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--- |
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license: mit |
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language: |
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- zh |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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widget: |
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- text: '李白（701年2月28日—762年12月） [28]，字太白，号青莲居士 [20]，祖籍陇西成纪（今甘肃省秦安县），出生于蜀郡绵州昌隆县（今四川省江油市青莲乡），一说出生于西域碎叶 [29]。唐朝伟大的浪漫主义诗人，凉武昭王李暠九世孙 [16] [23]。为人爽朗大方，乐于交友，爱好饮酒作诗，名列“酒中八仙” [2]。曾经得到唐玄宗李隆基赏识，担任翰林供奉 [126]，赐金放还，游历全国，先后迎娶宰相许圉师、宗楚客的孙女。唐肃宗李亨即位后，卷入永王之乱，流放夜郎，辗转到达当涂县令李阳冰家。上元二年，去世，时年六十二 [16]。著有《李太白集》 [26]，代表作有《望庐山瀑布》《行路难》《蜀道难》《将进酒》《早发白帝城》《黄鹤楼送孟浩然之广陵》等 [2]。李白所作词赋，就其开创意义及艺术成就而言，享有极为崇高的地位，后世誉为“诗仙”，与诗圣杜甫并称“李杜”。' |
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- text: "李白，字太白，号青莲居士，又号“谪仙人”，祖籍陇西成纪（今甘肃省秦安县），唐代伟大的浪漫主义诗人，被誉为“诗仙”，与杜甫并称“李杜”。李白为人爽朗大方，爱饮酒作诗，喜交友。他深受黄老列庄思想影响，有“济苍生、安黎元”的政治抱负，但却仕途不顺，只做过一些从仕小官。天宝元年（公元742年），因好友举荐，李白被唐玄宗召见，供奉翰林，但他并未获得高位和实权，只是作为文学侍从的角色，因权贵的谗毁，于天宝三载（744年）被排挤出京，此后在江淮一带盘桓，历经磨难。安史之乱爆发后，李白因永王李璘谋反案被牵连而流放夜郎，途中写下《早发白帝城》。不久后又遇赦返回，继续过着飘荡四方的流浪生活。晚年李白投奔他的族叔、当时在当涂（今属安徽）当县令的李阳冰，不久即病逝，享年六十二岁。李白的诗歌创作具有极高的艺术成就。他的诗以抒情为主，善于从民歌、神话中汲取营养素材，构成其特有的瑰丽绚烂的色彩，是屈原以来积极浪漫主义诗歌的新高峰。他将叙事、议论、抒情三者融为一体，以气贯之，既而形成了雄奇飘逸的风格。他的诗歌既有大气磅礴、奔腾跳跃的气势和力量，又有壮丽奇伟的景象，其中也不乏清新明快的句子。李白的乐府、歌行及绝句成就为最高。其歌行，完全打破诗歌创作的一切固有格式，笔法多端，达到了极其逍遥自在、变幻莫测、摇曳多姿的神奇境界，充分体现了浪漫主义的风格。李白的绝句自然明快，飘逸潇洒，能以简洁明快的语言表达出无尽的情思。在盛唐诗人中，王维、孟浩然长于五绝，王昌龄等七绝写得很好，兼长五绝与七绝而且同臻极境的，只有李白一人。总的来说，李白是一位具有世界影响的伟大诗人，他的诗歌在中国文学史上占有重要地位，对后世产生了深远的影响。他的诗才横溢，被誉为“诗仙”，他的作品充满了浪漫主义的色彩，具有极高的艺术价值和历史意义。" |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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This model is an artificial intelligence generated text detection model trained using real human text and AI generated text (mainly including Erine-Bot 4.0, Qwen-Turbo 4.0 and ChatGPT 3.0 )Can effectively identify whether text is generated by artificial intelligence. |
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<!-- Provide a longer summary of what this model is. --> |
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- **Developed by:** [More Information Needed] |
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- **Funded by [optional]:** [More Information Needed] |
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- **Shared by [optional]:** [More Information Needed] |
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- **Model type:** [More Information Needed] |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** [More Information Needed] |
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### Model Sources [optional] |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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You could implement the model with the sample if you want to classify between AI-generated text and real-text. |
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```python |
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from transformers import AutoTokenizer,AutoModelForSequenceClassification |
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tokenizer = AutoTokenizer.from_pretrained("Juner/AI-generated-text-detection-pair") |
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model = AutoModelForSequenceClassification.from_pretrained("Juner/AI-generated-text-detection-pair") |
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# 对输入进行编码并获取模型输出 |
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question = "你喜欢我吗？" |
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answer = "是的！我喜欢你！" |
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inputs = tokenizer(question+answer,padding =True,truncation=True,return_tensors="pt",max_length=512) |
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outputs = model(**inputs) |
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``` |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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[More Information Needed] |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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[More Information Needed] |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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[More Information Needed] |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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[More Information Needed] |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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[More Information Needed] |
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## More Information [optional] |
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[More Information Needed] |
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## Model Card Authors [optional] |
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## Model Card Contact |
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[More Information Needed] |
