Update README.md

README.md

@@ -1,3 +1,134 @@
 ---
+annotations_creators:
+- shibing624
+language_creators:
+- shibing624
+language:
+- zh
 license: cc-by-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1M<n<10M
+source_datasets:
+- https://huggingface.co/datasets
+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 SNLI_zh
+
+## Dataset Description
+- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec)
+- **Dataset:** [zh NLI](https://huggingface.co/datasets/shibing624/nli-zh-all)
+- **Size of downloaded dataset files:** 4.7 GB
+- **Total amount of disk used:** 4.7 GB
+### Dataset Summary
+
+中文自然语言推理（NLI）数据合集（nli-zh-all）
+
+
+
+### 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.
+```
+{"text1":"借款后多长时间给打电话","text2":"借款后多久打电话啊","label":1}
+{"text1":"没看到微粒贷","text2":"我借那么久也没有提升啊","label":0}
+```
+
+### Data Fields
+The data fields are the same among all splits.
+
+- `text1`: a `string` feature.
+- `text2`: a `string` feature.
+- `label`: a classification label, with possible values including entailment(1), contradiction(0)。
+
+### Data Splits
+
+after remove None and len(text) < 1 data:
+```shell
+
+$ wc -l nli-zh-all/*
+   48818 nli-zh-all/alpaca_gpt4-train.jsonl
+    5000 nli-zh-all/amazon_reviews-train.jsonl
+  519255 nli-zh-all/belle-train.jsonl
+   16000 nli-zh-all/cblue_chip_sts-train.jsonl
+  549326 nli-zh-all/chatmed_consult-train.jsonl
+   10142 nli-zh-all/cmrc2018-train.jsonl
+  395927 nli-zh-all/csl-train.jsonl
+   50000 nli-zh-all/dureader_robust-train.jsonl
+  709761 nli-zh-all/firefly-train.jsonl
+    9568 nli-zh-all/mlqa-train.jsonl
+  455875 nli-zh-all/nli_zh-train.jsonl
+   50486 nli-zh-all/ocnli-train.jsonl
+ 2678694 nli-zh-all/simclue-train.jsonl
+  419402 nli-zh-all/snli_zh-train.jsonl
+    3024 nli-zh-all/webqa-train.jsonl
+ 1213780 nli-zh-all/wiki_atomic_edits-train.jsonl
+   93404 nli-zh-all/xlsum-train.jsonl
+ 1006218 nli-zh-all/zhihu_kol-train.jsonl
+ 8234680 total
+
+```
+
+### Data Length
+
+![len](https://huggingface.co/datasets/shibing624/nli-zh-all/resolve/main/nli-zh-all-len.png)
+
+## Dataset Creation
+### Curation Rationale
+受[m3e-base](https://huggingface.co/moka-ai/m3e-base#M3E%E6%95%B0%E6%8D%AE%E9%9B%86)启发，合并了中文高质量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。
+
+- SNLI:
+@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}
+}
+
+#### Who are the annotators?
+原作者。
+
+
+### 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.
+
+
+### Licensing Information
+
+for reasearch
+
+用于学术研究
+
+
+
+### Contributions
+
+[shibing624](https://github.com/shibing624) add this dataset.