Delete CLUTRR_v1.py

CLUTRR_v1.py

@@ -1,153 +0,0 @@
-# -*- 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
-
-
-_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://github.com/kliang5/CLUTRR_huggingface_dataset/tree/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 CLUTRR_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("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": os.path.join(downloaded_files["train"], task + "_train.jsonl"),
-                      "task": task,
-                  },
-              ),
-              datasets.SplitGenerator(
-                  name=datasets.Split.VALIDATION,
-                  # These kwargs will be passed to _generate_examples
-                  gen_kwargs={
-                      "filepath": os.path.join(downloaded_files["validation"], task + "_val.jsonl"),
-                      "task": task,
-                  },
-              ),
-              datasets.SplitGenerator(
-                  name=datasets.Split.TEST,
-                  # These kwargs will be passed to _generate_examples
-                  gen_kwargs={
-                      "filepath": os.path.join(downloaded_files["test"], task + "_test.jsonl"),
-                      "task": task,
-                  },
-              ),
-          ]
-
-    def _generate_examples(self, filepath, task):
-      """Yields examples."""
-      with open(filepath, encoding="utf-8") as f:
-        i = 0
-        for line in f:
-            data = json.loads(line)
-            i += 1
-            yield i, {
-                "id": data["id"],
-                "story": data["story"],
-                "query": data["query"],
-                "target": data["target"],
-                "clean_story": data["clean_story"],
-                "proof_state": data["proof_state"],
-                "f_comb": data["f_comb"],
-                "task_name": data["task_name"],
-                "story_edges": data["story_edges"],
-                "edge_types": data["edge_types"],
-                "query_edge": data["query_edge"],
-                "genders": data["genders"],
-                "task_split": data["task_split"],
-            }