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"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" |
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import json |
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import datasets |
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logger = datasets.logging.get_logger(__name__) |
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_CITATION = """ |
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@misc{fitzgerald2022massive, |
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title={MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages}, |
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author={Jack FitzGerald and Christopher Hench and Charith Peris and Scott Mackie and Kay Rottmann and Ana Sanchez and Aaron Nash and Liam Urbach and Vishesh Kakarala and Richa Singh and Swetha Ranganath and Laurie Crist and Misha Britan and Wouter Leeuwis and Gokhan Tur and Prem Natarajan}, |
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year={2022}, |
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eprint={2204.08582}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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@inproceedings{bastianelli-etal-2020-slurp, |
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title = "{SLURP}: A Spoken Language Understanding Resource Package", |
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author = "Bastianelli, Emanuele and |
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Vanzo, Andrea and |
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Swietojanski, Pawel and |
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Rieser, Verena", |
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booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
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month = nov, |
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year = "2020", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://aclanthology.org/2020.emnlp-main.588", |
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doi = "10.18653/v1/2020.emnlp-main.588", |
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pages = "7252--7262", |
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abstract = "Spoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In this paper, we release SLURP, a new SLU package containing the following: (1) A new challenging dataset in English spanning 18 domains, which is substantially bigger and linguistically more diverse than existing datasets; (2) Competitive baselines based on state-of-the-art NLU and ASR systems; (3) A new transparent metric for entity labelling which enables a detailed error analysis for identifying potential areas of improvement. SLURP is available at https://github.com/pswietojanski/slurp." |
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} |
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""" |
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_LANGUAGE_PAIRS = ['af-ZA', 'am-ET', 'ar-SA', 'az-AZ', 'bn-BD', 'cy-GB', 'da-DK', 'de-DE', 'el-GR', 'en-US', 'es-ES', 'fa-IR', 'fi-FI', 'fr-FR', 'he-IL', 'hi-IN', 'hu-HU', 'hy-AM', 'id-ID', 'is-IS', 'it-IT', 'ja-JP', 'jv-ID', 'ka-GE', 'km-KH', 'kn-IN', 'ko-KR', 'lv-LV', 'ml-IN', 'mn-MN', 'ms-MY', 'my-MM', 'nb-NO', 'nl-NL', 'pl-PL', 'pt-PT', 'ro-RO', 'ru-RU', 'sl-SL', 'sq-AL', 'sv-SE', 'sw-KE', 'ta-IN', 'te-IN', 'th-TH', 'tl-PH', 'tr-TR', 'ur-PK', 'vi-VN', 'zh-CN', 'zh-TW'] |
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_LICENSE = "cc-by-4-0" |
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_DESCRIPTION = """ |
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MASSIVE is a parallel dataset of > 1M utterances across 51 languages with annotations |
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for the Natural Language Understanding tasks of intent prediction and slot annotation. |
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Utterances span 60 intents and include 55 slot types. MASSIVE was created by localizing |
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the SLURP dataset, composed of general Intelligent Voice Assistant single-shot interactions. |
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""" |
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_URL = "https://amazon-massive-nlu-dataset.s3.amazonaws.com/amazon-massive-dataset-1.0.tar.gz" |
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_SCENARIOS = ['calendar', 'recommendation', 'social', 'general', 'news', 'cooking', 'iot', 'email', 'weather', 'alarm', 'transport', 'lists', 'takeaway', 'play', 'audio', 'music', 'qa', 'datetime'] |
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_INTENTS = ['audio_volume_other', 'play_music', 'iot_hue_lighton', 'general_greet', 'calendar_set', 'audio_volume_down', 'social_query', 'audio_volume_mute', 'iot_wemo_on', 'iot_hue_lightup', 'audio_volume_up', 'iot_coffee', 'takeaway_query', 'qa_maths', 'play_game', 'cooking_query', 'iot_hue_lightdim', 'iot_wemo_off', 'music_settings', 'weather_query', 'news_query', 'alarm_remove', 'social_post', 'recommendation_events', 'transport_taxi', 'takeaway_order', 'music_query', 'calendar_query', 'lists_query', 'qa_currency', 'recommendation_movies', 'general_joke', 'recommendation_locations', 'email_querycontact', 'lists_remove', 'play_audiobook', 'email_addcontact', 'lists_createoradd', 'play_radio', 'qa_stock', 'alarm_query', 'email_sendemail', 'general_quirky', 'music_likeness', 'cooking_recipe', 'email_query', 'datetime_query', 'transport_traffic', 'play_podcasts', 'iot_hue_lightchange', 'calendar_remove', 'transport_query', 'transport_ticket', 'qa_factoid', 'iot_cleaning', 'alarm_set', 'datetime_convert', 'iot_hue_lightoff', 'qa_definition', 'music_dislikeness'] |
