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+ATEC/nli_zh-train.parquet filter=lfs diff=lfs merge=lfs -text
+BQ/nli_zh-train.parquet filter=lfs diff=lfs merge=lfs -text
+LCQMC/nli_zh-train.parquet filter=lfs diff=lfs merge=lfs -text
+PAWSX/nli_zh-train.parquet filter=lfs diff=lfs merge=lfs -text

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README.md

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----
-annotations_creators:
-- shibing624
-language_creators:
-- shibing624
-language:
-- zh
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 100K<n<20M
-source_datasets:
-- https://github.com/shibing624/text2vec
-- https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC
-- http://icrc.hitsz.edu.cn/info/1037/1162.htm
-- http://icrc.hitsz.edu.cn/Article/show/171.html
-- https://arxiv.org/abs/1908.11828
-- https://github.com/pluto-junzeng/CNSD
-task_categories:
-- text-classification
-task_ids:
-- natural-language-inference
-- semantic-similarity-scoring
-- text-scoring
-paperswithcode_id: snli
-pretty_name: Stanford Natural Language Inference
----
-# Dataset Card for NLI_zh
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
-- **Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) (located on the homepage)
-- **Size of downloaded dataset files:** 16 MB
-- **Total amount of disk used:** 42 MB
-### Dataset Summary
-
-常见中文语义匹配数据集，包含[ATEC](https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC)、[BQ](http://icrc.hitsz.edu.cn/info/1037/1162.htm)、[LCQMC](http://icrc.hitsz.edu.cn/Article/show/171.html)、[PAWSX](https://arxiv.org/abs/1908.11828)、[STS-B](https://github.com/pluto-junzeng/CNSD)共5个任务。
-
-数据源：
-
-- ATEC: https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC
-- BQ: http://icrc.hitsz.edu.cn/info/1037/1162.htm
-- LCQMC: http://icrc.hitsz.edu.cn/Article/show/171.html
-- PAWSX: https://arxiv.org/abs/1908.11828
-- STS-B: https://github.com/pluto-junzeng/CNSD
-
-
-### Supported Tasks and Leaderboards
-
-Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。
-
-中文匹配任务的结果目前在顶会paper上出现较少，我罗列一个我自己训练的结果：
-
-**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) 
-
-### Languages
-
-数据集均是简体中文文本。
-
-## Dataset Structure
-### Data Instances
-An example of 'train' looks as follows.
-```
-{
-  "sentence1": "刘诗诗杨幂谁漂亮",
-  "sentence2": "刘诗诗和杨幂谁漂亮",
-  "label": 1,
-}
-{
-  "sentence1": "汇理财怎么样",
-  "sentence2": "怎么样去理财",
-  "label": 0,
-}
-```
-
-### Data Fields
-The data fields are the same among all splits.
-
-- `sentence1`: a `string` feature.
-- `sentence2`: a `string` feature.
-- `label`: a classification label, with possible values including `similarity` (1), `dissimilarity` (0).
-
-### Data Splits
-
-#### ATEC
-
-```shell
-$ wc -l ATEC/*
-   20000 ATEC/ATEC.test.data
-   62477 ATEC/ATEC.train.data
-   20000 ATEC/ATEC.valid.data
-  102477 total
-```
-
-#### BQ
-
-```shell
-$ wc -l BQ/*
-   10000 BQ/BQ.test.data
-  100000 BQ/BQ.train.data
-   10000 BQ/BQ.valid.data
-  120000 total
-```
-
-#### LCQMC
-
-```shell
-$ wc -l LCQMC/*
-   12500 LCQMC/LCQMC.test.data
-  238766 LCQMC/LCQMC.train.data
-    8802 LCQMC/LCQMC.valid.data
-  260068 total
-```
-
-#### PAWSX
-
-```shell
-$ wc -l PAWSX/*
-    2000 PAWSX/PAWSX.test.data
-   49401 PAWSX/PAWSX.train.data
-    2000 PAWSX/PAWSX.valid.data
-   53401 total
-```
-
-#### STS-B
-
-```shell
-$ wc -l STS-B/*
-    1361 STS-B/STS-B.test.data
-    5231 STS-B/STS-B.train.data
-    1458 STS-B/STS-B.valid.data
-    8050 total
-```
-
-## Dataset Creation
-### Curation Rationale
-作为中文NLI(natural langauge inference)数据集，这里把这个数据集上传到huggingface的datasets，方便大家使用。
-### Source Data
-#### Initial Data Collection and Normalization
-#### Who are the source language producers?
-数据集的版权归原作者所有，使用各数据集时请尊重原数据集的版权。
-
-BQ: Jing Chen, Qingcai Chen, Xin Liu, Haijun Yang, Daohe Lu, Buzhou Tang, The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification EMNLP2018. 
-### Annotations
-#### Annotation process
-
-#### Who are the annotators?
-原作者。
-
-### Personal and Sensitive Information
-
-## Considerations for Using the Data
-### Social Impact of Dataset
-This dataset was developed as a benchmark for evaluating representational systems for text, especially including those induced by representation learning methods, in the task of predicting truth conditions in a given context. 
-
-Systems that are successful at such a task may be more successful in modeling semantic representations.
-### Discussion of Biases
-### Other Known Limitations
-## Additional Information
-### Dataset Curators
-
-- 苏剑林对文件名称有整理
-- 我上传到huggingface的datasets
-
-### Licensing Information
-
-用于学术研究。
-
-The BQ corpus is free to the public for academic research.
-
-
-### Contributions
-
-Thanks to [@shibing624](https://github.com/shibing624) add this dataset.

nli_zh.py

@@ -1,143 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-@author:XuMing([email protected])
-@description:
-"""
-
-"""Natural Language Inference (NLI) Chinese Corpus.(nli_zh)"""
-
-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。"""
-
-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 NliZh(datasets.GeneratorBasedBuilder):
-    """The Natural Language Inference Chinese(NLI_zh) Corpus."""
-
-    BUILDER_CONFIGS = [
-        NliZhConfig(
-            name="ATEC",
-            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=self.config.description,
-            features=datasets.Features(
-                {
-                    "sentence1": datasets.Value("string"),
-                    "sentence2": datasets.Value("string"),
-                    "label": datasets.Value("int32"),
-                    # "idx": datasets.Value("int32"),
-                }
-            ),
-            homepage=self.config.url,
-            citation=self.config.citation,
-        )
-
-    def _split_generators(self, dl_manager):
-        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.TRAIN,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"{self.config.name}.train.data"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"{self.config.name}.valid.data"),
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "filepath": os.path.join(dl_dir, f"{self.config.name}.test.data"),
-                },
-            ),
-        ]
-
-    def _generate_examples(self, filepath):
-        """This function returns the examples in the raw (text) form."""
-        with open(filepath, 'r', encoding="utf-8") as f:
-            for idx, row in enumerate(f):
-                # print(row)
-                terms = row.split('\t')
-                yield idx, {
-                    "sentence1": terms[0],
-                    "sentence2": terms[1],
-                    "label": int(terms[2]),
-                }