humset.py

# coding=utf-8
# 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.

"""HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response"""

import json
import datasets


_CITATION = """\
@misc{https://doi.org/10.48550/arxiv.2210.04573,
  doi = {10.48550/ARXIV.2210.04573},
  url = {https://arxiv.org/abs/2210.04573},
  author = {Fekih, Selim and Tamagnone, Nicolò and Minixhofer, Benjamin and Shrestha, Ranjan and Contla, Ximena and Oglethorpe, Ewan and Rekabsaz, Navid},
  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crisis Response},
  publisher = {arXiv},
  year = {2022},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
"""


_DESCRIPTION = """\
HumSet is a novel and rich multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response community. HumSet is curated by humanitarian analysts and covers various disasters around the globe that occurred from 2018 to 2021 in 46 humanitarian response projects. The dataset consists of approximately 17K annotated documents in three languages of English, French, and Spanish, originally taken from publicly-available resources. For each document, analysts have identified informative snippets (entries) in respect to common humanitarian frameworks, and assigned one or many classes to each entry. See the our paper for details.
"""

_HOMEPAGE = "https://huggingface.co/datasets/nlp-thedeep/humset"

_LICENSE = "The GitHub repository which houses this dataset has an Apache License 2.0."


_URLs = {
    "1.0.0": {
        "train": "data/train.jsonl",
        "dev": "data/validation.jsonl",
        "test": "data/test.jsonl",
    }
}


_SUPPORTED_VERSIONS = [
    # Using cased version.
    datasets.Version("1.0.0", "Only primary tags"),
    # Same data as 0.0.2
    #datasets.Version("1.0.0", ""),
    # Having the model predict newline separators makes it easier to evaluate
    # using summary-level ROUGE.
    #datasets.Version("2.0.0", "Separate target sentences with newline."),
]


DEFAULT_CONFIG_NAME = "1.0.0" #datasets.Version("1.0", "Using cased version.")

## COMMON FEATURES
MAIN_FEATURES = datasets.Features(
    {
        "entry_id":  datasets.Value("string"),
        "lead_id": datasets.Value("string"),
        "project_id": datasets.Value("string"),
        "lang": datasets.Value("string"),
        "n_tokens": datasets.Value("int64"),
        "project_title": datasets.Value("string"),
        "created_at": datasets.Value("string"),
        "document": datasets.Value("string"),
        "excerpt": datasets.Value("string"),
        "sectors": datasets.Sequence(
            feature=datasets.ClassLabel(
                names=[
                    'Agriculture', 
                    'Cross', 
                    'Education', 
                    'Food Security', 
                    'Health', 
                    'Livelihoods', 
                    'Logistics', 
                    'Nutrition', 
                    'Protection', 
                    'Shelter', 
                    'WASH'
                ]
            )
        ),
        "pillars_1d": datasets.Sequence(
            feature=datasets.ClassLabel(
                names=[
                    'Casualties', 
                    'Context', 
                    'Covid-19', 
                    'Displacement', 
                    'Humanitarian Access', 
                    'Information And Communication', 
                    'Shock/Event'
                ]
            )
        ),
        "pillars_2d": datasets.Sequence(
            feature=datasets.ClassLabel(
                names=[
                    'At Risk', 
                    'Capacities & Response', 
                    'Humanitarian Conditions', 
                    'Impact', 
                    'Priority Interventions', 
                    'Priority Needs'
                ]
            )
        ),
        "subpillars_1d": datasets.Sequence(
            feature=datasets.ClassLabel(
                names=[
                    'Casualties->Dead', 
                    'Casualties->Injured', 
                    'Casualties->Missing', 
                    'Context->Demography', 
                    'Context->Economy', 
                    'Context->Environment', 
                    'Context->Legal & Policy', 
                    'Context->Politics', 
                    'Context->Security & Stability', 
                    'Context->Socio Cultural', 
                    'Covid-19->Cases', 
                    'Covid-19->Contact Tracing', 
                    'Covid-19->Deaths', 
                    'Covid-19->Hospitalization & Care', 
                    'Covid-19->Restriction Measures', 
                    'Covid-19->Testing', 
                    'Covid-19->Vaccination',
                    'Displacement->Intentions', 
                    'Displacement->Local Integration', 
                    'Displacement->Pull Factors', 
                    'Displacement->Push Factors', 
                    'Displacement->Type/Numbers/Movements', 
                    'Humanitarian Access->Number Of People Facing Humanitarian Access Constraints/Humanitarian Access Gaps', 
                    'Humanitarian Access->Physical Constraints', 
                    'Humanitarian Access->Population To Relief', 
                    'Humanitarian Access->Relief To Population', 
                    'Information And Communication->Communication Means And Preferences', 
                    'Information And Communication->Information Challenges And Barriers', 
                    'Information And Communication->Knowledge And Info Gaps (Hum)', 
                    'Information And Communication->Knowledge And Info Gaps (Pop)', 
                    'Shock/Event->Hazard & Threats', 
                    'Shock/Event->Type And Characteristics', 
                    'Shock/Event->Underlying/Aggravating Factors'
                ]
            )
        ),
        "subpillars_2d": datasets.Sequence(
            feature=datasets.ClassLabel(
                names=[
                    'At Risk->Number Of People At Risk', 
                    'At Risk->Risk And Vulnerabilities', 
                    'Capacities & Response->International Response', 
                    'Capacities & Response->Local Response', 
                    'Capacities & Response->National Response', 
                    'Capacities & Response->Number Of People Reached/Response Gaps', 
                    'Humanitarian Conditions->Coping Mechanisms', 
                    'Humanitarian Conditions->Living Standards',
                    'Humanitarian Conditions->Number Of People In Need', 
                    'Humanitarian Conditions->Physical And Mental Well Being', 
                    'Impact->Driver/Aggravating Factors', 
                    'Impact->Impact On People', 
                    'Impact->Impact On Systems, Services And Networks', 
                    'Impact->Number Of People Affected', 
                    'Priority Interventions->Expressed By Humanitarian Staff', 
                    'Priority Interventions->Expressed By Population', 
                    'Priority Needs->Expressed By Humanitarian Staff', 
                    'Priority Needs->Expressed By Population'
                ]
            )
        )
        
