Difference between revisions of "Bidirectional Encoder Representations from Transformers (BERT)"
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** [[Transformer-XL]] | ** [[Transformer-XL]] | ||
* [http://venturebeat.com/2019/05/16/microsoft-makes-googles-bert-nlp-model-better/ Microsoft makes Google’s BERT NLP model better | Khari Johnson - VentureBeat] | * [http://venturebeat.com/2019/05/16/microsoft-makes-googles-bert-nlp-model-better/ Microsoft makes Google’s BERT NLP model better | Khari Johnson - VentureBeat] | ||
| − | * [[Watch me Build a Finance Startup]] | Siraj Raval | + | * [[Watch me Build a Finance Startup]] | [[Creatives#Siraj Raval|Siraj Raval]] |
* [http://medium.com/huggingface/distilbert-8cf3380435b5 Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT | Victor Sanh - Medium] | * [http://medium.com/huggingface/distilbert-8cf3380435b5 Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT | Victor Sanh - Medium] | ||
* [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. | ||
Revision as of 17:30, 14 September 2020
Youtube search... ...Google search
- Natural Language Processing (NLP)
- Google is improving 10 percent of searches by understanding language context - Say hello to BERT | Dieter Bohn - The Verge ...the old Google search algorithm treated that sentence as a “Bag-of-Words (BoW)”
- Google AI’s ALBERT claims top spot in multiple NLP performance benchmarks | Khari Johnson - VentureBeat
- RoBERTa:
- RoBERTa: A Robustly Optimized BERT Pretraining Approach | Y. Li, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov
- RoBERTa: A Robustly Optimized BERT Pretraining Approach | GitHub - iterates on BERT's pretraining procedure, including training the model longer, with bigger batches over more data; removing the next sentence prediction objective; training on longer sequences; and dynamically changing the masking pattern applied to the training data.
- 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
- Microsoft makes Google’s BERT NLP model better | Khari Johnson - VentureBeat
- Watch me Build a Finance Startup | Siraj Raval
- Smaller, faster, cheaper, lighter: Introducing DistilBERT, a distilled version of BERT | Victor Sanh - Medium
- 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.
- Understanding BERT: Is it a Game Changer in NLP? | Bharat S Raj - Towards Data Science
- Allen Institute for Artificial Intelligence, or AI2’s Aristo AI system finally passes an eighth-grade science test | Alan Boyle - GeekWire
- 7 Leading Language Models for NLP in 2020 | Mariya Yao - Topbots
- SMART - Multi-Task Deep Neural Networks (MT-DNN)
- Deep Distributed Q Network Partial Observability
- TaBERT
BERT Research | Chris McCormick
- BERT Research | Chris McCormick
- ChrisMcCormickAI online education
