metadata
dataset_info:
- config_name: default
features:
- name: utterance
dtype: string
- name: label
sequence: int64
splits:
- name: train
num_bytes: 7169122
num_examples: 9042
- name: test
num_bytes: 450937
num_examples: 358
download_size: 8973442
dataset_size: 7620059
- config_name: intents
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: tags
sequence: 'null'
- name: regex_full_match
sequence: 'null'
- name: regex_partial_match
sequence: 'null'
- name: description
dtype: 'null'
splits:
- name: intents
num_bytes: 291
num_examples: 10
download_size: 3034
dataset_size: 291
- config_name: intentsqwen3-32b
features:
- name: id
dtype: int64
- name: name
dtype: string
- name: tags
sequence: 'null'
- name: regex_full_match
sequence: 'null'
- name: regex_partial_match
sequence: 'null'
- name: description
dtype: string
splits:
- name: intents
num_bytes: 1282
num_examples: 10
download_size: 3939
dataset_size: 1282
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- config_name: intents
data_files:
- split: intents
path: intents/intents-*
- config_name: intentsqwen3-32b
data_files:
- split: intents
path: intentsqwen3-32b/intents-*
task_categories:
- text-classification
language:
- en
reuters
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.
Usage
It is intended to be used with our AutoIntent Library:
from autointent import Dataset
reuters = Dataset.from_hub("AutoIntent/reuters")
Source
This dataset is taken from ucirvine/reuters21578 and formatted with our AutoIntent Library:
from autointent import Dataset
import datasets
def get_intents_info(ds: datasets.DatasetDict) -> list[str]:
return sorted(set(name for intents in ds["train"]["topics"] for name in intents))
def parse(ds: datasets.Dataset, intent_names: list[str]) -> list[dict]:
return [{
"utterance": example["text"],
"label": [int(name in example["topics"]) for name in intent_names]
} for example in ds]
def get_low_resource_classes_mask(ds: list[dict], intent_names: list[str], fraction_thresh: float = 0.01) -> list[bool]:
res = [0] * len(intent_names)
for sample in ds:
for i, indicator in enumerate(sample["label"]):
res[i] += indicator
for i in range(len(intent_names)):
res[i] /= len(ds)
return [(frac < fraction_thresh) for frac in res]
def remove_low_resource_classes(ds: datasets.Dataset, mask: list[bool]) -> list[dict]:
res = []
for sample in ds:
if sum(sample["label"]) == 1 and mask[sample["label"].index(1)]:
continue
sample["label"] = [
indicator for indicator, low_resource in
zip(sample["label"], mask, strict=True) if not low_resource
]
res.append(sample)
return res
def remove_oos(ds: list[dict]):
return [sample for sample in ds if sum(sample["label"]) != 0]
if __name__ == "__main__":
reuters = datasets.load_dataset("ucirvine/reuters21578", "ModHayes", trust_remote_code=True)
intent_names = get_intents_info(reuters)
train_parsed = parse(reuters["train"], intent_names)
test_parsed = parse(reuters["test"], intent_names)
mask = get_low_resource_classes_mask(train_parsed, intent_names)
intent_names = [name for i, name in enumerate(intent_names) if not mask[i]]
train_filtered = remove_oos(remove_low_resource_classes(train_parsed, mask))
test_filtered = remove_oos(remove_low_resource_classes(test_parsed, mask))
intents = [{"id": i, "name": name} for i, name in enumerate(intent_names)]
reuters_converted = Dataset.from_dict({"intents": intents, "train": train_filtered, "test": test_filtered})