Legal Opinion Sentiment Classification (HELM)

HELM three-class task: label a legal-opinion phrase positive, negative, or neutral using Ratnayaka et al. OSF spreadsheets.

Also known as: legal_opinion

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Category	domain
Subcategory	three-way sentiment on English legal-opinion phrases (HELM Enterprise)
Page status	unknown
Metric	quasi_exact_match (schema); run spec also attaches weighted classification F1
Direction	higher_is_better
Unit	%
Dataset size	500
Publisher	University of Moratuwa and University of Colombo (dataset); Stanford CRFM (HELM Enterprise scenario)

What it measures

legal_opinion_sentiment_classification is HELM Enterprise's wrap of the phrase-level sentiment data released with Ratnayaka et al. (arXiv 2011.00318). The model reads one English sentence or fragment from a legal opinion and must answer positive, negative, or neutral. HELM does not score party-specific sentiment or the paper's BERT word-list pipeline. Text only.

Task format

Run spec legal_opinion_sentiment_classification. Scenario class name is legal_opinion. Instructions "Classify the sentences into one of the 3 sentiment categories. Possible labels: positive, neutral, negative." Generation adapter, output noun Label. Train xlsx columns Phrase/Label; test xlsx columns sentence/label.

Models reporting this benchmark

No model card in ModelSpec reports this benchmark yet.

Data

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