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Accelerate hyperparameter grid search for sentiment analysis with BERT models using Weights & Biases, Amazon EKS, and TorchElastic

AWS Machine Learning Blog

He spent 10 years as Head of Morgan Stanley’s Algorithmic Trading Division in San Francisco. Ana has had several leadership roles at startups and large corporations such as Intel and eBay, leading ML inference and linguistics related products. He is an Expert Advisor of Digital Technologies for Circular Economy with United Nations.

BERT 98
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68 Summaries of Machine Learning and NLP Research

Marek Rei

Linguistic Parameters of Spontaneous Speech for Identifying Mild Cognitive Impairment and Alzheimer Disease Veronika Vincze, Martina Katalin Szabó, Ildikó Hoffmann, László Tóth, Magdolna Pákáski, János Kálmán, Gábor Gosztolya. Computational Linguistics 2022. Additive embeddings are used for representing metadata about each note.

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All Languages Are NOT Created (Tokenized) Equal

Topbots

70% of research papers published in a computational linguistics conference only evaluated English.[ A comprehensive explanation of the BPE algorithm can be found on the HuggingFace Transformers course. I additionally use metadata from The World Atlas of Language Structures to obtain information such as language family (e.g.

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Modular Deep Learning

Sebastian Ruder

d) Hypernetwork: A small separate neural network generates modular parameters conditioned on metadata.  Instead of learning module parameters directly, they can be generated using an auxiliary model (a hypernetwork) conditioned on additional information and metadata. Parameter composition.  We Module parameter generation.  Instead

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The State of Multilingual AI

Sebastian Ruder

Initiatives   The Association for Computational Linguistics (ACL) has emphasized the importance of language diversity, with a special theme track at the main ACL 2022 conference on this topic. Writing System and Speaker Metadata for 2,800+ Language Varieties. Computational linguistics, 47(2), 255-308.

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ACL 2022 Highlights

Sebastian Ruder

The initiative focuses on making Computational Linguistics (CL) research accessible in 60 languages and across all modalities, including text/speech/sign language translation, closed captioning, and dubbing. We can also incorporate additional knowledge by modifying the training data, e.g., by inserting metadata strings (e.g.,

NLP 52