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"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark.""" |
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import csv |
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import os |
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import textwrap |
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import numpy as np |
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import datasets |
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_CLUTRR_CITATION = """\ |
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@article{sinha2019clutrr, |
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Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton}, |
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Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text}, |
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Year = {2019}, |
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journal = {Empirical Methods of Natural Language Processing (EMNLP)}, |
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arxiv = {1908.06177} |
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} |
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""" |
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_CLUTRR_DESCRIPTION = """\ |
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CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning), |
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a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) |
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to test the systematic generalization and inductive reasoning capabilities of NLU systems. |
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""" |
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_URL = "https://github.com/kliang5/CLUTRR_huggingface_dataset/tree/main/" |
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_TASK = ["gen_train23_test2to10", "gen_train234_test2to10", "rob_train_clean_23_test_all_23", "rob_train_disc_23_test_all_23", "rob_train_irr_23_test_all_23","rob_train_sup_23_test_all_23"] |
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class GLUTRR(datasets.GeneratorBasedBuilder): |
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"""BuilderConfig for GLUTRR.""" |
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BUILDER_CONFIGS = [ |
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datasets.BuilderConfig( |
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name=task, |
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version=datasets.Version("1.0.0"), |
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description="", |
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) |
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for task in _TASK |
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] |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_CLUTRR_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"story": datasets.Value("string"), |
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"query": datasets.Value("string"), |
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"target": datasets.Value("string"), |
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"clean_story": datasets.Value("string"), |
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"proof_state": datasets.Value("string"), |
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"f_comb": datasets.Value("string"), |
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"task_name": datasets.Value("string"), |
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"story_edges": datasets.Value("string"), |
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"edge_types": datasets.Value("string"), |
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"query_edge": datasets.Value("string"), |
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"genders": datasets.Value("string"), |
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"task_split": datasets.Value("string"), |
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} |
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), |
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supervised_keys=None, |
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homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/", |
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citation=_CLUTRR_CITATION, |
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) |
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def _split_generators(self, dl_manager): |
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"""Returns SplitGenerators.""" |
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task = str(self.config.name) |
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urls_to_download = { |
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"test": _URL + task + "/test.csv", |
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"train": _URL + task + "/train.csv", |
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"validation": _URL + task + "/validation.csv", |
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} |
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downloaded_files = dl_manager.download_and_extract(urls_to_download) |
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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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"filepath": os.path.join(downloaded_files["train"], task + "_train.jsonl"), |
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"task": task, |
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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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"filepath": os.path.join(downloaded_files["validation"], task + "_val.jsonl"), |
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"task": task, |
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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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"filepath": os.path.join(downloaded_files["test"], task + "_test.jsonl"), |
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"task": task, |
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}, |
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), |
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] |
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def _generate_examples(self, filepath, task): |
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"""Yields examples.""" |
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with open(filepath, encoding="utf-8") as f: |
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i = 0 |
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for line in f: |
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data = json.loads(line) |
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i += 1 |
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yield i, { |
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"id": data["id"], |
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"story": data["story"], |
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"query": data["query"], |
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"target": data["target"], |
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"clean_story": data["clean_story"], |
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"proof_state": data["proof_state"], |
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"f_comb": data["f_comb"], |
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"task_name": data["task_name"], |
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"story_edges": data["story_edges"], |
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"edge_types": data["edge_types"], |
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"query_edge": data["query_edge"], |
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"genders": data["genders"], |
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"task_split": data["task_split"], |
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} |