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_datasets("AutoIntent/reuters")

Source

This dataset is taken from ucirvine/reuters21578 and formatted with our AutoIntent Library:

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})