| from datasets import load_dataset | |
| dataset = load_dataset("allenai/s2orc", | |
| split="train[:1%]", | |
| num_proc=20) | |
| import spacy | |
| import spacy_fastlang | |
| nlp = spacy.load("en_core_web_sm") | |
| nlp.disable_pipes(nlp.pipe_names) | |
| nlp.add_pipe("language_detector") | |
| def has_abstract(example): | |
| if "paperAbstract" in example.keys() and example["paperAbstract"] is not None \ | |
| and len(example["paperAbstract"].split())>5: | |
| doc = nlp(example["paperAbstract"]) | |
| if doc._.language == 'en' and doc._.language_score >= 0.8: | |
| return True | |
| return False | |
| dataset_sub = dataset.filter(has_abstract) | |
| dataset_sub.push_to_hub("leminda-ai/s2orc_small",split='train',token='XXXXXXXXXXXX') |