Aalto's three-class sentiment set of English financial-news sentences; HELM generates a label and reports weighted F1 on a 70/30 split.
unassessed
| Category | domain |
|---|---|
| Subcategory | three-class investor-view sentiment on English financial news sentences |
| Page status | unknown |
| Metric | classification_weighted_f1 |
| Direction | higher_is_better |
| Dataset size | 4846 |
| Dataset licence | CC-BY-NC-SA-3.0 |
| Publisher | Aalto University School of Business |
Financial PhraseBank asks whether an English sentence from financial news or a company release is positive, negative, or neutral from an investor's view of the stock, using only the sentence. Sentiment that is not about financial impact is labelled neutral. It is single-turn text classification, not numerical QA. Not [financebench](financebench.md), not [fin_qa](fin_qa.md), and not [financeiq](financeiq.md).
HELM generates a label string after instructions that list positive, neutral, and negative. The scenario shuffles the chosen agreement file with seed 121 and cuts a 70/30 train/test split. Original work has no canonical train/test split; Hugging Face exposes four agreement configs as a single train split each.
No model card in ModelSpec reports this benchmark yet.