Update README.md

README.md

@@ -52,3 +52,97 @@ configs:
   - split: intents
     path: intents/intents-*
 ---
+
+# clinc150
+
+This is a text classification dataset. It is intended for machine learning research and experimentation.
+
+This dataset is obtained via formatting another publicly available data to be compatible with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html).
+
+## Usage
+
+It is intended to be used with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
+
+```python
+from autointent import Dataset
+banking77 = Dataset.from_datasets("AutoIntent/clinc150")
+```
+
+## Source
+
+This dataset is taken from `cmaldona/All-Generalization-OOD-CLINC150` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
+
+```python
+# define util
+"""Convert clincq50 dataset to autointent internal format and scheme."""
+
+from datasets import Dataset as HFDataset
+from datasets import load_dataset
+
+from autointent import Dataset
+from autointent.schemas import Intent, Sample
+
+
+def extract_intents_data(
+    clinc150_split: HFDataset, oos_intent_name: str = "ood"
+) -> tuple[list[Intent], dict[str, int]]:
+    """Extract intent names and assign ids to them."""
+    intent_names = sorted(clinc150_split.unique("labels"))
+    oos_intent_id = intent_names.index(oos_intent_name)
+    intent_names.pop(oos_intent_id)
+
+    n_classes = len(intent_names)
+    assert n_classes == 150  # noqa: PLR2004, S101
+
+    name_to_id = dict(zip(intent_names, range(n_classes), strict=False))
+    intents_data = [Intent(id=i, name=name) for name, i in name_to_id.items()]
+    return intents_data, name_to_id
+
+
+def convert_clinc150(
+    clinc150_split: HFDataset,
+    name_to_id: dict[str, int],
+    shots_per_intent: int | None = None,
+    oos_intent_name: str = "ood",
+) -> list[Sample]:
+    """Convert one split into desired format."""
+    oos_samples = []
+    classwise_samples = [[] for _ in range(len(name_to_id))]
+    n_unrecognized_labels = 0
+
+    for batch in clinc150_split.iter(batch_size=16, drop_last_batch=False):
+        for txt, name in zip(batch["data"], batch["labels"], strict=False):
+            if name == oos_intent_name:
+                oos_samples.append(Sample(utterance=txt))
+                continue
+            intent_id = name_to_id.get(name, None)
+            if intent_id is None:
+                n_unrecognized_labels += 1
+                continue
+            target_list = classwise_samples[intent_id]
+            if shots_per_intent is not None and len(target_list) >= shots_per_intent:
+                continue
+            target_list.append(Sample(utterance=txt, label=intent_id))
+
+    in_domain_samples = [sample for samples_from_single_class in classwise_samples for sample in samples_from_single_class]
+    
+    print(f"{len(in_domain_samples)=}")
+    print(f"{len(oos_samples)=}")
+    print(f"{n_unrecognized_labels=}\n")
+    
+    return in_domain_samples + oos_samples
+
+
+if __name__ == "__main__":
+    clinc150 = load_dataset("cmaldona/All-Generalization-OOD-CLINC150")
+
+    intents_data, name_to_id = extract_intents_data(clinc150["train"])
+
+    train_samples = convert_clinc150(clinc150["train"], name_to_id)
+    validation_samples = convert_clinc150(clinc150["validation"], name_to_id)
+    test_samples = convert_clinc150(clinc150["test"], name_to_id)
+
+    clinc150_converted = Dataset.from_dict(
+        {"train": train_samples, "validation": validation_samples, "test": test_samples, "intents": intents_data}
+    )
+```