MCINext/fever-fa

Dataset Summary

FEVER-Fa is a Persian (Farsi) dataset designed for the Retrieval task. It is a key component of the FaMTEB (Farsi Massive Text Embedding Benchmark) and represents a translated version of the original English FEVER dataset. This dataset is specifically tailored for evaluating models on automatic fact-checking by requiring the retrieval of evidential sentences from a pre-processed Wikipedia corpus that support or refute given claims.

• Language(s): Persian (Farsi)
• Task(s): Retrieval (Fact Checking, Evidence Retrieval)
• Source: Translated from the English FEVER dataset using Google Translate.
• Part of FaMTEB: Yes (specifically, part of the BEIR-Fa collection within FaMTEB)

Supported Tasks and Leaderboards

This dataset is primarily used to evaluate the performance of text embedding models on the Retrieval task. Model performance can be benchmarked and compared on the Persian MTEB Leaderboard on Hugging Face Spaces (filter by language: Persian).

Construction

The FEVER-Fa dataset was created by machine-translating the original English FEVER (Fact Extraction and VERification) dataset into Persian. The translation was performed using the Google Translate API.

As detailed in the "FaMTEB: Massive Text Embedding Benchmark in Persian Language" paper, the quality of the BEIR-Fa collection (of which FEVER-Fa is a part) underwent rigorous evaluation. This included:

1. Comparing BM25 retrieval scores between the original English versions and the translated Persian versions, which showed comparable performance.
2. Utilizing Large Language Models (LLMs) for a direct assessment of translation quality (GEMBA-DA framework), which indicated good overall translation quality, competitive with translations produced by other prominent LLMs.

Data Splits

The data is split into training and test sets as defined in the FaMTEB paper (Table 5):

• Train: 5,556,643 samples
• Development (Dev): 0 samples
• Test: 5,424,495 samples