Update README.md

README.md

@@ -14,7 +14,7 @@ dataset_info:
     num_bytes: 7584422.595703874
     num_examples: 10088
   download_size: 9002595
-  dataset_size: 15680087.0
+  dataset_size: 15680087
 - config_name: intents
   features:
   - name: id
@@ -46,4 +46,68 @@ configs:
   data_files:
   - split: intents
     path: intents/intents-*
+task_categories:
+- text-classification
+language:
+- en
 ---
+
+# banking77
+
+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/reuters")
+```
+
+## Source
+
+This dataset is taken from `ucirvine/reuters21578` and formatted with our [AutoIntent Library](https://deeppavlov.github.io/AutoIntent/index.html):
+
+```python
+from collections import defaultdict
+from datasets import load_dataset
+from autointent import Dataset
+
+# load original data
+reuters = load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True)
+
+# remove low-resource classes
+counter = defaultdict(int)
+for batch in reuters["train"].iter(batch_size=16):
+    for labels in batch["topics"]:
+        for lab in labels:
+            counter[lab] += 1
+names_to_remove = [name for name, cnt in counter.items() if cnt < 10]
+
+intent_names = sorted(set(name for intents in reuters["train"]["topics"] 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)}
+
+# extract only texts and labels
+def transform(example: dict):
+    return {
+        "utterance": example["text"],
+        "label": [name_to_id[intent_name] for intent_name in example["topics"] if intent_name not in names_to_remove],
+    }
+multilabel_reuters = reuters["train"].map(transform, remove_columns=reuters["train"].features.keys())
+
+# if any out-of-scope samples
+res = multilabel_reuters.to_list()
+for sample in res:
+    if len(sample["label"]) == 0:
+        sample.pop("label")
+
+# format
+intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
+reuters_converted = Dataset.from_dict({"intents": intents, "train": res})
+```