add dataset card

README.md

@@ -42,3 +42,55 @@ configs:
   - split: intents
     path: intents/intents-*
 ---
+
+# massive
+
+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
+
+dream = Dataset.from_datasets("AutoIntent/massive")
+```
+
+## Source
+
+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 load_dataset
+from autointent import Dataset
+
+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))
+    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 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"])
+```