Update README.md

README.md

@@ -74,11 +74,11 @@ This dataset is taken from `ucirvine/reuters21578` and formatted with our [AutoI
 
 ```python
 from collections import defaultdict
-from datasets import load_dataset
 from autointent import Dataset
+import datasets
 
 # load original data
-reuters = load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True)
+reuters = datasets.load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True)
 
 # remove low-resource classes
 counter = defaultdict(int)
@@ -94,20 +94,19 @@ for n in names_to_remove:
 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")
+def transform(ds: datasets.Dataset) -> list[dict]:
+    def _transform(example: dict):
+        return {
+            "utterance": example["text"],
+            "label": [int(name in example["topics"]) for name in intent_names if name not in names_to_remove]
+        }
+    ds = ds.map(_transform, remove_columns=ds.features.keys())
+    return [sample for sample in ds if sum(sample["label"]) != 0]
+
+train = transform(reuters["train"])
+test = transform(reuters["test"])
 
 # format
 intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
-reuters_converted = Dataset.from_dict({"intents": intents, "train": res})
+reuters_converted = Dataset.from_dict({"intents": intents, "train": train, "test": test})
 ```