Figuring out how to fine-tune @huggingface 🤗 Transformers on your own data? I just wrote a tutorial with examples covering seq classification, NER, and Q/A
Each takes you from downloading the data to training in PT or TF
Colab notebook included. Enjoy!
https://t.co/rk4FKMlzuU
Are neural networks learning or memorizing? It must be learning, otherwise how they generalize so well. But hey, maybe memorization is not against generalization, and maybe necessary when there's little training data for some classes. https://t.co/AQ1rqGIP01 by @vitalyFM & Zhang.
People are biased.
Data is biased, in part because people are biased.
Algorithms trained on biased data are biased.
But learning algorithms themselves are not biased.
Bias in data can be fixed.
Bias in people is harder to fix. https://t.co/6pAHiDC6aN
We've trained an AI system to solve the Rubik's Cube with a human-like robot hand.
This is an unprecedented level of dexterity for a robot, and is hard even for humans to do.
The system trains in an imperfect simulation and quickly adapts to reality: https://t.co/O04izt3KvO
Evolution of Representations in the Transformer: blog post on our @emnlp2019 paper is out!
blog post: https://t.co/tknJUpjPF9
paper: https://t.co/bX7fU9qNKs
@lena_voita, @RicoSennrich, @iatitov
New state-of-the-art for object detection on COCO. Liu et al introduce a composite backbone architecture that extracts more representational basic features than the original backbone (trained for image classification). Code & comparisons here: https://t.co/Z1LQoRa2lQ
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