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  <!-- Provide a quick summary of what the model is/does. -->
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- This modelcard aims to detect text that was generated by LLMs._hub/templates/modelcard_template.md?plain=1).
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  ## Model Details
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  ### Model Description
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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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  <!-- 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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  <!-- 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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  #### 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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  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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  #### 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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  <!-- Provide a quick summary of what the model is/does. -->
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+ This modelcard aims to detect text that was generated by LLMs.
 
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  ## Model Details
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  ### Model Description
 
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+ - **Developed by:** huolongguo10
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+ - **Model type:** bert
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+ - **Language(s) (NLP):** Chinese
 
 
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  - **License:** [More Information Needed]
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+ - **Finetuned from model [optional]:** bert-base-chinese
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  ### Model Sources [optional]
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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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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForMaskedLM
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ tokenizer = AutoTokenizer.from_pretrained("huolongguo10/LLM_detect")
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+ model = AutoModelForMaskedLM.from_pretrained("huolongguo10/LLM_detect")
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+ ```
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  ## Training Details
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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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+
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  ### Training Procedure
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  #### Training Hyperparameters
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+ - **Training regime:** fp32 <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
 
 
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  ## Evaluation
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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:** P100
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+ - **Hours used:** 4h
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+ - **Cloud Provider:** kaggle
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  ## Technical Specifications [optional]
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  ### Model Architecture and Objective
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+ bert
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  ### Compute Infrastructure
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  #### Hardware
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+ P100
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  #### Software
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+ transformers
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  ## Citation [optional]
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