Upload tab_fact.py

tab_fact.py

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+# Copyright 2020 The HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# 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.
+"""TabFact: A Large-scale Dataset for Table-based Fact Verification."""
+
+
+import json
+import os
+
+import datasets
+
+_CITATION = """\
+@inproceedings{2019TabFactA,
+  title={TabFact : A Large-scale Dataset for Table-based Fact Verification},
+  author={Wenhu Chen, Hongmin Wang, Jianshu Chen, Yunkai Zhang, Hong Wang, Shiyang Li, Xiyou Zhou and William Yang Wang},
+  booktitle = {International Conference on Learning Representations (ICLR)},
+  address = {Addis Ababa, Ethiopia},
+  month = {April},
+  year = {2020}
+}
+"""
+
+_DESCRIPTION = """\
+The problem of verifying whether a textual hypothesis holds the truth based on the given evidence, \
+also known as fact verification, plays an important role in the study of natural language \
+understanding and semantic representation. However, existing studies are restricted to \
+dealing with unstructured textual evidence (e.g., sentences and passages, a pool of passages), \
+while verification using structured forms of evidence, such as tables, graphs, and databases, remains unexplored. \
+TABFACT is large scale dataset with 16k Wikipedia tables as evidence for 118k human annotated statements \
+designed for fact verification with semi-structured evidence. \
+The statements are labeled as either ENTAILED or REFUTED. \
+TABFACT is challenging since it involves both soft linguistic reasoning and hard symbolic reasoning.
+"""
+
+_HOMEPAGE = "https://tabfact.github.io/"
+
+_GIT_ARCHIVE_URL = "https://github.com/wenhuchen/Table-Fact-Checking/archive/948b5560e2f7f8c9139bd91c7f093346a2bb56a8.zip"
+
+
+class TabFact(datasets.GeneratorBasedBuilder):
+    """TabFact: A Large-scale Dataset for Table-based Fact Verification."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        features = {
+            "id": datasets.Value("int32"),
+            "table": {
+                "id": datasets.Value("string"),
+                "header": datasets.features.Sequence(datasets.Value("string")),
+                "rows": datasets.features.Sequence(
+                    datasets.features.Sequence(datasets.Value("string"))
+                ),
+                "caption": datasets.Value("string"),
+            },
+            "statement": datasets.Value("string"),
+            "label": datasets.Value("int32"),
+        }
+
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(features),
+            supervised_keys=None,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        extracted_path = dl_manager.download_and_extract(_GIT_ARCHIVE_URL)
+
+        repo_path = os.path.join(
+            extracted_path,
+            "Table-Fact-Checking-948b5560e2f7f8c9139bd91c7f093346a2bb56a8",  # pragma: allowlist secret
+        )
+        all_csv_path = os.path.join(repo_path, "data", "all_csv")
+
+        train_statements_file = os.path.join(
+            repo_path, "tokenized_data", "train_examples.json"
+        )
+        val_statements_file = os.path.join(
+            repo_path, "tokenized_data", "val_examples.json"
+        )
+        test_statements_file = os.path.join(
+            repo_path, "tokenized_data", "test_examples.json"
+        )
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "statements_file": train_statements_file,
+                    "all_csv_path": all_csv_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "statements_file": val_statements_file,
+                    "all_csv_path": all_csv_path,
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "statements_file": test_statements_file,
+                    "all_csv_path": all_csv_path,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, statements_file, all_csv_path):
+        def convert_to_table_structure(table_str):
+            header = table_str.split("\n")[0].split("#")
+            rows = [row.split("#") for row in table_str.strip().split("\n")[1:]]
+            return {"header": header, "rows": rows}
+
+        with open(statements_file, encoding="utf-8") as f:
+            examples = json.load(f)
+
+        for i, (table_id, example) in enumerate(examples.items()):
+            table_file_path = os.path.join(all_csv_path, table_id)
+            with open(table_file_path, encoding="utf-8") as f:
+                table_text = f.read()
+
+            statements, labels, caption = example
+
+            for statement_idx, (statement, label) in enumerate(zip(statements, labels)):
+                table = convert_to_table_structure(table_text)
+                yield (
+                    f"{i}_{statement_idx}",
+                    {
+                        "id": i,
+                        "table": {
+                            "id": table_id,
+                            "header": table["header"],
+                            "rows": table["rows"],
+                            "caption": caption,
+                        },
+                        "statement": statement,
+                        "label": label,
+                    },
+                )