dialogue_nli.py

import csv
import json
import logging as logger
import os

import datasets


_CITATION = """\
@misc{welleck2019dialogue,
      title={Dialogue Natural Language Inference}, 
      author={Sean Welleck and Jason Weston and Arthur Szlam and Kyunghyun Cho},
      year={2019},
      eprint={1811.00671},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}
"""

_DESCRIPTION = """\
Dialogue NLI is a dataset that addresses the issue of consistency in dialogue models.
"""

_URL = "dnli.zip"

_HOMEPAGE = "https://wellecks.github.io/dialogue_nli/"

_LICENSE = "MIT"

class Dialogue_NLI(datasets.GeneratorBasedBuilder):
    """DNLI"""

    VERSION = datasets.Version("1.0.0")

    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="dialogue_nli", version=VERSION, description="dnli"),
    ]

    DEFAULT_CONFIG_NAME = "dialogue_nli"  # It's not mandatory to have a default configuration. Just use one if it make sense.

    def _info(self):
        return datasets.DatasetInfo(
            description=_DESCRIPTION,
            features=datasets.Features(
                {
                    "id": datasets.Value("string"),
                    "label": datasets.Value("string"),
                    "premise": datasets.Value("string"),
                    "hypothesis": datasets.Value("string"),
                    # "triple1": datasets.Value("string"),
                    # "triple2": datasets.Value("string"),                
                    "dtype": datasets.Value("string"),
                }
            ),
            # No default supervised_keys (as we have to pass both question
            # and context as input).
            supervised_keys=None,
            homepage=_HOMEPAGE,
            citation=_CITATION,
        )

    def _split_generators(self, dl_manager):
        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name

        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS
        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
        data_dir = dl_manager.download_and_extract(_URL)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_train.jsonl"),
                    "split": "train",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_dev.jsonl"),
                    "split": "dev",
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                # These kwargs will be passed to _generate_examples
                gen_kwargs={
                    "filepath": os.path.join(data_dir, "dnli/dialogue_nli/dialogue_nli_test.jsonl"),
                    "split": "test"
                },
            ),
        ]

    def _generate_examples(self, filepath, split):
        """This function returns the examples in the raw (text) form."""
        logger.info("generating examples from = %s", filepath)
        with open(filepath) as f:
            data = json.load(f)
            # print(data)
            for example in data:
                label = example.get("label", "").strip()
                id_ = example.get("id", "").strip()
                hypothesis = example.get("sentence1", "").strip()
                premise = example.get("sentence2", "").strip()
                dtype = example.get("dtype", "").strip()

                # Features currently used are "context", "question", and "answers".
                # Others are extracted here for the ease of future expansions.
                yield id_, {
                    "premise": premise,
                    "hypothesis": hypothesis,
                    "id": id_,
                    "label": label,
                    "dtype": dtype,
                }