README.md

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: 1924
        num_examples: 65
    download_size: 3837
    dataset_size: 1924
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-*
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 collections import defaultdict
from autointent import Dataset
import datasets

# load original data
reuters = datasets.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(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": train, "test": test})