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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

Transformer-based language models such as BERT ( Bidirectional Transformers for Language Understanding ) have the ability to capture words or sentences within a bigger context of data, and allow for the classification of the news sentiment given the current state of the world. The code can be found on the GitHub repo.

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Building a Sentiment Classification System With BERT Embeddings: Lessons Learned

The MLOps Blog

Sentiment analysis, commonly referred to as opinion mining/sentiment classification, is the technique of identifying and extracting subjective information from source materials using computational linguistics , text analysis , and natural language processing. are used to classify the text sentiment.

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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. University of Szeged. Imperial, Google Research.

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Large Language Models – Technical Overview

Viso.ai

If a computer program is trained on enough data such that it can analyze, understand, and generate responses in natural language and other forms of content, it is called a Large Language Model (LLM). An easy way to describe LLM is an AI algorithm capable of understanding and generating human language.

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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. In Findings of the Association for Computational Linguistics: ACL 2022 , pages 2340–2354, Dublin, Ireland.

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Reward Isn't Free: Supervising Robot Learning with Language and Video from the Web

The Stanford AI Lab Blog

Indeed, this recipe of massive, diverse datasets combined with scalable offline learning algorithms (e.g. Replicating these impressive generalization and adaptation capabilities in robot learning algorithms would certainly be a step toward robots that can be used in unstructured, real world environments. Neumann, M., Gardner, M.,

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The State of Multilingual AI

Sebastian Ruder

Research models such as BERT and T5 have become much more accessible while the latest generation of language and multi-modal models are demonstrating increasingly powerful capabilities. In Findings of the Association for Computational Linguistics: ACL 2022 (pp. RoBERTa: A Robustly Optimized BERT Pretraining Approach.