Want to know if a 🤗Transformers model has a slow/fast tokenizer or an implementation in your favorite backend? A new table (auto-magically updated) just made its way in the docs! pic.twitter.com/pEj9OIAYbU
— Sylvain Gugger (@GuggerSylvain) November 25, 2020
Any JAX/Flax lovers out there? Ever wanted to use 🤗 Transformers with all the awesome features of JAX? Well you're in luck! 😍
— Hugging Face (@huggingface) November 24, 2020
We've worked with the Google Flax team to enable support for BERT and RoBERTa! 🚀
Check it out 👇https://t.co/zdNUqabOLw pic.twitter.com/qMakbVzbxj
Can't believe that watermark is already 6 yrs old! Just pushed the yearly update :). Lots of clean-up under the hood thx to kind contribs! Beyond its intended purpose, I guess it may now also serve as a nice template for magic func & Python package dev ;) https://t.co/mcWXveqldQ pic.twitter.com/xzWnVXnuNa
— Sebastian Raschka (@rasbt) November 24, 2020
You train data on GPUs. You visualize data on GPUs. Now you can do #MachineLearning preprocessing too. Keep your end-to-end pipelines all in #GPU memory with @rapidsai's new @scikit_learn-compatible preprocessing support in cuML. https://t.co/kOe6yMo2FW
— RAPIDS AI (@RAPIDSai) November 23, 2020
I pared Python's stdlib HTTP handler down to just 30 lines of code, and made it part of my new (early alpha/experimental) `fastcgi` library. Might be useful for folks who need a high level of control in their small python HTTP servers.https://t.co/FbPKSulUJy pic.twitter.com/i7xUPlbq8j
— Jeremy Howard (@jeremyphoward) November 23, 2020
The powerful mT5 model including all pre-trained models is now part of Transformers.
— Hugging Face (@huggingface) November 20, 2020
mT5 was trained on 101 languages and yields SOTA on many multilingual tasks.
Thanks, @lintingxue, Noah Constant, @ada_rob, @colinraffel for releasing the weights!
➡️ https://t.co/sWdM7VNeLw. pic.twitter.com/mOj9hEkpCG
mvp.css is a nice easy way to quickly make a decent-looking web page, without even needing any class attributes. View the source of this page to see how clean the HTML is.https://t.co/9NCyEEgaRq pic.twitter.com/7YDDx1rIXA
— Jeremy Howard (@jeremyphoward) November 19, 2020
It's official! @RAPIDSai is on @kaggle! 🔥🔥🔥
— Joshua Patterson (@datametrician) November 18, 2020
Let's light up the leaderboard with #GPU solutions! https://t.co/mxEyCoHK0B
The PR for native support of categorical variables in Gradient Boosting has been merged: https://t.co/c1WeTrtZBv See for an example https://t.co/DMUIwCQhB0 awesome work @thomasjpfan @hug_nicolas
— Andreas Mueller (@amuellerml) November 17, 2020
Just made a user-friendly Python library for using our CORAL (COnsistent RAnk LOgits) in @PyTorch. Includes examples showing how to equip deep neural networks (convolutional neural networks and multilayer perceptrons) for ordinal regression: https://t.co/JUqZ7ZYRHN pic.twitter.com/Qkvq18uZ0W
— Sebastian Raschka (@rasbt) November 16, 2020
We've started a great collaboration between @PyTorch and @MLflow, to bring a rich set #MLOps functionality to PyTorch users. We've been working on this with the PyTorch team for a while and we're super excited to release a first wave of integrations today: https://t.co/UnZ6fSOec3
— Matei Zaharia (@matei_zaharia) November 12, 2020
"Deep-learning-in-cloud".Just stumbled about this really useful resource. This list compiles a list of 41 different services for using GPUs for DL! Especially useful for students looking to get into using GPUs for their class projects: https://t.co/VI1JnQ9Z9O pic.twitter.com/xNyNGBrjmg
— Sebastian Raschka (@rasbt) November 11, 2020
