generation file

generate_split.py

@@ -0,0 +1,19 @@
+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')