Omkar2118/datasets huggingface dataset:
FairTranslate_fr

The FairTranslate Dataset includes **2,418 sentence pairs**, each centered around an occupation, designed to assess gender expression and translation in English-French contexts. Each English sentence appears in **three gender variants** (male, female, inclusive), allowing for direct counterfactual comparisons. This structure supports fairness evaluations and helps analyze how models handle **grammatical gender, inclusive forms**, and **coreference resolution** in translation.

Each example in the dataset is annotated with rich metadata:

- **English**: Sentence involving an occupation, designed to test both explicit and subtle cues of gender.
- **French**: Ground-truth translation faithfully aligned with the intended gender variant.
- **Gender**: Target gender for translation: `male`, `female`, or `inclusive`.
- **Ambiguity**: Level of gender ambiguity in the English source sentence:
  - `ambiguous`: No explicit gender cues.
  - `unambiguous`: Clear pronouns or cues (e.g., “he”, “she”, “they”).
  - `long unambiguous`: Gender resolvable from distant context, testing long-range coreference.
- **Stereotype**: Whether the occupation is `male-stereotyped`, `female-stereotyped`, or `gender-balanced`, based on real-world statistics from [Statbel](https://statbel.fgov.be/).
- **Occupation**: A list of the three gendered French forms for each occupation, e.g., `["infirmier", "infirmière", "infirmier.ière"]`.

Introducing paper: FairTranslate: An English-French Dataset for Gender Bias Evaluation in Machine Translation by Overcoming Gender Binarity