    }
)


class HumsetConfig(datasets.BuilderConfig):
    """BuilderConfig for DuoRC SelfRC."""

    def __init__(self, **kwargs):
        """BuilderConfig for DuoRC SelfRC.
        Args:
          **kwargs: keyword arguments forwarded to super.
        """
        super(HumsetConfig, self).__init__(**kwargs)


class Humset(datasets.GeneratorBasedBuilder):
    """DuoRC Dataset"""

    BUILDER_CONFIGS = [
        HumsetConfig(
            name=str(version),
            description=f"version {str(version)}", 
            version=version
        )
        for version in _SUPPORTED_VERSIONS
    ]

    def _info(self):

        if self.config.name == "1.0.0":
            return datasets.DatasetInfo(
                # This is the description that will appear on the datasets page.
                description=_DESCRIPTION,
                # This defines the different columns of the dataset and their types
                features=MAIN_FEATURES,
                homepage=_HOMEPAGE,
                license=_LICENSE,
                citation=_CITATION,
            )

    def _split_generators(self, dl_manager):
        
        """Returns SplitGenerators."""

        my_urls = _URLs[self.config.name]
        downloaded_files = dl_manager.download_and_extract(my_urls)
        return [
            datasets.SplitGenerator(
                name=datasets.Split.TRAIN,
                gen_kwargs={
                    "filepath": downloaded_files["train"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.VALIDATION,
                gen_kwargs={
                    "filepath": downloaded_files["dev"],
                },
            ),
            datasets.SplitGenerator(
                name=datasets.Split.TEST,
                gen_kwargs={
                    "filepath": downloaded_files["test"],
                },
            ),
        ]

    def _generate_examples(self, filepath):
        
        """This function returns the examples in the raw (text) form."""
        with open(filepath, encoding="utf-8") as f:
            data = list(f)
            idx = 0
            for line in data:
                row = json.loads(line)   
                if self.config.name == "1.0.0":
                    yield idx, row
                idx+=1
                #for example in duorc:
                
                
                """
                plot_id = example["id"]
                plot = example["plot"].strip()
                title = example["title"].strip()
                for qas in example["qa"]:
                    question_id = qas["id"]
                    question = qas["question"].strip()
                    answers = [answer.strip() for answer in qas["answers"]]
                    no_answer = qas["no_answer"]

                    yield question_id, {
                        "title": title,
                        "plot": plot,
                        "question": question,
                        "plot_id": plot_id,
                        "question_id": question_id,
                        "answers": answers,
                        "no_answer": no_answer,
                    }
                """