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104 - Model Distillation, with Victor Sanh and Thomas Wolf

31:22
 
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Manage episode 252392329 series 1452120
Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode we talked with Victor Sanh and Thomas Wolf from HuggingFace about model distillation, and DistilBERT as one example of distillation. The idea behind model distillation is compressing a large model by building a smaller model, with much fewer parameters, that approximates the output distribution of the original model, typically for increased efficiency. We discussed how model distillation was typically done previously, and then focused on the specifics of DistilBERT, including training objective, empirical results, ablations etc. We finally discussed what kinds of information you might lose when doing model distillation.
  continue reading

145 episod

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iconKongsi
 
Manage episode 252392329 series 1452120
Kandungan disediakan oleh NLP Highlights and Allen Institute for Artificial Intelligence. Semua kandungan podcast termasuk episod, grafik dan perihalan podcast dimuat naik dan disediakan terus oleh NLP Highlights and Allen Institute for Artificial Intelligence atau rakan kongsi platform podcast mereka. Jika anda percaya seseorang menggunakan karya berhak cipta anda tanpa kebenaran anda, anda boleh mengikuti proses yang digariskan di sini https://ms.player.fm/legal.
In this episode we talked with Victor Sanh and Thomas Wolf from HuggingFace about model distillation, and DistilBERT as one example of distillation. The idea behind model distillation is compressing a large model by building a smaller model, with much fewer parameters, that approximates the output distribution of the original model, typically for increased efficiency. We discussed how model distillation was typically done previously, and then focused on the specifics of DistilBERT, including training objective, empirical results, ablations etc. We finally discussed what kinds of information you might lose when doing model distillation.
  continue reading

145 episod

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