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--- |
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annotations_creators: |
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- shibing624 |
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language_creators: |
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- shibing624 |
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language: |
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- zh |
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license: cc-by-4.0 |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 1M<n<10M |
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source_datasets: |
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- https://huggingface.co/datasets |
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task_categories: |
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- text-classification |
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task_ids: |
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- natural-language-inference |
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- semantic-similarity-scoring |
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- text-scoring |
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paperswithcode_id: snli |
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pretty_name: Stanford Natural Language Inference |
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--- |
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# Dataset Card for SNLI_zh |
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## Dataset Description |
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- **Repository:** [Chinese NLI dataset](https://github.com/shibing624/text2vec) |
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- **Dataset:** [zh NLI](https://huggingface.co/datasets/shibing624/nli-zh-all) |
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- **Size of downloaded dataset files:** 4.7 GB |
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- **Total amount of disk used:** 4.7 GB |
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### Dataset Summary |
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中文自然语言推理（NLI）数据合集（nli-zh-all） |
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### Supported Tasks and Leaderboards |
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Supported Tasks: 支持中文文本匹配任务，文本相似度计算等相关任务。 |
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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： |
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**Leaderboard:** [NLI_zh leaderboard](https://github.com/shibing624/text2vec) |
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### Languages |
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数据集均是简体中文文本。 |
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## Dataset Structure |
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### Data Instances |
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An example of 'train' looks as follows. |
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``` |
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{"text1":"借款后多长时间给打电话","text2":"借款后多久打电话啊","label":1} |
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{"text1":"没看到微粒贷","text2":"我借那么久也没有提升啊","label":0} |
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``` |
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### Data Fields |
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The data fields are the same among all splits. |
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- `text1`: a `string` feature. |
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- `text2`: a `string` feature. |
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- `label`: a classification label, with possible values including entailment(1), contradiction(0)。 |
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### Data Splits |
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after remove None and len(text) < 1 data: |
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```shell |
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$ wc -l nli-zh-all/* |
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48818 nli-zh-all/alpaca_gpt4-train.jsonl |
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5000 nli-zh-all/amazon_reviews-train.jsonl |
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519255 nli-zh-all/belle-train.jsonl |
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16000 nli-zh-all/cblue_chip_sts-train.jsonl |
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549326 nli-zh-all/chatmed_consult-train.jsonl |
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10142 nli-zh-all/cmrc2018-train.jsonl |
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395927 nli-zh-all/csl-train.jsonl |
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50000 nli-zh-all/dureader_robust-train.jsonl |
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709761 nli-zh-all/firefly-train.jsonl |
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9568 nli-zh-all/mlqa-train.jsonl |
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455875 nli-zh-all/nli_zh-train.jsonl |
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50486 nli-zh-all/ocnli-train.jsonl |
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2678694 nli-zh-all/simclue-train.jsonl |
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419402 nli-zh-all/snli_zh-train.jsonl |
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3024 nli-zh-all/webqa-train.jsonl |
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1213780 nli-zh-all/wiki_atomic_edits-train.jsonl |
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93404 nli-zh-all/xlsum-train.jsonl |
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1006218 nli-zh-all/zhihu_kol-train.jsonl |
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8234680 total |
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``` |
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### Data Length |
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## Dataset Creation |
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### Curation Rationale |
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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， |
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这里把这个数据集上传到huggingface的datasets，方便大家使用。 |
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### Source Data |
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#### Initial Data Collection and Normalization |
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#### Who are the source language producers? |
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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。 |
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- SNLI: |
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@inproceedings{snli:emnlp2015, |
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Author = {Bowman, Samuel R. and Angeli, Gabor and Potts, Christopher, and Manning, Christopher D.}, |
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Booktitle = {Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, |
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Publisher = {Association for Computational Linguistics}, |
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Title = {A large annotated corpus for learning natural language inference}, |
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Year = {2015} |
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} |
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#### Who are the annotators? |
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原作者。 |
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### Social Impact of Dataset |
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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. |
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Systems that are successful at such a task may be more successful in modeling semantic representations. |
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### Licensing Information |
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for reasearch |
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用于学术研究 |
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### Contributions |
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[shibing624](https://github.com/shibing624) add this dataset. |
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