Remove Computational Linguistics Remove Explainability Remove Natural Language Processing
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Natural Language Processing with R

Heartbeat

Source: Author The field of natural language processing (NLP), which studies how computer science and human communication interact, is rapidly growing. By enabling robots to comprehend, interpret, and produce natural language, NLP opens up a world of research and application possibilities.

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SQuARE: Towards Multi-Domain and Few-Shot Collaborating Question Answering Agents

ODSC - Open Data Science

Do you yearn to compare different QA models but dread the time-consuming process of setting them up? Are you curious about explainability methods like saliency maps but feel lost about where to begin? Question Answering is the task in Natural Language Processing that involves answering questions posed in natural language.

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68 Summaries of Machine Learning and NLP Research

Marek Rei

I have written short summaries of 68 different research papers published in the areas of Machine Learning and Natural Language Processing. Interpreting Language Models with Contrastive Explanations Kayo Yin, Graham Neubig. Explaining black box text modules in natural language with language models Chandan Singh, Aliyah R.

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A Gentle Introduction to GPTs

Mlearning.ai

You don’t need to have a PhD to understand the billion parameter language model GPT is a general-purpose natural language processing model that revolutionized the landscape of AI. GPT-3 is a autoregressive language model created by OpenAI, released in 2020 . What is GPT-3?

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Sentiment Analysis With SparkNLP and Comet

Heartbeat

Picture by Anna Nekrashevich , Pexels.com Introduction Sentiment analysis is a natural language processing technique which identifies and extracts subjective information from source materials using computational linguistics and text analysis. Spark NLP is a natural language processing library built on Apache Spark.

NLP 52
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Instruction fine-tuning for FLAN T5 XL with Amazon SageMaker Jumpstart

AWS Machine Learning Blog

Amazon EBS is well suited to both database-style applications that rely on random reads and writes, and to throughput-intensive applications that perform long, continuous reads and writes. """, """ Amazon Comprehend uses natural language processing (NLP) to extract insights about the content of documents.

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Explosion in 2019: Our Year in Review

Explosion

Among other things, Ines discussed fast.ai ’s new course on Natural Language Processing and using Polyaxon for model training and experiment management. ? Adriane is a computational linguist who has been engaged in research since 2005, completing her PhD in 2012.

NLP 52