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---
license: mit
language:
- zh
metrics:
- accuracy
pipeline_tag: text-classification
widget:
- 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”。'
- 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。"
---
# Model Card for Model ID
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## Model Details
### Model Description
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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## How to Get Started with the Model
You could implement the model with the sample if you want to classify between AI-generated text and real-text.
```python
from transformers import AutoTokenizer,AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("Juner/AI-generated-text-detection-pair")
model = AutoModelForSequenceClassification.from_pretrained("Juner/AI-generated-text-detection-pair")
# 对输入进行编码并获取模型输出
question = "你喜欢我吗?"
answer = "是的!我喜欢你!"
inputs = tokenizer(question+answer,padding =True,truncation=True,return_tensors="pt",max_length=512)
outputs = model(**inputs)
```
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