---
dataset_info:
- config_name: Documents
  features:
  - name: doc_id
    dtype: string
  - name: doc
    dtype: string
  splits:
  - name: history
    num_bytes: 508218
    num_examples: 224
  - name: religion
    num_bytes: 302837
    num_examples: 126
  - name: recherche
    num_bytes: 235256
    num_examples: 69
  - name: python
    num_bytes: 660763
    num_examples: 194
  download_size: 952235
  dataset_size: 1707074
- config_name: MOOC_MCQ_Queries
  features:
  - name: query_id
    dtype: string
  - name: query
    dtype: string
  - name: answers
    sequence: string
  - name: distractions
    sequence: string
  - name: relevant_docs
    sequence: string
  splits:
  - name: history
    num_bytes: 13156
    num_examples: 58
  - name: religion
    num_bytes: 52563
    num_examples: 125
  - name: recherche
    num_bytes: 18791
    num_examples: 52
  - name: python
    num_bytes: 29759
    num_examples: 85
  download_size: 80494
  dataset_size: 114269
configs:
- config_name: Documents
  data_files:
  - split: history
    path: Documents/history-*
  - split: religion
    path: Documents/religion-*
  - split: recherche
    path: Documents/recherche-*
  - split: python
    path: Documents/python-*
- config_name: MOOC_MCQ_Queries
  data_files:
  - split: history
    path: MOOC_MCQ_Queries/history-*
  - split: religion
    path: MOOC_MCQ_Queries/religion-*
  - split: recherche
    path: MOOC_MCQ_Queries/recherche-*
  - split: python
    path: MOOC_MCQ_Queries/python-*
---

# Text embedding Datasets

The text embedding datasets consist of several (query, passage) paired datasets aiming for text-embedding model finetuning. These datasets are ideal for developing and testing algorithms in the fields of natural language processing, information retrieval, and similar applications.

## Dataset Details
Each dataset in this collection is structured to facilitate the training and evaluation of text-embedding models. The datasets are diverse, covering multiple domains and formats. They are particularly useful for tasks like semantic search, question-answering systems, and document retrieval.
### [MOOC MCQ Queries]
  The "MOOC MCQ Queries" dataset is derived from [FUN MOOC](https://www.fun-mooc.fr/fr/), an online platform offering a wide range of French courses across various domains. This dataset is uniquely valuable for its high-quality content, manually curated to assist students in understanding course materials better.
#### Content Overview:
- **Language**: French
- **Domains**:
  - History: 57 examples
  - Religion: 125 examples
  - [Other domains to be added]
- **Dataset Description**:
  Each record in the dataset includes the following fields:
  ```json
  {
    "query_id": "Unique identifier for each query",
    "query": "Text of the multiple-choice question (MCQ)",
    "answers": ["List of correct answer choices"],
    "distractions": ["List of incorrect choices"],
    "relevant_docs": ["List of relevant document IDs aiding the answer"]
  }
  ```
- **statistics**:
  | Category       | Num. of Queries | Query Avg. Words | Number of Docs | Short Docs (<375 words) | Long Docs (≥375 words) | Doc Avg. Words |
  |----------------|-----------------|------------------|----------------|-------------------------|------------------------|----------------|
  | history        | 57              | 11.31            | 224            | 147                     | 77                     | 351.79         |
  | religion       | 125             | 15.08            | 126            | 78                      | 48                     | 375.63         |
  | recherche      | 52              | 12.71            | 69             | 20                      | 49                     | 535.00         |
  | python         | 85              | 21.24            | 194            | 27                      | 167                    | 552.60         |

### [Wikitext generated Queries]
  To complete

### [Documents]
  This dataset is an extensive collection of document chunkings or entire document for short texts, designed to complement the MOOC MCQ Queries and other datasets in the collection.

  - **chunking strategies**:
    - MOOC MCQ Queries: documents are chunked according to their natural divisions, like sections or subsections, ensuring that each chunk maintains contextual integrity.
  - **content format**:
    
    ```json
    {
    "doc_id": "Unique identifier for each document",
    "doc": "Text content of the document"
    }
    ```