have a look at ml-workspace - https://github.com/ml-tooling/ml-workspace that runs in Docker and would provide interfaces for both Python and R, VSCode as well as a GPU accelerated variant for doing Tensorflow etc - either Windows or Linux can support Docker containers (Linux is less trouble apparently - I only have played with it in Linux personally)
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Dynamically spin up VM (based on specific HTTPS request) and stop it once session is over?
It will be a web based IDE dev kit (like Jupyter Hub, or JupyterLab) if you are familiar with them)
- All-in-One Docker Based IDE for Data Science and ML
- Visual Studio Code now available as Web based editor for GitHub repos
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[P] Install or update CUDA, NVIDIA Drivers, Pytorch, Tensorflow, and CuDNN with a single command: Lambda Stack
I'll stick with https://github.com/ml-tooling/ml-workspace, is a docker with all tools installed, also the option of using GPU, so I think is better than only for debian. This way anyone can use it.
What are some alternatives?
Basic-UI-for-GPT-J-6B-with-low-vram - A repository to run gpt-j-6b on low vram machines (4.2 gb minimum vram for 2000 token context, 3.5 gb for 1000 token context). Model loading takes 12gb free ram.
JupyterLab - JupyterLab computational environment.
keytotext - Keywords to Sentences
Gitpod - DEPRECATED since Gitpod 0.5.0; use https://github.com/gitpod-io/gitpod/tree/master/chart and https://github.com/gitpod-io/gitpod/tree/master/install/helm
fastai - The fastai deep learning library
gector - Official implementation of the papers "GECToR – Grammatical Error Correction: Tag, Not Rewrite" (BEA-20) and "Text Simplification by Tagging" (BEA-21)
self-hosted - Sentry, feature-complete and packaged up for low-volume deployments and proofs-of-concept
browser-ml-inference - Edge Inference in Browser with Transformer NLP model
Code-Server - VS Code in the browser
Transformers-Tutorials - This repository contains demos I made with the Transformers library by HuggingFace.
cocalc-docker - DEPRECATED (was -- Docker setup for running CoCalc as downloadable software on your own computer)