Difference between revisions of "Bidirectional Encoder Representations from Transformers (BERT)"
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* [http://arxiv.org/abs/1909.10351 TinyBERT: Distilling BERT for Natural Language Understanding | X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu] researchers at Huawei produces a model called TinyBERT that is 7.5 times smaller and nearly 10 times faster than the original. It also reaches nearly the same language understanding performance as the original. | * [http://arxiv.org/abs/1909.10351 TinyBERT: Distilling BERT for Natural Language Understanding | X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu] researchers at Huawei produces a model called TinyBERT that is 7.5 times smaller and nearly 10 times faster than the original. It also reaches nearly the same language understanding performance as the original. | ||
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Revision as of 20:48, 4 October 2019
Youtube search... ...Google search
- Google AI’s ALBERT claims top spot in multiple NLP performance benchmarks | Khari Johnson - VentureBeat
- Facebook AI’s RoBERTa improves Google’s BERT pretraining methods | Khari Johnson - VentureBeat
- Google's BERT - built on ideas from ULMFiT, ELMo, and OpenAI
- Attention Mechanism/Transformer Model
- Natural Language Processing (NLP)
- Microsoft makes Google’s BERT NLP model better | Khari Johnson - VentureBeat
- Watch me Build a Finance Startup | Siraj Raval
- TinyBERT: Distilling BERT for Natural Language Understanding | X. Jiao, Y. Yin, L. Shang, X. Jiang, X. Chen, L. Li, F. Wang, and Q. Liu researchers at Huawei produces a model called TinyBERT that is 7.5 times smaller and nearly 10 times faster than the original. It also reaches nearly the same language understanding performance as the original.
