Create README.md

README.md

@@ -1,3 +1,184 @@
----
-license: apache-2.0
----
+---
+annotations_creators:
+- shibing624
+language_creators:
+- shibing624
+languages:
+- zh
+licenses:
+- cc-by-4-0
+multilinguality:
+- monolingual
+size_categories:
+- 100K<n<20M
+source_datasets:
+- extended|other-flicker-30k
+- 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
+- text-scoring
+task_ids:
+- natural-language-inference
+- semantic-similarity-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。
+可以从数据集对应的链接自行下载，也可以从[百度网盘(提取码:qkt6)](https://pan.baidu.com/s/1d6jSiU1wHQAEMWJi7JJWCQ)下载。其中senteval_cn目录是评测数据集汇总，senteval_cn.zip是senteval目录的打包，两者下其一就好。
+
+ATEC: https://github.com/IceFlameWorm/NLP_Datasets/tree/master/ATEC (重新划分了train、valid和test)
+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 (只保留了中文部分)
+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
+The hypotheses were elicited by presenting crowdworkers with captions from preexisting datasets without the associated photos, but the vocabulary of the hypotheses still reflects the content of the photos as well as the caption style of writing (e.g. mostly present tense). The dataset developers report 37,026 distinct words in the corpus, ignoring case. They allowed bare NPs as well as full sentences. Using the Stanford PCFG Parser 3.5.2 (Klein and Manning, 2003) trained on the standard training set as well as on the Brown Corpus (Francis and Kucera 1979), the authors report that 74% of the premises and 88.9% of the hypotheses result in a parse rooted with an 'S'. The corpus was developed between 2014 and 2015.
+Crowdworkers were presented with a caption without the associated photo and asked to produce three alternate captions, one that is definitely true, one that might be true, and one that is definitely false. See Section 2.1 and Figure 1 for details (Bowman et al., 2015).
+The corpus includes content from the [Flickr 30k corpus](http://shannon.cs.illinois.edu/DenotationGraph/) and the [VisualGenome corpus](https://visualgenome.org/). The photo captions used to prompt the data creation were collected on Flickr by [Young et al. (2014)](https://www.aclweb.org/anthology/Q14-1006.pdf), who extended the Flickr 8K dataset developed by [Hodosh et al. (2013)](https://www.jair.org/index.php/jair/article/view/10833). Hodosh et al. collected photos from the following Flickr groups: strangers!, Wild-Child (Kids in Action), Dogs in Action (Read the Rules), Outdoor Activities, Action Photography, Flickr-Social (two or more people in the photo). Young et al. do not list the specific groups they collected photos from. The VisualGenome corpus also contains images from Flickr, originally collected in [MS-COCO](https://cocodataset.org/#home) and [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/).
+The premises from the Flickr 30k corpus corrected for spelling using the Linux spell checker and ungrammatical sentences were removed. Bowman et al. do not report any normalization, though they note that punctuation and capitalization are often omitted.
+#### 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.