| | --- |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - 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 |
| |
|
| | **DBPedia-Fa** is a Persian (Farsi) dataset tailored for the **Retrieval** task, focusing on **entity retrieval**. It is a translated version of the English DBpedia dataset used in the **BEIR benchmark**, and a key 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 (Entity Retrieval) |
| | - **Source:** Translated from the English DBpedia dataset used in BEIR |
| | - **Part of FaMTEB:** Yes — under BEIR-Fa |
| |
|
| | ## Supported Tasks and Leaderboards |
| |
|
| | The dataset is used to evaluate **text embedding models** on their ability to retrieve **structured entity information** in response to **heterogeneous queries**. Results are benchmarked on the **Persian MTEB Leaderboard** on Hugging Face Spaces (language filter: Persian). |
| |
|
| | ## Construction |
| |
|
| | The dataset was created by: |
| |
|
| | - Translating an English DBpedia entity retrieval dataset into Persian using the **Google Translate API** |
| | - Retaining the structure of the original **knowledge base entity retrieval task**, where the goal is to return accurate entities from DBpedia based on various query types |
| |
|
| | According to the *FaMTEB* paper, the **BEIR-Fa datasets** underwent: |
| |
|
| | - **BM25 retrieval score comparisons** with English counterparts |
| | - **LLM-based evaluation** using the **GEMBA-DA framework** to confirm **high translation quality** for retrieval tasks |
| |
|
| | ## Data Splits |
| |
|
| | As reported in the FaMTEB paper (Table 5): |
| |
|
| | - **Train:** 0 samples |
| | - **Dev:** 0 samples |
| | - **Test:** 4,651,208 samples |
| |
|
| | **Total:** ~4.69 million examples |
| |
|