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_TAGS = ['O', 'B-food_type', 'B-movie_type', 'B-person', 'B-change_amount', 'I-relation', 'I-game_name', 'B-date', 'B-movie_name', 'I-person', 'I-place_name', 'I-podcast_descriptor', 'I-audiobook_name', 'B-email_folder', 'B-coffee_type', 'B-app_name', 'I-time', 'I-coffee_type', 'B-transport_agency', 'B-podcast_descriptor', 'I-playlist_name', 'B-media_type', 'B-song_name', 'I-music_descriptor', 'I-song_name', 'B-event_name', 'I-timeofday', 'B-alarm_type', 'B-cooking_type', 'I-business_name', 'I-color_type', 'B-podcast_name', 'I-personal_info', 'B-weather_descriptor', 'I-list_name', 'B-transport_descriptor', 'I-game_type', 'I-date', 'B-place_name', 'B-color_type', 'B-game_name', 'I-artist_name', 'I-drink_type', 'B-business_name', 'B-timeofday', 'B-sport_type', 'I-player_setting', 'I-transport_agency', 'B-game_type', 'B-player_setting', 'I-music_album', 'I-event_name', 'I-general_frequency', 'I-podcast_name', 'I-cooking_type', 'I-radio_name', 'I-joke_type', 'I-meal_type', 'I-transport_type', 'B-joke_type', 'B-time', 'B-order_type', 'B-business_type', 'B-general_frequency', 'I-food_type', 'I-time_zone', 'B-currency_name', 'B-time_zone', 'B-ingredient', 'B-house_place', 'B-audiobook_name', 'I-ingredient', 'I-media_type', 'I-news_topic', 'B-music_genre', 'I-definition_word', 'B-list_name', 'B-playlist_name', 'B-email_address', 'I-currency_name', 'I-movie_name', 'I-device_type', 'I-weather_descriptor', 'B-audiobook_author', 'I-audiobook_author', 'I-app_name', 'I-order_type', 'I-transport_name', 'B-radio_name', 'I-business_type', 'B-definition_word', 'B-artist_name', 'I-movie_type', 'B-transport_name', 'I-email_folder', 'B-music_album', 'I-house_place', 'I-music_genre', 'B-drink_type', 'I-alarm_type', 'B-music_descriptor', 'B-news_topic', 'B-meal_type', 'I-transport_descriptor', 'I-email_address', 'I-change_amount', 'B-device_type', 'B-transport_type', 'B-relation', 'I-sport_type', 'B-personal_info'] |
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_ALL = "all" |
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class MASSIVE(datasets.GeneratorBasedBuilder): |
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"""MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name = name, |
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version = datasets.Version("1.0.0"), |
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description = f"The MASSIVE corpora for {name}", |
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) for name in _LANGUAGE_PAIRS |
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] |
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BUILDER_CONFIGS.append(datasets.BuilderConfig( |
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name = _ALL, |
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version = datasets.Version("1.0.0"), |
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description = f"The MASSIVE corpora for entire corpus", |
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)) |
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DEFAULT_CONFIG_NAME = _ALL |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"locale": datasets.Value("string"), |
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"partition": datasets.Value("string"), |
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"scenario": datasets.features.ClassLabel(names=_SCENARIOS), |
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"intent": datasets.features.ClassLabel(names=_INTENTS), |
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"utt": datasets.Value("string"), |
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"annot_utt": datasets.Value("string"), |
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"tokens": datasets.Sequence(datasets.Value("string")), |
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"ner_tags": datasets.Sequence( |
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datasets.features.ClassLabel( |
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names = _TAGS |
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) |
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), |
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"worker_id": datasets.Value("string"), |
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"slot_method": datasets.Sequence({ |
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"slot": datasets.Value("string"), |
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"method": datasets.Value("string"), |
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}), |
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"judgments": datasets.Sequence({ |
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"worker_id": datasets.Value("string"), |
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"intent_score": datasets.Value("int8"), |
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"slots_score": datasets.Value("int8"), |
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"grammar_score": datasets.Value("int8"), |
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"spelling_score": datasets.Value("int8"), |
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"language_identification": datasets.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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homepage="https://github.com/alexa/massive", |
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citation=_CITATION, |
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license=_LICENSE, |
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) |
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def _split_generators(self, dl_manager): |
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archive = dl_manager.download(_URL) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TRAIN, |
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gen_kwargs={ |
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"files": dl_manager.iter_archive(archive), |
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"split": "train", |
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"lang": self.config.name, |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.VALIDATION, |
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gen_kwargs={ |
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"files": dl_manager.iter_archive(archive), |
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"split": "dev", |
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"lang": self.config.name, |
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} |
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), |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={ |
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"files": dl_manager.iter_archive(archive), |
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"split": "test", |
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"lang": self.config.name, |
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} |
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), |
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] |
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def _getBioFormat(self, text): |
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tags, tokens = [], [] |
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bio_mode = False |
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cpt_bio = 0 |
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current_tag = None |
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split_iter = iter(text.split(" ")) |
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for s in split_iter: |
