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A Guide to 400+ Categorized Large Language Model(LLM) Datasets

Analytics Vidhya

But what if I tell you there’s a goldmine: a repository packed with over 400+ datasets, meticulously categorised across five essential dimensions—Pre-training Corpora, Fine-tuning Instruction Datasets, Preference Datasets, Evaluation Datasets, and Traditional NLP Datasets and more?

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Build Text Categorization Model with Spark NLP

Analytics Vidhya

Overview Setting up John Snow labs Spark-NLP on AWS EMR and using the library to perform a simple text categorization of BBC articles. The post Build Text Categorization Model with Spark NLP appeared first on Analytics Vidhya. Introduction.

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20 GitHub Repositories to Master Natural Language Processing (NLP)

Marktechpost

Natural Language Processing (NLP) is a rapidly growing field that deals with the interaction between computers and human language. As NLP continues to advance, there is a growing need for skilled professionals to develop innovative solutions for various applications, such as chatbots, sentiment analysis, and machine translation.

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NLP Rise with Transformer Models | A Comprehensive Analysis of T5, BERT, and GPT

Unite.AI

Natural Language Processing (NLP) has experienced some of the most impactful breakthroughs in recent years, primarily due to the the transformer architecture. The introduction of word embeddings, most notably Word2Vec, was a pivotal moment in NLP. One-hot encoding is a prime example of this limitation.

BERT 298
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Weak supervision for non-categorical applications + superalignment

Snorkel AI

Extending weak supervision to non-categorical problems Our research presented in our paper “ Universalizing Weak Supervision ” aimed to extend weak supervision beyond its traditional categorical boundaries to more complex, non-categorical problems where rigid categorization isn’t practical.

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Legal NLP Releases Law Stack Exchange Classifier, Subpoena NER and more

John Snow Labs

The latest version of Legal NLP comes with a new classification model on Law Stack Exchange questions and Named-Entity Recognition on Subpoenas. setOutputCol("class") ) With the model, questions can be categorized. For example, the following text is categorized by the model as belonging to the copyright category.

NLP 98
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Accelerating scope 3 emissions accounting: LLMs to the rescue

IBM Journey to AI blog

This article explores an innovative way to streamline the estimation of Scope 3 GHG emissions leveraging AI and Large Language Models (LLMs) to help categorize financial transaction data to align with spend-based emissions factors. Why are Scope 3 emissions difficult to calculate?

ESG 273