Update README.md

README.md

@@ -70,50 +70,59 @@ banking77 = Dataset.from_datasets("AutoIntent/banking77")
 This dataset is taken from `PolyAI/banking77` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
 
 ```python
-# define utils
-import requests
+"""Convert events dataset to autointent internal format and scheme."""
+
 import json
-from autointent import Dataset
+
+import requests
+from datasets import Dataset as HFDataset
 from datasets import load_dataset
 
-def load_json_from_url(github_file: str):
-    raw_text = requests.get(github_file).text
-    return json.loads(raw_text)
-
-def convert_banking77(banking77_train, shots_per_intent, intent_names):
-    all_labels = sorted(banking77_train.unique("label"))
-    n_classes = len(intent_names)
-    assert all_labels == list(range(n_classes))
-
-    classwise_utterance_records = [[] for _ in range(n_classes)]
-    intents = [
-        {
-            "id": i,
-            "name": name,
-        }
-        for i, name in enumerate(intent_names)
-    ]
-
-    for b77_batch in banking77_train.iter(batch_size=16, drop_last_batch=False):
-        for txt, intent_id in zip(b77_batch["text"], b77_batch["label"], strict=False):
-            target_list = classwise_utterance_records[intent_id]
-            if shots_per_intent is not None and len(target_list) >= shots_per_intent:
-                continue
-            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})
-
-# load 
-file_url = "https://huggingface.co/datasets/PolyAI/banking77/resolve/main/dataset_infos.json"
-dataset_description = load_json_from_url(file_url)
-intent_names = dataset_description["default"]["features"]["label"]["names"]
-banking77 = load_dataset("PolyAI/banking77", trust_remote_code=True)
-
-# convert
-banking77_converted = convert_banking77(
-    banking77["train"],
-    shots_per_intent=None,
-    intent_names=intent_names
-)
+from autointent import Dataset
+from autointent.schemas import Intent, Sample
+
+
+def get_intents_data(github_file: str | None = None) -> list[Intent]:
+    """Load specific json from HF repo."""
+    github_file = github_file or "https://huggingface.co/datasets/PolyAI/banking77/resolve/main/dataset_infos.json"
+    raw_text = requests.get(github_file, timeout=5).text
+    dataset_description = json.loads(raw_text)
+    intent_names = dataset_description["default"]["features"]["label"]["names"]
+    return [Intent(id=i, name=name) for i, name in enumerate(intent_names)]
+
+
+def convert_banking77(
+    banking77_split: HFDataset, intents_data: list[Intent], shots_per_intent: int | None = None
+) -> list[Sample]:
+    """Convert one split into desired format."""
+    all_labels = sorted(banking77_split.unique("label"))
+
+    n_classes = len(intents_data)
+    if all_labels != list(range(n_classes)):
+        msg = "Something's wrong"
+        raise ValueError(msg)
+
+    classwise_samples = [[] for _ in range(n_classes)]
+
+    for sample in banking77_split:
+        target_list = classwise_samples[sample["label"]]
+        if shots_per_intent is not None and len(target_list) >= shots_per_intent:
+            continue
+        target_list.append(Sample(utterance=sample["text"], label=sample["label"]))
+
+    samples = [sample for samples_from_one_class in classwise_samples for sample in samples_from_one_class]
+    print(f"{len(samples)=}")
+    return samples
+
+
+if __name__ == "__main__":
+    intents_data = get_intents_data()
+    banking77 = load_dataset("PolyAI/banking77", trust_remote_code=True)
+
+    train_samples = convert_banking77(banking77["train"], intents_data=intents_data)
+    test_samples = convert_banking77(banking77["test"], intents_data=intents_data)
+
+    banking77_converted = Dataset.from_dict(
+        {"train": train_samples, "test": test_samples, "intents": intents_data}
+    )
 ```