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if s.startswith("["): |
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current_tag = s.strip("[") |
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bio_mode = True |
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cpt_bio += 1 |
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next(split_iter) |
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continue |
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elif s.endswith("]"): |
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bio_mode = False |
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if cpt_bio == 1: |
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prefix = "B-" |
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else: |
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prefix = "I-" |
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token = prefix + current_tag |
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word = s.strip("]") |
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current_tag = None |
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cpt_bio = 0 |
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else: |
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if bio_mode == True: |
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if cpt_bio == 1: |
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prefix = "B-" |
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else: |
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prefix = "I-" |
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token = prefix + current_tag |
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word = s |
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cpt_bio += 1 |
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else: |
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token = "O" |
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word = s |
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tags.append(token) |
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tokens.append(word) |
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return tokens, tags |
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def _generate_examples(self, files, split, lang): |
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key_ = 0 |
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if lang == "all": |
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lang = _LANGUAGE_PAIRS.copy() |
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else: |
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lang = [lang] |
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logger.info("⏳ Generating examples from = %s", ", ".join(lang)) |
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for path, f in files: |
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l = path.split("1.0/data/")[-1].split(".jsonl")[0] |
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if not lang: |
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break |
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elif l in lang: |
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lang.remove(l) |
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else: |
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continue |
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lines = f.read().decode(encoding="utf-8").split("\n") |
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for line in lines: |
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data = json.loads(line) |
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if data["partition"] != split: |
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continue |
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if "slot_method" in data: |
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slot_method = [ |
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{ |
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"slot": s["slot"], |
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"method": s["method"], |
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} for s in data["slot_method"] |
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] |
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else: |
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slot_method = [] |
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if "judgments" in data: |
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judgments = [ |
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{ |
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"worker_id": j["worker_id"], |
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"intent_score": j["intent_score"], |
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"slots_score": j["slots_score"], |
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"grammar_score": j["grammar_score"], |
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"spelling_score": j["spelling_score"], |
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"language_identification": j["language_identification"] if "language_identification" in j else "target", |
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} for j in data["judgments"] |
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] |
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else: |
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judgments = [] |
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tokens, tags = self._getBioFormat(data["annot_utt"]) |
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yield key_, { |
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"id": data["id"], |
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"locale": data["locale"], |
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"partition": data["partition"], |
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"scenario": data["scenario"], |
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"intent": data["intent"], |
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"utt": data["utt"], |
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"annot_utt": data["annot_utt"], |
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"tokens": tokens, |
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"ner_tags": tags, |
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"worker_id": data["worker_id"], |
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"slot_method": slot_method, |
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"judgments": judgments, |
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} |
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key_ += 1 |
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