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--- |
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license: mit |
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language: |
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- en |
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tags: |
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- tau |
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- hep |
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- fcc |
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- clic |
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- ee |
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- reconstruction |
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- identification |
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- decay_mode |
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- foundation_model |
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- omnijet_alpha |
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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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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). |
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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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- **Developed by:** Joschka Birk, Anna Hallin, Gregor Kasieczka |
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- **Model type:** Transformer |
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- **Language(s) (NLP):** Pytorch |
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- **Finetuned from model:** https://doi.org/10.1088/2632-2153/ad66ad |
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The OmniJet- \\(\alpha\\) model was published in [here](https://doi.org/10.1088/2632-2153/ad66ad) was used as the base model for identifying hadronically decaying taus, reconstructing their kinematics and predicting their decay mode. |
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The base model, initially trained on [JetClass dataset](https://doi.org/10.5281/zenodo.6619768), was now fine-tuned on [Fu \\(\tau\\)ure](https://doi.org/10.5281/zenodo.13881061) dataset. |
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The models included here are for 3 separate tasks: |
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- Tau-tagging (binary classification) |
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- Tau kinematic reconstruction (regression) |
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- Tau decay mode classification (multiclass-classification) |
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And for 3 different ways of training: |
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- From scratch |
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- Fixed backbone (fine-tune only head) |
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- Fine-tuning (fine-tune both head and backbone) |
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This will add up to 9 different models. |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository (base model):** https://github.com/uhh-pd-ml/omnijet_alpha |
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- **Repository (fine-tuned model):** https://github.com/HEP-KBFI/ml-tau-en-reg |
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- **Paper:** https://doi.org/10.1088/2632-2153/ad66ad |
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## Uses |
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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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The intended use of the models is to study the feasibility of foundation models for the purposes of reconstructing and identifying hadronically decaying tau leptons. |
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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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This model is not intended for physics measurements on real data. The trainings have been done on CLIC detector simulations. |
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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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The model has only been trained on simulation data and has not been validated against real data. Although the base model has been published in a peer-reviewed journal, the fine-tuned model has not been. |
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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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```bash |
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# Clone the repository |
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git clone [email protected]:HEP-KBFI/ml-tau-en-reg.git --recursive |
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cd ml-tau-en-reg |
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# Get the models |
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git clone https://huggingface.co/LauritsT/TauRecoID models |
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``` |
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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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The data used to fine-tune the base model can be found here: [Fu \\(\tau\\)ure](https://doi.org/10.5281/zenodo.13881061) dataset |
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#### Training Hyperparameters |
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- No hyperparameter tuning has been done. <!--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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Training on 1M jets on AMD MI250x for 100 epochs takes ~8h. |
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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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Testing data can also be found in the same [Zenodo entry](https://doi.org/10.5281/zenodo.13881061) as the rest of the data. |
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#### Software |
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[Software](https://github.com/HEP-KBFI/ml-tau-en-reg/) to train and analyze the model |
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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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[OmniJet- \\(\alpha\\)](https://doi.org/10.1088/2632-2153/ad66ad) |
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## Model Card Authors [optional] |
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Laurits Tani ([email protected]) |
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## Model Card Contact |
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Laurits Tani ([email protected]) |