Upload CLUTRR_v1.py

CLUTRR_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
+
+
+_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"],
+            }