Update README.md

README.md

@@ -78,33 +78,36 @@ massive = Dataset.from_datasets("AutoIntent/massive")
 This dataset is taken from `mteb/amazon_massive_intent` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
 
 ```python
+from datasets import Dataset as HFDataset
 from datasets import load_dataset
+
 from autointent import Dataset
+from autointent.schemas import Intent, Sample
+
 
-def convert_massive(massive_train):
-    intent_names = sorted(massive_train.unique("label"))
-    name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))
+def extract_intents_info(split: HFDataset) -> tuple[list[Intent], dict[str, int]]:
+    """Extract metadata."""
+    intent_names = sorted(split.unique("label"))
+    intent_names.remove("cooking_query")
+    intent_names.remove("audio_volume_other")
     n_classes = len(intent_names)
+    name_to_id = dict(zip(intent_names, range(n_classes), strict=False))
+    intents_data = [Intent(id=i, name=intent_names[i]) for i in range(n_classes)]
+    return intents_data, name_to_id
+
+
+def convert_massive(split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
+    """Extract utterances and labels."""
+    return [Sample(utterance=s["text"], label=name_to_id[s["label"]]) for s in split if s["label"] in name_to_id]
+
 
-    classwise_utterance_records = [[] for _ in range(n_classes)]
-    intents = [
-        {
-            "id": i,
-            "name": name,
-            
-        }
-        for i, name in enumerate(intent_names)
-    ]
-
-    for batch in massive_train.iter(batch_size=16, drop_last_batch=False):
-        for txt, name in zip(batch["text"], batch["label"], strict=False):
-            intent_id = name_to_id[name]
-            target_list = classwise_utterance_records[intent_id]
-            target_list.append({"utterance": txt, "label": intent_id})
-
-    utterances = [rec for lst in classwise_utterance_records for rec in lst]
-    return Dataset.from_dict({"intents": intents, "train": utterances})
-
-massive = load_dataset("mteb/amazon_massive_intent", "en")
-massive_converted = convert_massive(massive["train"])
+if __name__ == "__main__":
+    massive = load_dataset("mteb/amazon_massive_intent", "en")
+    intents, name_to_id = extract_intents_info(massive["train"])
+    train_samples = convert_massive(massive["train"], name_to_id)
+    test_samples = convert_massive(massive["test"], name_to_id)
+    validation_samples = convert_massive(massive["validation"], name_to_id)
+    dataset = Dataset.from_dict(
+        {"intents": intents, "train": train_samples, "test": test_samples, "validation": validation_samples}
+    )
 ```