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  library_name: transformers
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  tags: []
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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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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [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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- [More Information Needed]
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- ### Downstream Use [optional]
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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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- ## 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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- ### 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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- ## Evaluation
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- #### Factors
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- #### Metrics
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- ### Results
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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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- ## Environmental Impact
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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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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- #### Software
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- ## Citation [optional]
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- **BibTeX:**
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- ## Glossary [optional]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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  library_name: transformers
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  tags: []
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  ---
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+ # DACTYL Text Detector
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+ ## Training Configuration
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+ ```json
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+ {
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+ "training_split": "training",
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+ "evaluation_split": "testing",
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+ "results_path": "libauc-distilroberta-base.csv",
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+ "num_epochs": 1,
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+ "model_path": "ShantanuT01/dactyl-distilroberta-base-pretrained",
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+ "tokenizer": "distilroberta-base",
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+ "optimizer": "SOTAs",
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+ "optimizer_type": "libauc",
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+ "optimizer_args": {
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+ "lr": 1e-05
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+ },
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+ "loss_fn": "tpAUC_KL_Loss",
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+ "reset_classification_head": true,
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+ "loss_type": "libauc",
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+ "loss_fn_args": {
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+ "data_len": 466005
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+ },
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+ "needs_loss_fn_as_parameter": false,
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+ "save_path": "ShantanuT01/dactyl-distilroberta-base-finetuned",
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+ "training_args": {
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+ "batch_size": 64,
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+ "needs_sampler": true,
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+ "needs_index": true,
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+ "shuffle": false,
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+ "sampling_rate": 0.5,
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+ "apply_sigmoid": true
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+ },
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+ "best_model_path": "best-tpauc-model-distilroberta-base"
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+ }
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+ ```
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+ ## Results
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+ | model | AP Score | AUC Score | OPAUC Score | TPAUC Score |
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+ |:--------------------------------------------------------------------------------------|-----------:|------------:|--------------:|--------------:|
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+ | DeepSeek-V3 | 0.997783 | 0.999901 | 0.998984 | 0.991968 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-RedditWritingPrompts-testing | 0.385431 | 0.965153 | 0.925687 | 0.38438 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-abstracts-testing | 0.875288 | 0.985992 | 0.971279 | 0.75316 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-news-testing | 0.546747 | 0.90218 | 0.847515 | 0 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-reviews-testing | 0.385851 | 0.935973 | 0.835005 | 0 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-student_essays-testing | 0.0376482 | 0.806328 | 0.611652 | 0 |
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+ | ShantanuT01/fine-tuned-Llama-3.2-1B-Instruct-apollo-mini-tweets-testing | 0.594489 | 0.976732 | 0.917376 | 0.310269 |
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+ | claude-3-5-haiku-20241022 | 0.982784 | 0.996476 | 0.989317 | 0.899193 |
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+ | claude-3-5-sonnet-20241022 | 0.994605 | 0.999284 | 0.997636 | 0.982059 |
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+ | gemini-1.5-flash | 0.980723 | 0.997217 | 0.987467 | 0.879463 |
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+ | gemini-1.5-pro | 0.964801 | 0.994032 | 0.976397 | 0.771443 |
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+ | gpt-4o-2024-11-20 | 0.983534 | 0.997288 | 0.989648 | 0.903522 |
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+ | gpt-4o-mini | 0.996224 | 0.999868 | 0.998646 | 0.992356 |
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+ | llama-3.2-90b | 0.971754 | 0.992887 | 0.982388 | 0.831708 |
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+ | llama-3.3-70b | 0.990585 | 0.998126 | 0.99489 | 0.953972 |
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+ | mistral-large-latest | 0.995819 | 0.999661 | 0.998056 | 0.984268 |
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+ | mistral-small-latest | 0.996215 | 0.999667 | 0.998216 | 0.986264 |
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+ | overall | 0.992324 | 0.993112 | 0.982549 | 0.834284 |