Weights & Biases
Clem explains the virtuous cycles behind the creation and success of Hugging Face, and shares his thoughts on where NLP is heading.
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Clément Delangue is co-founder and CEO of Hugging Face, the AI community building the future. Hugging Face started as an open source NLP library and has quickly grown into a commercial product used by over 5,000 companies.
Connect with Clem:
📍 Twitter: https://twitter.com/ClementDelangue
📍 LinkedIn: https://www.linkedin.com/in/clementdelangue/
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🌟 Transcript: http://wandb.me/gd-clement-delangue 🌟
⏳ Timestamps:
0:00 Sneak peek and intro
0:56 What is Hugging Face?
4:15 The success of Hugging Face Transformers
7:53 Open source and virtuous cycles
10:37 Working with both TensorFlow and PyTorch
13:20 The “Write With Transformer” project
14:36 Transfer learning in NLP
16:43 BERT and DistilBERT
22:33 GPT
26:32 The power of the open source community
29:40 Current applications of NLP
35:15 The Turing Test and conversational AI
41:19 Why speech is an upcoming field within NLP
43:44 The human challenges of machine learning
Links Discussed:
📍 Write With Transformer, Hugging Face Transformer’s text generation demo: https://transformer.huggingface.co/
📍 “Attention Is All You Need” (Vaswani et al., 2017): https://arxiv.org/abs/1706.03762
📍 EleutherAI and GPT-Neo: https://github.com/EleutherAI/gpt-neo]
📍 Rasa, open source conversational AI: https://rasa.com/
📍 Roblox article on BERT: https://blog.roblox.com/2020/05/scaled-bert-serve-1-billion-daily-requests-cpus/
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Love these interviews!
Does anyone know the article Clément was referring to at the end? I'd love to read it. I can't find anything by coinbase about Bert or Distilbert, but I remember there was an interesting blog post by Roblox a year ago about using distilbert in production.
Thanks a lot for these videos. Very interesting topic, especially at the end where Clément mentions speech – spoken language is different from written language in various regards. Current models are still mainly trained on written language. Incorporating spoken language probably offers huge potentials for conversational applications, both in production and understanding of language.