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library_name: transformers
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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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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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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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Use the code below to get started with the model.
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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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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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library_name: transformers
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license: mit
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language:
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- zh
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pipeline_tag: text2text-generation
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---
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# AutoPersona
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这是一个基于[萌娘百科](https://zh.moegirl.org.cn/%E8%90%8C%E5%A8%98)的原始文本从[GPT-4o](https://openai.com/index/hello-gpt-4o/)中蒸馏下来的自动从未预处理的脏数据中总结角色人设的中文[llama-3模型](https://huggingface.co/KomeijiForce/Meta-Llama-3-8B-AutoPersona-Chinese),使用该模型可以免费地从大量未经预处理的脏文本中迅速地抽取有用的人设信息,可以帮助构建大规模的人设数据。
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## 使用
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本工具非常容易使用,功能被完全封装在```auto_persona(character, passage)```这个function之中,以下是一个使用例:
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```python
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from autopersona import AutoPersona
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model = AutoPersona("KomeijiForce/Meta-Llama-3-8B-AutoPersona-Chinese")
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character = "古明地恋"
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passage = '''{| style="text-align:left"
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| """能力""" || 操纵无意识程度的能力
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| """危险度""" || 不明
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| """人类友好度""" || 完全没有
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| """主要活动场所""" || 不明
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|}
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跟姐姐觉一样的妖怪,觉。只不过她是一个把自己的内心关闭了,没办法读出他人的内心的觉。'''
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print(auto_persona(character, passage))
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```
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你将会看到如下的输出
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```
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古明地恋是一位拥有操纵无意识程度能力的妖怪,跟姐姐觉一样的觉。然而,她的内心已被关闭,无法读出他人的内心。她的危险度不明,人类友好度为完全没有,主要活动场所不明。
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```
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当模型认为```passage```无法被转化为人设信息时,会固定输出“无用信息。”
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```python
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character = "古明地恋"
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passage = '''== 经历 =='''
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print(model.auto_persona(character, passage))
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```
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你将会看到“无用信息。”作为输出
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## 应用
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你可以在这个萌娘百科的原始文本[milashkaarshif/MoeGirlPedia_wikitext_raw_archive](https://huggingface.co/datasets/milashkaarshif/MoeGirlPedia_wikitext_raw_archive)上使用AutoPersona,但是我在使用load_dataset的时候遇到了问题,所以你可以看使用我从该repo中的.gz文件中得到的2024年5月的萌娘百科的原始文本[(KomeijiForce/moe_girl_wiki)](KomeijiForce/moe_girl_wiki)上使用。
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