huolongguo10
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README.md
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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:**
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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]:**
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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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### 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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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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### Training Procedure
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#### Training Hyperparameters
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- **Training regime:**
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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:**
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- **Hours used:**
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- **Cloud Provider:**
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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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### Compute Infrastructure
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#### Hardware
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#### Software
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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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### 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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