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import xml.etree.ElementTree as ET |
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import datasets |
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import pandas as pd |
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from huggingface_hub import hf_hub_url |
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logger = datasets.logging.get_logger(__name__) |
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class LaCourConfig(datasets.BuilderConfig): |
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def __init__(self, **kwargs): |
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super(LaCourConfig, self).__init__(**kwargs) |
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class LaCourDataset(datasets.GeneratorBasedBuilder): |
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""" |
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A class used to represent a Dataset. |
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... |
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Attributes |
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---------- |
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VERSION : datasets.Version |
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a version number for the dataset |
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BUILDER_CONFIGS : list |
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a list of BuilderConfig instances |
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Methods |
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------- |
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_info(): |
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Returns the dataset information. |
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_split_generators(download_manager: datasets.DownloadManager): |
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Returns SplitGenerators. |
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_generate_examples(): |
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Yields examples. |
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""" |
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VERSION = datasets.Version("0.1.0") |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name="transcripts", |
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version=VERSION, |
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description="transcript dataset based on xml files", |
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), |
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datasets.BuilderConfig( |
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name="documents", |
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version=VERSION, |
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description="linked documents associated with the webcast" |
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) |
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] |
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DEFAULT_CONFIG_NAME = "transcripts" |
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def _info(self): |
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""" |
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Returns the dataset information. |
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... |
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Returns |
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------- |
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datasets.DatasetInfo |
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a DatasetInfo instance containing information about the dataset |
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""" |
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if self.config.name == "transcripts": |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"webcast_id": datasets.Value("string"), |
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"segment_id": datasets.features.Value("int32"), |
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"speaker_name": datasets.features.Value("string"), |
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"speaker_role": datasets.features.Value("string"), |
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"data": datasets.features.Sequence({ |
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"begin": datasets.features.Value("float32"), |
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"end": datasets.features.Value("float32"), |
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"language": datasets.features.Value("string"), |
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"text": datasets.features.Value("string"), |
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}) |
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} |
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), |
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supervised_keys=None, |
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) |
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else: |
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return datasets.DatasetInfo( |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("int32"), |
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"webcast_id": datasets.Value("string"), |
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"hearing_title": datasets.Value("string"), |
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"hearing_date": datasets.Value("string"), |
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"hearing_type": datasets.Value("string"), |
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"application_number": datasets.features.Sequence(datasets.Value("string")), |
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"case_id": datasets.Value("string"), |
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"case_name": datasets.Value("string"), |
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"case_url": datasets.Value("string"), |
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"ecli": datasets.Value("string"), |
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"type": datasets.Value("string"), |
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"document_date": datasets.Value("string"), |
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"importance": datasets.Value("int32"), |
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"articles": datasets.features.Sequence(datasets.Value("string")), |
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"respondent_government": datasets.features.Sequence(datasets.Value("string")), |
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"issue": datasets.Value("string"), |
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"strasbourg_caselaw": datasets.Value("string"), |
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"external_sources": datasets.Value("string"), |
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"conclusion": datasets.Value("string"), |
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"separate_opinion": datasets.Value("bool") |
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} |
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), |
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supervised_keys=None, |
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) |
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def _split_generators(self, dl_manager): |
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""" |
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Returns SplitGenerators. |
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Parameters |
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---------- |
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download_manager : datasets.DownloadManager |
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a DownloadManager instance |
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Returns |
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------- |
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list |
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a list of SplitGenerator instances |
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""" |
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base_url_xml = hf_hub_url("TrustHLT/LaCour", filename="lacourxml.tar.gz", repo_type="dataset") |
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base_url_json = hf_hub_url("TrustHLT/LaCour", filename="lacour_linked_documents.json", repo_type="dataset") |
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if self.config.name == "transcripts": |
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path = dl_manager.download(base_url_xml) |
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xmlpath = dl_manager.iter_archive(path) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": xmlpath}), |
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] |
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else: |
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jsonpath = dl_manager.download(base_url_json) |
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return [ |
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datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": jsonpath}), |
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] |
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def _generate_examples(self, filepaths): |
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""" |
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This method reads the files in the provided transcripts, parses the data, and yields it in a structured format. |
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For the configuration "xml", it reads XML files and extracts speaker segments and associated metadata. |
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Parameters |
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---------- |
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filepaths : list |
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A list of filepaths to the data files. |
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Yields |
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------ |
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tuple |
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A tuple containing an ID and a dictionary with the data. The dictionary keys include 'id' and 'data'. |
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'data' is a list of lists, where each inner list contains a key and a value extracted from the data file. |
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""" |
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if self.config.name == "transcripts": |
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id_ = 0 |
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for fpath, file in filepaths: |
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logger.info("generating examples from = %s", fpath) |
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tree = ET.parse(file) |
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root = tree.getroot() |
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segment_id = 0 |
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for speakerSegment in root.findall('SpeakerSegment'): |
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text_segments = [] |
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for segment in speakerSegment.findall('Segment'): |
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meta_data = segment.find('meta_data') |
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text_segments.append({ |
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"begin": meta_data.findtext('TimestampBegin', ''), |
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"end": meta_data.findtext('TimestampEnd', ''), |
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"language": meta_data.findtext('Language', ''), |
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"text": segment.findtext('text', '').strip(), |
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}) |
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feature = id_, { |
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"id": id_, |
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"webcast_id": fpath.split('_')[1] + "_" + fpath.split('_')[2].split('.')[0], |
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"segment_id": segment_id, |
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"speaker_role": meta_data.findtext('Role', ''), |
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"speaker_name": meta_data.findtext('Name', ''), |
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"data": text_segments |
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} |
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yield feature |
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id_ += 1 |
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segment_id += 1 |
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elif self.config.name == "documents": |
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id_ = 0 |
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df = pd.read_json(filepaths, orient="index", dtype={"webcast_id": str}) |
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logger.info("generating examples from = %s", filepaths) |
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cols = df.columns.tolist() |
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cols.remove('appno') |
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df['judges'] = df['judges'].fillna('') |
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df = df.groupby(cols)['appno'].apply(';'.join).reset_index() |
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for _, row in df.iterrows(): |
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feature = id_,{ |
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"id": id_, |
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"webcast_id": row["webcast_id"], |
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"hearing_title": row["hearing_title"], |
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"hearing_date": row["hearing_date"], |
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"hearing_type": row["hearing_type"], |
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"application_number": row["appno"].split(';'), |
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"case_id": row["case_id"], |
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"case_name": row["case_name"], |
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"case_url": row["case_url"], |
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"ecli": row["ecli"], |
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"type": row["type"], |
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"document_date": row["document_date"], |
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"importance": row["importance"], |
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"articles": row["articles"].split(';'), |
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"respondent_government": row["respondent"].split(';'), |
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"issue": row["issue"], |
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"strasbourg_caselaw": row["strasbourg_caselaw"], |
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"external_sources": row["external_sources"], |
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"conclusion": row["conclusion"], |
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"separate_opinion": row["separate_opinion"] |
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} |
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yield feature |
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id_ += 1 |
