add nlizh

nli_zh.py

@@ -1,65 +1,146 @@
-"""Natural Language Inference (NLI) Chinese Corpus.(nli_zh)"""
+# -*- coding: utf-8 -*-
+"""
+@author:XuMing([email protected])
+@description:
+"""
 
+"""Natural Language Inference (NLI) Chinese Corpus.(nli_zh)"""
 
-import csv
 import os
 
 import datasets
 
+_DESCRIPTION = """纯文本数据，格式：（sentence1， sentence2， label）。常见中文语义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。"""
 
-_DESCRIPTION = """\
-常见中文语义匹配数据集，包含ATEC、BQ、LCQMC、PAWSX、STS-B共5个任务。
-"""
+ATEC_HOME = "https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC"
+BQ_HOME = "http://icrc.hitsz.edu.cn/info/1037/1162.htm"
+LCQMC_HOME = "http://icrc.hitsz.edu.cn/Article/show/171.html"
+PAWSX_HOME = "https://arxiv.org/abs/1908.11828"
+STSB_HOME = "https://github.com/pluto-junzeng/CNSD"
+
+_CITATION = "https://github.com/shibing624/text2vec"
+
+_DATA_URL = "https://github.com/shibing624/text2vec/releases/download/1.1.2/senteval_cn.zip"
+
+
+class NliZhConfig(datasets.BuilderConfig):
+    """BuilderConfig for NLI_zh"""
 
+    def __init__(self, features, data_url, citation, url, label_classes=(0, 1), **kwargs):
+        """BuilderConfig for NLI_zh
+        Args:
+          features: `list[string]`, list of the features that will appear in the
+            feature dict. Should not include "label".
+          data_url: `string`, url to download the zip file from.
+          citation: `string`, citation for the data set.
+          url: `string`, url for information about the data set.
+          label_classes: `list[int]`, sim is 1, else 0.
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super().__init__(version=datasets.Version("1.0.0"), **kwargs)
+        self.features = features
+        self.label_classes = label_classes
+        self.data_url = data_url
+        self.citation = citation
+        self.url = url
 
-class Nli_zh(datasets.GeneratorBasedBuilder):
+
+class NliZh(datasets.GeneratorBasedBuilder):
     """The Natural Language Inference Chinese(NLI_zh) Corpus."""
 
     BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
+        NliZhConfig(
             name="ATEC",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text import of NLI_zh",
-        )
+            description=_DESCRIPTION,
+            features=["sentence1", "sentence1"],
+            data_url=_DATA_URL,
+            citation=_CITATION,
+            url=ATEC_HOME,
+        ),
+        NliZhConfig(
+            name="BQ",
+            description=_DESCRIPTION,
+            features=["sentence1", "sentence1"],
+            data_url=_DATA_URL,
+            citation=_CITATION,
+            url=BQ_HOME,
+        ),
+        NliZhConfig(
+            name="LCQMC",
+            description=_DESCRIPTION,
+            features=["sentence1", "sentence1"],
+            data_url=_DATA_URL,
+            citation=_CITATION,
+            url=LCQMC_HOME,
+        ),
+        NliZhConfig(
+            name="PAWSX",
+            description=_DESCRIPTION,
+            features=["sentence1", "sentence1"],
+            data_url=_DATA_URL,
+            citation=_CITATION,
+            url=PAWSX_HOME,
+        ),
+        NliZhConfig(
+            name="STS-B",
+            description=_DESCRIPTION,
+            features=["sentence1", "sentence1"],
+            data_url=_DATA_URL,
+            citation=_CITATION,
+            url=STSB_HOME,
+        ),
     ]
 
     def _info(self):
         return datasets.DatasetInfo(
-            description=_DESCRIPTION,
+            description=self.config.description,
             features=datasets.Features(
                 {
                     "sentence1": datasets.Value("string"),
                     "sentence2": datasets.Value("string"),
                     "label": datasets.Value("int32"),
+                    # "idx": datasets.Value("int32"),
                 }
             ),
-            supervised_keys=None,
-            homepage="https://github.com/shibing624/text2vec",
+            homepage=self.config.url,
+            citation=self.config.citation,
         )
 
     def _split_generators(self, dl_manager):
-        dl_dir = dl_manager.download_and_extract(_DATA_URL)
-        data_dir = os.path.join(dl_dir, "nli_zh")
+        dl_dir = dl_manager.download_and_extract(self.config.data_url) or ""
+        dl_dir = os.path.join(dl_dir, f"senteval_cn/{self.config.name}")
         return [
             datasets.SplitGenerator(
-                name=datasets.Split.TEST, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_test.txt")}
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, f"{self.config.name}.train.data"),
+                    "split": datasets.Split.TRAIN,
+                },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_dev.txt")}
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, f"{self.config.name}.valid.data"),
+                    "split": datasets.Split.VALIDATION,
+                },
             ),
             datasets.SplitGenerator(
-                name=datasets.Split.TRAIN, gen_kwargs={"filepath": os.path.join(data_dir, "snli_1.0_train.txt")}
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "data_file": os.path.join(dl_dir, f"{self.config.name}.test.data"),
+                    "split": datasets.Split.TEST,
+                },
             ),
         ]
 
     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"]
+        with open(filepath, 'r', encoding="utf-8") as f:
+            for idx, row in enumerate(f):
+                # print(row)
+                terms = row.split('\t')
                 yield idx, {
-                    "premise": row["sentence1"],
-                    "hypothesis": row["sentence2"],
-                    "label": label,
-                }
+                    "sentence1": terms[0],
+                    "sentence2": terms[1],
+                    "label": int(terms[2]),
+                }