|
|
--- |
|
|
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 |
|
|
|
