Create snli-zh.py

snli-zh.py

@@ -0,0 +1,97 @@
+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and 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.
+
+# Lint as: python3
+"""The Stanford Natural Language Inference (SNLI) Corpus.
+
+translate to chinese：https://github.com/liuhuanyong
+upload： https://github.com/shibing624
+"""
+
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@inproceedings{snli:emnlp2015,
+    Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.},
+    Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
+    Publisher = {Association for Computational Linguistics},
+    Title = {A large annotated corpus for learning natural language inference},
+    Year = {2015}
+}
+"""
+
+_DESCRIPTION = """\
+The SNLI corpus (version 1.0) is a collection of 570k human-written English
+sentence pairs manually labeled for balanced classification with the labels
+entailment, contradiction, and neutral, supporting the task of natural language
+inference (NLI), also known as recognizing textual entailment (RTE).
+"""
+
+_DATA_URL = "https://huggingface.co/datasets/shibing624/snli-zh/resolve/main/ChineseTextualInference-train.txt"
+
+
+class Snli(datasets.GeneratorBasedBuilder):
+    """The Stanford Natural Language Inference (SNLI-zh) Corpus."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text import of SNLI-zh",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "premise": datasets.Value("string"),
+                    "hypothesis": datasets.Value("string"),
+                    "label": datasets.features.ClassLabel(names=["entailment", "neutral", "contradiction"]),
+                }
+            ),
+
+            supervised_keys=None,
+            homepage="https://nlp.stanford.edu/projects/snli/",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        dl_file = dl_manager.download_and_extract(_DATA_URL)
+   
+        return [
+            
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_file}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        with open(filepath, encoding="utf-8") as f:
+            reader = csv.DictReader(f, delimiter="\t", quoting=csv.QUOTE_NONE)
+            for idx, row in enumerate(reader):
+                label = -1 if row["gold_label"] == "-" else row["gold_label"]
+                yield idx, {
+                    "premise": row["sentence1"],
+                    "hypothesis": row["sentence2"],
+                    "label": label,
+                }