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README.md

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----
-language:
-- en
-license:
-- unknown
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
----
-
-# Dataset Card for CLUTRR 
-
-## Table of Contents
-
-## Dataset Description
-### Dataset Summary
-**CLUTRR** (**C**ompositional **L**anguage **U**nderstanding and **T**ext-based **R**elational **R**easoning), a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) to test the systematic generalization and inductive reasoning capabilities of NLU systems.  
-
-The CLUTRR benchmark allows us to test a model’s ability for **systematic generalization** by testing on stories that contain unseen combinations of logical rules, and test for the various forms of **model robustness** by adding different kinds of superfluous noise facts to the stories.
-
-### Dataset Task
-CLUTRR contains a large set of semi-synthetic stories involving hypothetical families. The task is to infer the relationship between two family members, whose relationship is not explicitly mentioned in the given story.
-
-Join the CLUTRR community in  https://www.cs.mcgill.ca/~ksinha4/clutrr/ 
-## Dataset Structure
-We show detailed information for all 14 configurations of the dataset.
-
-### configurations:
-**id**: a unique series of characters and numbers that identify each instance <br>
-**story**: one semi-synthetic story involving hypothetical families<br>
-**query**: the target query/relation which contains two names, where the goal is to classify the relation that holds between these two entities<br>
-**target**: indicator for the correct relation for the query <br>
-**target_text**: text for the correct relation for the query <br>
-the indicator follows the rule as follows: <br> "aunt": 0, "son-in-law": 1, "grandfather": 2, "brother": 3,
-    "sister": 4,
-    "father": 5,
-    "mother": 6,
-    "grandmother": 7,
-    "uncle": 8,
-    "daughter-in-law": 9,
-    "grandson": 10,
-    "granddaughter": 11,
-    "father-in-law": 12,
-    "mother-in-law": 13,
-    "nephew": 14,
-    "son": 15,
-    "daughter": 16,
-    "niece": 17,
-    "husband": 18,
-    "wife": 19,
-    "sister-in-law": 20  <br>
-**clean\_story**: the story without noise factors<br>
-**proof\_state**: the logical rule of the kinship generation <br>
-**f\_comb**: the kinships of the query followed by the logical rule<br>
-**task\_name**: the task of the sub-dataset in a form of "task_[num1].[num2]"<br> 
-The first number [num1] indicates the status of noise facts added in the story: 1- no noise facts; 2- Irrelevant facts*; 3- Supporting facts*; 4- Disconnected facts*.<br>
-The second number [num2] directly indicates the length of clauses for the task target.<br>
-*for example:*<br>
-*task_1.2 -- task requiring clauses of length 2 without adding noise facts*<br> 
-*task_2.3 -- task requiring clauses of length 3 with Irrelevant noise facts added in the story*<br> 
-**story\_edges**: all the edges in the kinship graph<br>
-**edge\_types**: similar to the f\_comb, another form of the query's kinships followed by the logical rule <br>
-**query\_edge**: the corresponding edge of the target query in the kinship graph<br>
-**genders**: genders of names appeared in the story<br>
-**task\_split**: train,test <br>
-
-*Further explanation of Irrelevant facts, Supporting facts and Disconnected facts can be found in the 3.5 Robust Reasoning section in https://arxiv.org/abs/1908.06177
-
-### Data Instances
-
-An example of 'train'in Task 1.2 looks as follows.
-```
-{
-  "id": b2b9752f-d7fa-46a9-83ae-d474184c35b6,
-  "story": "[Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.",
-  "query": ('April', 'Ashley'),
-  "target": 7,
-  "target_text": "grandmother",
-  "clean_story": [Lillian] and her daughter [April] went to visit [Lillian]'s mother [Ashley] last Sunday.,
-  "proof_state": [{('April', 'grandmother', 'Ashley'): [('April', 'mother', 'Lillian'), ('Lillian', 'mother', 'Ashley')]}],
-  "f_comb": "mother-mother",
-  "task_name": "task_1.2",
-  "story_edges": [(0, 1), (1, 2)],
-  "edge_types": ['mother', 'mother'],
-  "query_edge": (0, 2),
-  "genders": "April:female,Lillian:female,Ashley:female",
-  "task_split": trian
-}
-```
-### Data Splits
-
-#### Data Split Name
-(corresponding with the name used in the paper)
-
-| task_split | split name in paper | train &validation task |test task |
-| :---:   |  :---:  | :-: | :-: |
-| gen_train23_test2to10 | data_089907f8 | 1.2, 1.3 | 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 | 
-| gen_train234_test2to10 | data_db9b8f04 | 1.2, 1.3, 1.4| 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 1.10 |    
-| rob_train_clean_23_test_all_23 | data_7c5b0e70 | 1.2,1.3 | 1.2, 1.3, 2.3, 3.3, 4.3 |   
-| rob_train_sup_23_test_all_23 | data_06b8f2a1 | 2.2, 2.3 | 2.2, 2.3, 1.3, 3.3, 4.3 |  
-| rob_train_irr_23_test_all_23 | data_523348e6 | 3.2, 3.3 | 3.2, 3.3, 1.3, 2.3, 4.3 |
-| rob_train_disc_23_test_all_23 | data_d83ecc3e | 4.2, 4.3 | 4.2, 4.3, 1.3, 2.3, 3.3 |
-
-#### Data Split Summary
-Number of Instances in each split 
-
-| task_split | train | validation | test |
-| :-: |  :---: | :---: | :---: |
-| gen_train23_test2to10   | 9074 | 2020 | 1146 |
-| gen_train234_test2to10 | 12064 | 3019 | 1048 |
-| rob_train_clean_23_test_all_23 | 8098 | 2026 | 447 |
-| rob_train_disc_23_test_all_23 | 8080 | 2020 | 445 |
-| rob_train_irr_23_test_all_23 | 8079 | 2020 | 444 |
-| rob_train_sup_23_test_all_23 | 8123 | 2031 | 447 |
-
-
-## Citation Information
-```
-@article{sinha2019clutrr,
-  Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
-  Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
-  Year = {2019},
-  journal = {Empirical Methods of Natural Language Processing (EMNLP)},
-  arxiv = {1908.06177}
-}
-```

