Create nli-zh-all.py

nli-zh-all.py

@@ -0,0 +1,109 @@
+# 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 Chinese Natural Language Inference (NLI-zh-all) Corpus.
+upload： https://github.com/shibing624
+"""
+
+
+import csv
+import os
+
+import datasets
+
+
+_CITATION = """\
+@misc{Text2vec,
+  author = {Xu, Ming},
+  title = {Text2vec: Text to vector toolkit},
+  year = {2022},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  howpublished = {\url{https://github.com/shibing624/text2vec}},
+}
+"""
+
+_DESCRIPTION = """\
+The SNLI corpus (version 1.0) is a merged chinese sentence similarity dataset, supporting the task of natural language
+inference (NLI), also known as recognizing textual entailment (RTE).
+"""
+
+_DATA_URL = "https://huggingface.co/datasets/shibing624/nli-zh-all/resolve/main/sampled_data"
+
+
+class Nli(datasets.GeneratorBasedBuilder):
+    """The Chinese Natural Language Inference (NLI-zh-all) Corpus."""
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="plain_text",
+            version=datasets.Version("1.0.0", ""),
+            description="Plain text import of NLI-zh-all",
+        )
+    ]
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "text1": datasets.Value("string"),
+                    "text2": datasets.Value("string"),
+                    "label": datasets.Value("int64"),
+                }
+            ),
+
+            supervised_keys=None,
+            homepage="https://github.com/shibing624/text2vec",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        
+        files = ['simclue-train-2k.jsonl',
+                'nli_zh-train-25k.jsonl',
+                'alpaca_gpt4-train-2k.jsonl',
+                'cmrc2018-train-2k.jsonl',
+                'snli_zh-train-5k.jsonl',
+                'chatmed_consult-train-500.jsonl',
+                'zhihu_kol-train-2k.jsonl',
+                'cblue_chip_sts-train-2k.jsonl',
+                'csl-train-500.jsonl',
+                'webqa-train-500.jsonl',
+                'xlsum-train-1k.jsonl',]
+        data_files = [f"{_DATA_URL}/{i}" for i in files]
+        return [
+            
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN, gen_kwargs={"filepath": dl_manager.download_and_extract(data_files)}
+            ),
+        ]
+
+    def _generate_examples(self, filepath):
+        """This function returns the examples in the raw (text) form."""
+        id = 0
+        if isinstance(filepath, str):
+            filepath = [filepath]
+        for file in filepath:
+            with open(file, encoding="utf-8") as f:
+                for key, row in enumerate(f):
+                    data = json.loads(row)
+                    yield id, {
+                            "text1": data["text1"],
+                            "text2": data["text2"],
+                            "label": data["label"]
+                    }
+                    id += 1