metadata
dataset_info:
features:
- name: text1
dtype: string
- name: text2
dtype: string
- name: label
dtype: float64
- name: source
dtype: string
splits:
- name: train
num_bytes: 293947097
num_examples: 241957
- name: test
num_bytes: 50716064
num_examples: 39359
download_size: 58828058
dataset_size: 344663161
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
task_categories:
- text-classification
language:
- fr
size_categories:
- 1M<n<10M
This is a dataset for training a mixed cross-encoder. The purpose of the cross-encoder is to calculate not only a relevance score between a question and a context (whether the answer to the question can be found in the document or not) but also to calculate a similarity score between two sentences. This dataset is a combination of the PIAF, FQuAD, SQuAD-French, pandora-s-fr, and stsd-fr datasets.