position_bank_test / README.md
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---
tags:
- eeg
- neuroscience
- foundation-model
- pytorch
license: other
extra_gated_prompt: >-
## EEG FOUNDATION MODEL RESPONSIBLE USE AGREEMENT
This model is available for general use (research, commercial, and personal), provided strictly that you adhere to the following privacy and safety standards. By requesting access, you agree to be bound by the following ethical principles and the regulatory guidance outlined in **EDPB Opinion 28/2024**.
1. **No Privacy Intrusion or Reconstruction**
You acknowledge that AI models trained on personal data may not be fully anonymous and can be vulnerable to attacks. You expressly agree **NOT** to:
- Attempt to extract, infer, or reconstruct subject-level EEG data or personal information from the model weights or outputs.
- Perform "Model Inversion" or "Membership Inference" attacks to extract statistical data related to specific individuals.
- Attempt to re-identify individuals from the model's embeddings.
2. **No Harm, Surveillance, or Discrimination**
In line with protecting fundamental rights, you will not use this model for:
- **Biometric Identification:** Continuous monitoring, behavioral profiling, or identification of natural persons.
- **Discrimination:** Any purpose that leads to unfair treatment of individuals or groups, or exploits vulnerabilities (e.g., age, disability).
- **Manipulation:** Coercing or exploiting users, particularly vulnerable populations, or infringing on human autonomy.
3. **Fair Use, Security, and Data Minimisation**
If you deploy this model, you accept accountability for the processing. You must:
- **Minimize Data:** Ensure any additional data used with the model is limited, pseudonymised where possible, and securely handled.
- **Be Transparent:** Any research or deployment must clearly state the purpose, limitations, and safeguards implemented to protect rights.
- **Secure the Deployment:** Implement measures to prevent unauthorized access or adversarial attacks on the model.
4. **Redistribution and Access Revocation**
- **No Redistribution:** You will not share, host, or distribute the model weights or derivatives to users without permission; they must access the model via this repository to agree to these terms.
- **Dataset Withdrawal:** If any underlying dataset becomes closed or restricted, access to this model may be revoked or replaced by a retrained version.
extra_gated_fields:
Full Name: text
Organization / Entity: text
I want to use this model for:
type: select
options:
- Research
- Education
- label: Other
value: other
geo: ip_location
I agree to the non-identification, no-harm, and privacy terms above: checkbox
I acknowledge that access may be revoked if underlying datasets are restricted: checkbox
extra_gated_description: >-
Access is open to verified users who agree to strict privacy, no-harm, and
non-identification policies compliant with EDPB guidelines.
extra_gated_button_content: Accept Terms & Request Access
---
# Model Card for Model ID
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## Model Details
### Model Description
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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.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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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.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
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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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### 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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**BibTeX:**
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**APA:**
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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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