Natural Language Inference

Natural Language Inference (NLI), also known as Recognizing Textual Entailment (RTE), is a sequence classification problem, where given two (short, ordered) documents -- premise and hypothesis, the task is to determine the inference relation between them.

Samples are classified into one of the three labels depending on whether a hypothesis is true (entailment), false (contradiction), or undetermined (neutral) given a premise. Here's an example:

Premise: A man inspects the uniform of a figure in some East Asian country.
Hypothesis: The man is sleeping.
Label: contradiction

Premise: An older and younger man smiling.
Hypothesis: Two men are smiling and laughing at the cats playing on the floor.
Label: neutral

Premise: A soccer game with multiple males playing.
Hypothesis: Some men are playing a sport.
Label: entailment

Note: For NLI you must combine the premise and hypothesis documents for logging. We recommend joining the document text with a separator such as <> to help visualization in the Galileo console.

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Watch our NLI tutorials ๐Ÿ“ผ

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  • HuggingFace ๐Ÿค—

  • PyTorch

  • TensorFlow

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