Create README.md

README.md

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+---
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: qrels/train.jsonl
+      - split: validation
+        path: qrels/validation.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