metadata

dataset_info:
  - config_name: default
    features:
      - name: utterance
        dtype: string
      - name: label
        sequence: int64
    splits:
      - name: train
        num_bytes: 396298199
        num_examples: 55000
      - name: test
        num_bytes: 59593199
        num_examples: 5000
    download_size: 189778506
    dataset_size: 455891398
  - config_name: intents
    features:
      - name: id
        dtype: int64
      - name: name
        dtype: 'null'
      - name: tags
        sequence: 'null'
      - name: regexp_full_match
        sequence: 'null'
      - name: regexp_partial_match
        sequence: 'null'
      - name: description
        dtype: 'null'
    splits:
      - name: intents
        num_bytes: 420
        num_examples: 21
    download_size: 2970
    dataset_size: 420
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

eurlex

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

eurlex = Dataset.from_datasets("AutoIntent/eurlex")

Source

This dataset is taken from coastalcph/multi_eurlex and formatted with our AutoIntent Library:

from datasets import load_dataset
from autointent import Dataset

eurlex = load_dataset("coastalcph/multi_eurlex", "en", trust_remote_code=True)
labels = []
def transform(example: dict):
    for intent in example["labels"]:
        labels.append(intent)
    return {"utterance": example["text"], "label": example["labels"]}

labels = [{"id": label, "name": None} for label in set(labels)]
multilabel_eurlex_train = eurlex["train"].map(transform, remove_columns=eurlex["train"].features.keys())
multilabel_eurlex_test = eurlex["test"].map(transform, remove_columns=eurlex["test"].features.keys())
eurlex_converted = Dataset.from_dict({
    "intents": labels,
    "test": multilabel_eurlex_test.to_list(),
    "train": multilabel_eurlex_train.to_list()
})