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import json |
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from typing import List |
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import datasets |
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from datasets import BuilderConfig |
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from datasets.features import Features |
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MAX_DIRECTORY_NAME_LENGTH = 255 |
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_CITATION = """\ |
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@article{kim2020domain, |
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title={Beyond Domain APIs: Task-oriented Conversational Modeling with Unstructured Knowledge Access}, |
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author={Seokhwan Kim and Mihail Eric and Karthik Gopalakrishnan and Behnam Hedayatnia and Yang Liu and Dilek Hakkani-Tur}, |
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journal={arXiv preprint arXiv:2006.03533} |
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year={2020} |
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} |
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""" |
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_HOMEPAGE = "https://github.com/alexa/alexa-with-dstc10-track2-dataset" |
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_DESCRIPTION = """\ |
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""" |
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_BASE_URL_DSTC10 = "https://raw.githubusercontent.com/alexa/alexa-with-dstc10-track2-dataset/main/task2" |
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_BASE_URL_DSTC9 = ( |
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"https://raw.githubusercontent.com/alexa/alexa-with-dstc9-track1-dataset/master" |
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) |
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_URLs = { |
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"train": { |
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"logs": f"{_BASE_URL_DSTC9}/data/train/logs.json", |
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"labels": f"{_BASE_URL_DSTC9}/data/train/labels.json", |
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"knowledge": f"{_BASE_URL_DSTC9}/data/knowledge.json", |
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}, |
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"validation": { |
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"logs": f"{_BASE_URL_DSTC10}/data/val/logs.json", |
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"labels": f"{_BASE_URL_DSTC10}/data/val/labels.json", |
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"knowledge": f"{_BASE_URL_DSTC10}/data/knowledge.json", |
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}, |
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"test": { |
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"logs": f"{_BASE_URL_DSTC10}/data/test/logs.json", |
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"labels": f"{_BASE_URL_DSTC10}/data/test/labels.json", |
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"knowledge": f"{_BASE_URL_DSTC10}/data/knowledge.json", |
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}, |
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} |
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class DSTC10Track2Task2(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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BUILDER_CONFIGS = [ |
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BuilderConfig( |
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name="generation", |
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version=VERSION, |
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description="", |
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), |
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] |
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DEFAULT_CONFIG_NAME = "generation" |
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def _info(self): |
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features = datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"gem_id": datasets.Value("string"), |
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"turns": [ |
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{ |
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"speaker": datasets.Value("string"), |
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"text": datasets.Value("string"), |
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"nbest": [ |
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{ |
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"hyp": datasets.Value("string"), |
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"score": datasets.Value("float"), |
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} |
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], |
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} |
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], |
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"knowledge": { |
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"domain": datasets.Value("string"), |
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"entity_name": datasets.Value("string"), |
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"title": datasets.Value("string"), |
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"body": datasets.Value("string"), |
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}, |
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"response": datasets.Value("string"), |
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"source": datasets.Value("string"), |
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"linearized_input": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"references": [datasets.Value("string")], |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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supervised_keys=None, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _generate_examples(self, logs, knowledge, labels, split=None): |
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with open(logs) as fp: |
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logs_data = json.load(fp) |
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with open(labels) as fp: |
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labels_data = json.load(fp) |
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with open(knowledge) as fp: |
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knowledge_data = json.load(fp) |
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i = 0 |
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for log, label in zip(logs_data, labels_data): |
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if not label["target"]: |
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continue |
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for turn in log: |
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if "nbest" not in turn: |
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turn["nbest"] = [] |
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if "source" not in label: |
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source = "multiwoz" |
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else: |
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source = label["source"] |
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domain, entity_id, doc_id = ( |
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label["knowledge"][0].get(key) |
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for key in ["domain", "entity_id", "doc_id"] |
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) |
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entity_name = knowledge_data[domain][str(entity_id)]["name"] |
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snippet = knowledge_data[domain][str(entity_id)]["docs"][str(doc_id)] |
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x = { |
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"id": str(i), |
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"gem_id": f"GEM-dstc10_track2_task2-{split}-{i}", |
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"turns": log, |
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"source": source, |
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"knowledge": { |
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"domain": domain, |
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"entity_name": entity_name, |
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"title": snippet["title"], |
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"body": snippet["body"], |
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}, |
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"response": label["response"], |
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"target": label["response"], |
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"references": [label["response"]], |
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} |
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x["linearized_input"] = self._linearize_example(x) |
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i += 1 |
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yield x["id"], x |
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def _download_files(self, urls, data_files, dl_manager): |
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if data_files is not None: |
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for split, update_dict in data_files.items(): |
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if isinstance(update_dict, dict): |
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for key, value in update_dict.items(): |
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urls[split][key] = value |
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return dl_manager.download_and_extract(urls) |
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def _linearize_example(self, d): |
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repr_string = "" |
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for t in d["turns"]: |
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repr_string += f"<{t['speaker']}> {t['text']} " |
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repr_string += f"|| knowledge domain: {d['knowledge']['domain']}, entity: {d['knowledge']['entity_name']}, title: {d['knowledge']['title']}, information: {d['knowledge']['body']}" |
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return repr_string |
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def _split_generators(self, dl_manager) -> List[datasets.SplitGenerator]: |
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urls_to_download = _URLs |
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downloaded_files = self._download_files( |
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urls_to_download, self.config.data_files, dl_manager |
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) |
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for split in ["train", "validation", "test"]: |
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downloaded_files[split]["split"] = split |
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return [ |
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datasets.SplitGenerator(name=ds_split, gen_kwargs=downloaded_files[split]) |
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for ds_split, split in ( |
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(datasets.Split.TRAIN, "train"), |
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(datasets.Split.VALIDATION, "validation"), |
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(datasets.Split.TEST, "test"), |
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) |
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] |
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