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v1.py

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-# -*- coding: utf-8 -*-
-"""CLUTRR_Dataset Loading Script.ipynb
-Automatically generated by Colaboratory.
-Original file is located at
-    https://colab.research.google.com/drive/1q9DdeHA5JbgTHkH6kfZe_KWHQOwHZA97
-"""
-# coding=utf-8
-# Copyright 2019 The CLUTRR Datasets Authors and the HuggingFace Datasets Authors.
-#
-# CLUTRR is CC-BY-NC 4.0 (Attr Non-Commercial Inter.) licensed, as found in the LICENSE file.
-#
-# 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.
-
-# Lint as: python3
-"""The CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning) benchmark."""
-
-
-import csv
-import os
-import textwrap
-
-import numpy as np
-
-import datasets
-import json
-
-_CLUTRR_CITATION = """\
-@article{sinha2019clutrr,
-  Author = {Koustuv Sinha and Shagun Sodhani and Jin Dong and Joelle Pineau and William L. Hamilton},
-  Title = {CLUTRR: A Diagnostic Benchmark for Inductive Reasoning from Text},
-  Year = {2019},
-  journal = {Empirical Methods of Natural Language Processing (EMNLP)},
-  arxiv = {1908.06177}
-}
-"""
-
-_CLUTRR_DESCRIPTION = """\
-CLUTRR (Compositional Language Understanding and Text-based Relational Reasoning),
- a diagnostic benchmark suite, is first introduced in (https://arxiv.org/abs/1908.06177) 
- to test the systematic generalization and inductive reasoning capabilities of NLU systems.
-"""
-_URL = "https://raw.githubusercontent.com/kliang5/CLUTRR_huggingface_dataset/main/"
-_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"]
-
-class v1(datasets.GeneratorBasedBuilder):
-    """BuilderConfig for CLUTRR."""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name=task,
-            version=datasets.Version("1.0.0"),
-            description="",
-        )
-        for task in _TASK
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_CLUTRR_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "story": datasets.Value("string"),
-                    "query": datasets.Value("string"),
-                    "target": datasets.Value("int32"),
-                    "target_text": datasets.Value("string"),
-                    "clean_story": datasets.Value("string"),
-                    "proof_state": datasets.Value("string"),
-                    "f_comb": datasets.Value("string"),
-                    "task_name": datasets.Value("string"),
-                    "story_edges": datasets.Value("string"),
-                    "edge_types": datasets.Value("string"),
-                    "query_edge": datasets.Value("string"),
-                    "genders": datasets.Value("string"),
-                    "task_split": datasets.Value("string"),
-                }
-            ),
-            # No default supervised_keys (as we have to pass both premise
-            # and hypothesis as input).
-            supervised_keys=None,
-            homepage="https://www.cs.mcgill.ca/~ksinha4/clutrr/",
-            citation=_CLUTRR_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-          """Returns SplitGenerators."""
-          # dl_manager is a datasets.download.DownloadManager that can be used to
-          # download and extract URLs
-
-          task = str(self.config.name)
-          urls_to_download = {
-              "test": _URL + task + "/test.csv",
-              "train": _URL + task + "/train.csv",
-              "validation": _URL + task + "/validation.csv",
-          }
-          downloaded_files = dl_manager.download_and_extract(urls_to_download)
-       
-
-          return [
-              datasets.SplitGenerator(
-                  name=datasets.Split.TRAIN,
-                  # These kwargs will be passed to _generate_examples
-                  gen_kwargs={
-                      "filepath": downloaded_files["train"],
-                      "task": task,
-                  },
-              ),
-              datasets.SplitGenerator(
-                  name=datasets.Split.VALIDATION,
-                  # These kwargs will be passed to _generate_examples
-                  gen_kwargs={
-                      "filepath": downloaded_files["validation"],
-                      "task": task,
-                  },
-              ),
-              datasets.SplitGenerator(
-                  name=datasets.Split.TEST,
-                  # These kwargs will be passed to _generate_examples
-                  gen_kwargs={
-                      "filepath": downloaded_files["test"],
-                      "task": task,
-                  },
-              ),
-          ]
-
-    def _generate_examples(self, filepath, task):
-      """Yields examples."""
-      with open(filepath, encoding="utf-8") as f:
-        reader = csv.reader(f)
-        for id_, data in enumerate(reader):
-          if id_ == 0:
-            continue
-          # yield id_, data
-          # id_ += 1
-          yield id_, {
-              "id": data[1],
-              "story": data[2],
-              "query": data[3],
-              "target": data[4],
-              "target_text": data[5],
-              "clean_story": data[6],
-              "proof_state": data[7],
-              "f_comb": data[8],
-              "task_name": data[9],
-              "story_edges": data[10],
-              "edge_types": data[11],
-              "query_edge": data[12],
-              "genders": data[13],
-              "task_split": data[14],
-            }