Recent Advances in Language Model Fine-tuning
— Sebastian Ruder (@seb_ruder) February 24, 2021
New blog post that takes a closer look at fine-tuning, the most common way large pre-trained language models are used in practice.https://t.co/A5KYoq5zuw
Recent Advances in Language Model Fine-tuning
— Sebastian Ruder (@seb_ruder) February 24, 2021
New blog post that takes a closer look at fine-tuning, the most common way large pre-trained language models are used in practice.https://t.co/A5KYoq5zuw
I made a silly game: try to guess if a C/C++ code snippet is real or GPT2-generated: https://t.co/Rgfk7Hw0h0
— Brendan Dolan-Gavitt (@moyix) February 23, 2021
The new SOTA is in Transformers! DeBERTa-v2 beats the human baseline on SuperGLUE and up to a crazy 91.7% dev accuracy on MNLI task.
— Hugging Face (@huggingface) February 22, 2021
Beats T5 while 10x smaller!
DeBERTa-v2 contributed by @Pengcheng2020 from @MSFTResearch
Try it directly on the hub: https://t.co/HhlL5WrJxp pic.twitter.com/fcUUCiKE0z
Interesting analysis by @mhmazur. Human work is driven by clear goals and is informed by task-specific context. A model that is optimized for generating plausible-sounding text, ignoring goals and context, virtually never produces any useful answer (unless by random chance). https://t.co/QPzapZgale
— François Chollet (@fchollet) February 19, 2021
🚨 NEW MODEL ALERT 🚨
— Hugging Face (@huggingface) February 17, 2021
Translate text to, or between 50 languages with mBART-50 from @facebookai !
🇺🇳 One-to-Many model: translate from English to 49 other languages
↔️ Many-to-Many model: translation btw any pair of 50 languages pic.twitter.com/qC5rEaSrfZ
2021 version of CMU "Neural Networks for NLP" slides (https://t.co/X2rd0eHNiW) and videos (https://t.co/6Yi2cZmOLE) are being posted in real time! Check it out for a comprehensive graduate-level class on NLP! New this year: assignment on implementing parts of your own NN toolkit. pic.twitter.com/kpyA1nQAE5
— Graham Neubig (@gneubig) February 15, 2021
Fascinating new dataset on Kaggle: posts from the subreddit ‘WallStreetBets’. https://t.co/6hFzsifDc3
— Kaggle (@kaggle) February 10, 2021
How to optimize @huggingface models with @weights_biases https://t.co/fmeaziPIFe
— Pete Skomoroch (@peteskomoroch) February 9, 2021
🚨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
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
If you want to find out what the new @spacy_io v3 is all about, check out this video I recorded with @honnibal 😇 We're walking you through some of the most exciting new features!
— Ines Montani 〰️ (@_inesmontani) February 1, 2021
📺 Watch it here: https://t.co/y0UYh7py3d pic.twitter.com/6HpnvmrAco