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, 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:
{ "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:
{ "doc_id": "Unique identifier for each document", "doc": "Text content of the document" }