unpredictable_cluster-noise.py

# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""This loads the UnpredicTable-cluster-noise dataset."""

import json
import os
import pandas as pd

import datasets

_CITATION = """\
@misc{chan2022few,
  author = {Chan, Jun Shern and Pieler, Michael and Jao, Jonathan and Scheurer, Jérémy and Perez, Ethan},
  title = {Few-shot Adaptation Works with UnpredicTable Data},
  publisher={arXiv},
  year = {2022},
  url = {https://arxiv.org/abs/2208.01009}
}
"""

_DESCRIPTION = """\
The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
"""

_HOMEPAGE = "https://ethanperez.net/unpredictable"

_LICENSE = "Apache 2.0"

_URL = "https://huggingface.co/datasets/MicPie/unpredictable_cluster-noise/resolve/main/data/unpredictable_cluster-noise.jsonl"

logger = datasets.logging.get_logger(__name__)


class UnpredicTable(datasets.GeneratorBasedBuilder):
    """
    The UnpredicTable dataset consists of web tables formatted as few-shot tasks for fine-tuning language models to improve their few-shot performance. For more details please see the accompanying dataset card.
    """

    VERSION = datasets.Version("1.0.0")

    def _info(self):
        features = datasets.Features(
            {
                "task": datasets.Value("string"),
                "input": datasets.Value("string"),
                "output": datasets.Value("string"),
                "options": datasets.Sequence([datasets.Value("string")]),
                "pageTitle": datasets.Value("string"),
                "outputColName": datasets.Value("string"),
                "url": datasets.Value("string"),
                "wdcFile": datasets.Value("string")
            }
        )
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=features,
            license=_LICENSE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        """Returns SplitGenerators."""
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={"filepath": data_dir},
            ),
        ]

    def _generate_examples(self, filepath):
        """Yields examples."""
        with open(filepath, encoding="utf-8") as f:
            for i, row in enumerate(f):
                data = json.loads(row)
                key = f"{data['task']}_{i}"
                yield key, {
                    "task": data["task"],
                    "input": data["input"],
                    "output": data["output"],
                    "options": data["options"],
                    "pageTitle": data["pageTitle"],
                    "outputColName": data["outputColName"],
                    "url": data["url"],
                    "wdcFile": data["wdcFile"],
                }