🚨Transformers is expanding to Speech!🚨
— Hugging Face (@huggingface) February 8, 2021
🤗Transformers v4.3.0 is out and we are excited to welcome @facebookai's Wav2Vec2 as the first Automatic Speech Recognition model to our library!
👉Now, you can transcribe your audio files directly on the hub: https://t.co/ABKmyNnz58 pic.twitter.com/tet3SYgpMz
If you're interested in relation extraction & building NLP pipelines for custom use cases, check out this in-depth video tutorial by @Oxykodit 👇
— Ines Montani 〰️ (@_inesmontani) February 8, 2021
Bonus: a really cool illustrated breakdown of the ML model implementation & how to go from problem definition ➡️ model architecture. https://t.co/00lR46wEDI
If you're building recommender systems, I recommend checking out TFRS: https://t.co/vdOudcXmqH
— François Chollet (@fchollet) February 8, 2021
Just finished a complete tutorial on optimizing @huggingface models with @weights_biases
— Boris Dayma (@borisdayma) February 4, 2021
No extra line of code required 🥳https://t.co/aMyQ6EQyJS
Announcing Observable Inputs! 🎚🎛 Lightweight UI components to help you explore data and build interactive displays. https://t.co/odojmeV2tq
— Mike Bostock (@mbostock) February 4, 2021
Introducing the World Models Library, an open-source, platform-agnostic suite of tasks and tools for examination of world model design and performance in visual model-based reinforcement learning. Learn more and grab the code at https://t.co/l9jKoBWS2V pic.twitter.com/sT6W4m2FDe
— Google AI (@GoogleAI) February 3, 2021
Very cool new @fastdotai callback from @ignaciooguiza: `PredictionDynamics()`, which shows how predictions develop during training. Inspired by @karpathy.https://t.co/csMtCLKlbi pic.twitter.com/CeNfBIxnXC
— Jeremy Howard (@jeremyphoward) February 3, 2021
Learn how to use the Determined AI platform to offload common infrastructure problems such as scaling training and hyperparameter tuning when developing #PyTorch models. https://t.co/tNYbSMZ3pz
— PyTorch (@PyTorch) February 1, 2021
🆕 We've released Stanza v1.2—our Python neural NLP toolkit for PoS, parsing, NER for dozens of human languages. UD 2.7 models, multi-document support, faster tokenization, fix race conditions, fixes “data gap bugs” with tokenization & deps in many las https://t.co/Lm2WdAddTz
— Stanford NLP Group (@stanfordnlp) January 29, 2021
Have you heard about this nbdev thing but wondering what its really like to write code in it?
— Hamel Husain (@HamelHusain) January 27, 2021
Watch me as I live-code a nbdev project from scratch 😆 👇✨
Thanks to @NorthwesternU for hosting the eventhttps://t.co/sOXb2unMGg
GPU accelerated feature engineering on @Kaggle? Yes Please! Check out our latest post showing how you can use NVTabular and @RAPIDSai to speed up workflows by 11x and training of @fastdotai on Kaggle by 2x in this great blog post by Benedikt Schifferer: https://t.co/4FK4xA2Rij
— Even Oldridge (@Even_Oldridge) January 26, 2021
#ipygany 0.5.0 is out!
— Martin Renou (@martinRenou) January 26, 2021
The Python library for scientific visualization in a @ProjectJupyter Notebook, powered by your GPU!
This release adds support for JupyterLab 3 and new colormaps and colorbars.
Repo: https://t.co/L1qkg790Lq
Live docs: https://t.co/OKTgd5tIPj pic.twitter.com/K0mGYCCJRu
