Create update_dataset.py

update_dataset.py

@@ -0,0 +1,50 @@
+from datasets import load_dataset
+import random
+
+dataset = load_dataset("Jean-Baptiste/wikiner_fr")
+
+
+# Remove duplicated rows in the dataset #####
+
+
+# Remove duplicates in each set
+def remove_duplicates(examples: dict[str, list]) -> list[bool]:
+    seen_sentences = set()
+    res = []
+    for example_tokens in examples['tokens']:
+        sentence = tuple(example_tokens)
+        if sentence not in seen_sentences:
+            res.append(True)
+            seen_sentences.add(sentence)
+        else:
+            res.append(False)
+    print(f"Removed {len(examples['tokens']) - sum(res)} duplicates")
+    return res
+
+
+dataset = dataset.filter(remove_duplicates, batched=True, batch_size=None)
+
+# Remove the duplicates in the train set present in the test set (leakage)
+test_sentences = set(tuple(w) for w in dataset['test']['tokens'])
+dataset['train'] = dataset['train'].filter(
+    lambda examples: [s not in test_sentences for s in [tuple(w) for w in examples['tokens']]],
+    batched=True,
+    batch_size=None
+)
+
+
+# Decapitalize words randomly #####
+
+def decapitalize_tokens(example, probability=0.2):
+    for i, token in enumerate(example['tokens']):
+        if token.istitle() and \
+                i != 0 and \
+                random.random() < probability and \
+                example['ner_tags'][i] != 0:
+            example['tokens'][i] = token.lower()
+    return example
+
+
+dataset_with_mixed_caps = dataset.map(decapitalize_tokens)
+
+dataset_with_mixed_caps.push_to_hub("wikiner_fr_mixed_caps")