Bart Large Mnli Yahoo Answers

joeddav

bart-large-mnli-yahoo-answers

The bart-large-mnli-yahoo-answers model is a variant of BART (Bidirectional and Auto-Regressive Transformers) that has been fine-tuned on the MultiNLI (Natural Language Inference) and Yahoo Answers datasets. It is designed for zero-shot classification tasks, where the model is capable of predicting the correct class label for a given input, even if it has not been explicitly trained on that particular task or dataset. This model specifically focuses on classifying Yahoo Answers questions into different categories.

Use cases

The bart-large-mnli-yahoo-answers AI model opens up several intriguing possibilities for practical applications. One use case would be in content moderation, where the model could classify user-generated questions and determine their suitability for display or flagging them for review based on the identified category. Additionally, this model could be utilized in building intelligent search engines that can categorize and organize search results more effectively. Another potential application is in question-answering systems, where the model could assist in providing relevant answers by first categorizing the incoming questions. Finally, this model's zero-shot classification capability makes it useful for any scenario where classifying Yahoo Answers questions into various categories is required, without the need for extensive task-specific training. This model could serve as a foundation for developing innovative products, such as intelligent chatbots, recommendation systems, or data analysis tools.

zero-shot-classification

Pricing

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Creator Models

ModelCostRuns
Distilbert Base Uncased Agnews Student$?28
Distilbert Base Uncased Go Emotions Student$?94,421
Xlm Roberta Large Xnli$?26,609

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Overview

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PropertyValue
Creatorjoeddav
Model NameBart Large Mnli Yahoo Answers
Description
Platform did not provide a description for this model.
Tagszero-shot-classification
Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Runs29,584
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Cost per Run$-
Prediction Hardware-
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