Update README.md

README.md

@@ -47,3 +47,93 @@ configs:
   - split: intents
     path: intents/intents-*
 ---
+
+# events
+
+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/events")
+```
+
+## Source
+
+This dataset is taken from `knowledgator/events_classification_biotech` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
+
+```python
+"""Convert events 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
+
+# these classes contain too few sampls
+names_to_remove = [
+    "partnerships & alliances",
+    "patent publication",
+    "subsidiary establishment",
+    "department establishment",
+]
+
+def extract_intents_data(events_dataset: HFDataset) -> tuple[list[Intent], dict[str, int]]:
+    """Extract intent names and assign ids to them."""
+    intent_names = sorted({name for intents in events_dataset["train"]["all_labels"] for name in intents})
+    for n in names_to_remove:
+        intent_names.remove(n)
+    name_to_id = {name: i for i, name in enumerate(intent_names)}
+    intents_data = [Intent(id=i,name=name) for i, name in enumerate(intent_names)]
+    return intents_data, name_to_id
+
+
+def converting_mapping(example: dict, name_to_id: dict[str, int]) -> dict[str, str | list[int]]:
+    """Extract utterance and label and drop the rest."""
+    return {
+        "utterance": example["content"],
+        "label": [
+            name_to_id[intent_name] for intent_name in example["all_labels"] if intent_name not in names_to_remove
+        ],
+    }
+
+
+def convert_events(events_split: HFDataset, name_to_id: dict[str, int]) -> list[Sample]:
+    """Convert one split into desired format."""
+    events_split = events_split.map(
+        converting_mapping, remove_columns=events_split.features.keys(),
+        fn_kwargs={"name_to_id": name_to_id}
+    )
+
+    in_domain_samples = []
+    oos_samples = []  # actually this dataset doesn't contain oos_samples so this will stay empty
+    for sample in events_split.to_list():
+        if sample["utterance"] is None:
+            continue
+        if len(sample["label"]) == 0:
+            sample.pop("label")
+            oos_samples.append(sample)
+        else:
+            in_domain_samples.append(sample)
+
+    return [Sample(**sample) for sample in in_domain_samples + oos_samples]
+
+if __name__ == "__main__":
+    # FYI: https://github.com/huggingface/datasets/issues/7248
+    events_dataset = load_dataset("knowledgator/events_classification_biotech", trust_remote_code=True)
+
+    intents_data, name_to_id = extract_intents_data(events_dataset)
+
+    train_samples = convert_events(events_dataset["train"], name_to_id)
+    test_samples = convert_events(events_dataset["test"], name_to_id)
+
+    events_converted = Dataset.from_dict(
+        {"train": train_samples, "test": test_samples, "intents": intents_data}
+    )
+```