---
license: mit
language:
- en
tags:
- tau
- hep
- fcc
- clic
- ee
- reconstruction
- identification
- decay_mode
- foundation_model
- omnijet_alpha
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

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).

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->

- **Developed by:** Joschka Birk, Anna Hallin, Gregor Kasieczka
- **Model type:** Transformer
- **Language(s) (NLP):** Pytorch
- **Finetuned from model:** https://doi.org/10.1088/2632-2153/ad66ad

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.
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.
The models included here are for 3 separate tasks:

- Tau-tagging (binary classification)
- Tau kinematic reconstruction (regression)
- Tau decay mode classification (multiclass-classification)

And for 3 different ways of training:

- From scratch
- Fixed backbone (fine-tune only head)
- Fine-tuning (fine-tune both head and backbone)

This will add up to 9 different models.

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository (base model):** https://github.com/uhh-pd-ml/omnijet_alpha
- **Repository (fine-tuned model):** https://github.com/HEP-KBFI/ml-tau-en-reg
- **Paper:**  https://doi.org/10.1088/2632-2153/ad66ad

## Uses

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

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.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
This model is not intended for physics measurements on real data. The trainings have been done on CLIC detector simulations.


## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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.


## How to Get Started with the Model

Use the code below to get started with the model.

```bash
# Clone the repository
git clone git@github.com:HEP-KBFI/ml-tau-en-reg.git --recursive
cd ml-tau-en-reg


# Get the models
git clone https://huggingface.co/LauritsT/TauRecoID models
```


## Training Details

### Training Data

<!-- 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. -->

The data used to fine-tune the base model can be found here: [Fu \\(\tau\\)ure](https://doi.org/10.5281/zenodo.13881061) dataset



#### Training Hyperparameters

- No hyperparameter tuning has been done. <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

Training on 1M jets on AMD MI250x for 100 epochs takes ~8h.

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics


#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

Testing data can also be found in the same [Zenodo entry](https://doi.org/10.5281/zenodo.13881061) as the rest of the data.


#### Software

[Software](https://github.com/HEP-KBFI/ml-tau-en-reg/) to train and analyze the model

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

[OmniJet- \\(\alpha\\)](https://doi.org/10.1088/2632-2153/ad66ad)
## Model Card Authors [optional]

Laurits Tani (laurits.tani@cern.ch)

## Model Card Contact

Laurits Tani (laurits.tani@cern.ch)