| 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. |