Update README.md

README.md

@@ -73,34 +73,44 @@ snips = Dataset.from_datasets("AutoIntent/snips")
 This dataset is taken from `benayas/snips` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
 
 ```python
-# define util
+"""Convert snips dataset to autointent internal format and scheme."""  # noqa: INP001
+
+from datasets import Dataset as HFDataset
 from datasets import load_dataset
+
 from autointent import Dataset
+from autointent.schemas import Intent, Sample
+
 
-def convert_snips(snips_train):
-    intent_names = sorted(snips_train.unique("category"))
+def _extract_intents_data(split: HFDataset) -> tuple[dict[str, int], list[Intent]]:
+    intent_names = sorted(split.unique("category"))
     name_to_id = dict(zip(intent_names, range(len(intent_names)), strict=False))
-    n_classes = len(intent_names)
-
-    classwise_utterance_records = [[] for _ in range(n_classes)]
-    intents = [
-        {
-            "id": i,
-            "name": name,
-        }
-        for i, name in enumerate(intent_names)
-    ]
-
-    for batch in snips_train.iter(batch_size=16, drop_last_batch=False):
+
+    return name_to_id, [Intent(id=i, name=name) for i, name in enumerate(intent_names)]
+
+
+def convert_snips(split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
+    """Convert one split into desired format."""
+    n_classes = len(name_to_id)
+
+    classwise_samples = [[] for _ in range(n_classes)]
+
+    for batch in split.iter(batch_size=16, drop_last_batch=False):
         for txt, name in zip(batch["text"], batch["category"], strict=False):
             intent_id = name_to_id[name]
-            target_list = classwise_utterance_records[intent_id]
+            target_list = classwise_samples[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})
+    return [Sample(**sample) for samples_from_one_class in classwise_samples for sample in samples_from_one_class]
+
+
+if __name__ == "__main__":
+    snips = load_dataset("benayas/snips")
+
+    name_to_id, intents_data = _extract_intents_data(snips["train"])
+
+    train_samples = convert_snips(snips["train"], name_to_id)
+    test_samples = convert_snips(snips["test"], name_to_id)
 
-# load and format
-snips = load_dataset("benayas/snips")
-snips_converted = convert_snips(snips["train"])
+    dataset = Dataset.from_dict({"train": train_samples, "test": test_samples, "intents": intents_data})
 ```