![]() from transformers import pipelineĬlassifier = pipeline( "text-classification", model = "roberta-large-mnli")Ĭlassifier( "A soccer game with multiple males playing. You can use the □ Transformers library text-classification pipeline to infer with NLI models. Hypothesis: Some men are playing a sport. Premise: Soccer game with multiple males playing. Additional Code Samples - Amazon Textract AWS Documentation Amazon Textract Developer Guide Additional Code Samples PDF RSS The following table provides links to more Amazon Textract code examples. Label: Entailment Inference You can use the Transformers library text-classification pipeline to infer with NLI models. Premise: A man inspects the uniform of a figure in some East Asian country. Label: Contradiction Example 2: Premise: Soccer game with multiple males playing. NLI models have different variants, such as Multi-Genre NLI, Question NLI and Winograd NLI. ![]() The benchmark dataset for this task is GLUE (General Language Understanding Evaluation). neutral, which means there's no relation between the hypothesis and the premise.contraction, which means the hypothesis is false.entailment, which means the hypothesis is true.Concretely, the model takes a premise and a hypothesis and returns a class that can either be: ![]() In NLI the model determines the relationship between two given texts. This can help understand churn and retention by grouping reviews by sentiment, to later analyze the text and make strategic decisions based on this knowledge. You can track the sentiments of your customers from the product reviews using sentiment analysis models. ![]()
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