---
configs:
  - config_name: default
    data_files:
      - split: train
        path: qrels/train.jsonl
      - split: dev
        path: qrels/dev.jsonl
      - split: test
        path: qrels/test.jsonl
  - config_name: corpus
    data_files:
      - split: corpus
        path: corpus.jsonl
  - config_name: queries
    data_files:
      - split: queries
        path: queries.jsonl
---
## Dataset Summary

**FiQA2018-Fa** is a Persian (Farsi) dataset designed for the **Retrieval** task, specifically targeting **opinion-based question answering** in the **financial domain**. It is a translated version of the original English **FiQA 2018** dataset and a core component of the [FaMTEB (Farsi Massive Text Embedding Benchmark)](https://huggingface.co/spaces/mteb/leaderboard), under the **BEIR-Fa** collection.

- **Language(s):** Persian (Farsi)  
- **Task(s):** Retrieval (Opinion-based Question Answering, Financial QA)  
- **Source:** Translated from the English FiQA 2018 dataset using Google Translate  
- **Part of FaMTEB:** Yes — under BEIR-Fa

## Supported Tasks and Leaderboards

The dataset evaluates **text embedding models** on their ability to retrieve **relevant financial content** in response to **subjective, opinion-based questions**. Results are benchmarked on the **Persian MTEB Leaderboard** on Hugging Face Spaces (language filter: Persian).

## Construction

Steps in dataset creation:

- Translation of the **original English FiQA 2018** dataset (based on StackExchange "Investment" forum posts) using the **Google Translate API**  
- The dataset retains mappings between **user questions** and **relevant opinion-based answers**

As outlined in the *FaMTEB* paper, the BEIR-Fa datasets (including FiQA2018-Fa) underwent:

- **BM25 retrieval comparison** with the original English  
- **Translation quality analysis** using the **GEMBA-DA LLM evaluation framework**

These evaluations confirmed **good translation quality** for retrieval benchmarking.

## Data Splits

According to the FaMTEB paper (Table 5):

- **Train:** 71,804 samples  
- **Dev:** 0 samples  
- **Test:** 59,344 samples  

**Total:** ~131,148 examples