--- dataset_info: features: - name: sentence dtype: string - name: simplification dtype: string - name: dataset dtype: string splits: - name: train num_bytes: 339019973 num_examples: 781801 - name: validation num_bytes: 972654 num_examples: 2385 - name: test num_bytes: 753090 num_examples: 1439 download_size: 218816945 dataset_size: 340745717 configs: - config_name: default data_files: - split: train path: data/train-* - split: validation path: data/validation-* - split: test path: data/test-* language: - fra task_categories: - text-generation pretty_name: frenchSIMPLIFICATION --- # Dataset information **Dataset concatenating Simplification datasets, available in French and open-source.** There are a total of **785,625** rows, of which 781,801 are for training, 2,385 for validation and 1,439 for testing. # Usage ``` from datasets import load_dataset dataset = load_dataset("CATIE-AQ/frenchSIMPLIFICATION") ``` # Dataset ## Details of rows | Dataset Original | Splits | Note | | ----------- | ----------- | ----------- | | [clear](http://natalia.grabar.free.fr/resources.php#remi)| 4,196 train / 300 validation / 100 test | | | [wikilarge](http://natalia.grabar.free.fr/resources.php#remi)| 296,402 train / 992 validation / 359 test | | | [GEM/BiSECT](https://huggingface.co/GEM/BiSECT)| 491,035 train / 2,400 validation / 1,036 test | We keep only the data in French (`fr`) | | [alector](https://alectorsite.wordpress.com/corpus/)| 1,108 train | We cut the 79 original texts into sentences to obtain 1,108 data instead of 79. | ## Removing duplicate data and leaks The sum of the values of the datasets listed here gives the following result: ``` DatasetDict({ train: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 792741 }) validation: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 3692 }) test: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 1495 }) }) ``` However, a data item in training split A may not be in A's test split, but may be present in B's test set, creating a leak when we create the A+B dataset. The same logic applies to duplicate data. So we need to make sure we remove them. After our clean-up, we finally have the following numbers: ``` DatasetDict({ train: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 781801 }) validation: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 2385 }) test: Dataset({ features: ['sentence', 'simplification', 'dataset'], num_rows: 1439 }) }) ``` ## Columns - the `sentence` column contains the text - the `simplification` column contains the simplification of the `sentence` - the `dataset` column identifies the row's original dataset (if you wish to apply filters to it) ## Split - `train` corresponds to the concatenation of `clear` + `wikilarge` + `bisect` + `alector` - `validation` corresponds to the concatenation of `clear` + `wikilarge` + `bisect` - `test` corresponds to `clear` + `wikilarge` + `bisect` # Citations ### Alector ``` @inproceedings{gala-etal-2020-alector, title = "{A}lector: A Parallel Corpus of Simplified {F}rench Texts with Alignments of Misreadings by Poor and Dyslexic Readers", author = {Gala, N{\'u}ria and Tack, Ana{\"\i}s and Javourey-Drevet, Ludivine and Fran{\c{c}}ois, Thomas and Ziegler, Johannes C.}, editor = "Calzolari, Nicoletta and B{\'e}chet, Fr{\'e}d{\'e}ric and Blache, Philippe and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, H{\'e}l{\`e}ne and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios", booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference", month = may, year = "2020", address = "Marseille, France", publisher = "European Language Resources Association", url = "https://aclanthology.org/2020.lrec-1.169", pages = "1353--1361", language = "English", ISBN = "979-10-95546-34-4",} ``` ### BiSECT ``` @inproceedings{bisect2021, title={BiSECT: Learning to Split and Rephrase Sentences with Bitexts}, author={Kim, Joongwon and Maddela, Mounica and Kriz, Reno and Xu, Wei and Callison-Burch, Chris}, booktitle={Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP)}, year={2021}} ``` ### CLEAR ``` @inproceedings{grabar-cardon-2018-clear, title = "{CLEAR} {--} Simple Corpus for Medical {F}rench", author = "Grabar, Natalia and Cardon, R{\'e}mi", editor = {J{\"o}nsson, Arne and Rennes, Evelina and Saggion, Horacio and Stajner, Sanja and Yaneva, Victoria}, booktitle = "Proceedings of the 1st Workshop on Automatic Text Adaptation ({ATA})", month = nov, year = "2018", address = "Tilburg, the Netherlands", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/W18-7002", doi = "10.18653/v1/W18-7002", pages = "3--9", } ``` ### Wikilarge ``` @inproceedings{cardon-grabar-2020-french, title = "{F}rench Biomedical Text Simplification: When Small and Precise Helps", author = "Cardon, R{\'e}mi and Grabar, Natalia", editor = "Scott, Donia and Bel, Nuria and Zong, Chengqing", booktitle = "Proceedings of the 28th International Conference on Computational Linguistics", month = dec, year = "2020", address = "Barcelona, Spain (Online)", publisher = "International Committee on Computational Linguistics", url = "https://aclanthology.org/2020.coling-main.62", doi = "10.18653/v1/2020.coling-main.62", pages = "710--716", } ``` ### frenchSIMPLIFICATION ``` @misc{frenchSIMPLIFICATION_2025, author = { {BOURDOIS, Loïck} }, organization = { {Centre Aquitain des Technologies de l'Information et Electroniques} }, year = 2025, url = { https://huggingface.co/datasets/CATIE-AQ/frenchSIMPLIFICATION }, doi = { 10.57967/hf/7134 }, publisher = { Hugging Face } } ``` # License [cc-by-4.0](https://creativecommons.org/licenses/by/4.0/deed.en)