Instructions to use Dukee2506/Cobbs_Head with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dukee2506/Cobbs_Head with Transformers:
# Load model directly from transformers import AutoModelForPreTraining model = AutoModelForPreTraining.from_pretrained("Dukee2506/Cobbs_Head", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Cobbs_Head
Model Details
- Developed by: Dukee2506 (private competition release)
- Model type: Transformer-based sequence model
- Languages: English
- License: Research-only, non-commercial
- Base model: Private pre-trained backbone (not disclosed)
Intended Use
This model is provided for a closed competition task.
It is intended to decode sequential biosignal inputs into text.
Direct Use
- Running inference on provided competition data.
Out-of-Scope Use
- Any deployment outside research/competition setting.
- Using with unrelated modalities or datasets.
Training Data
The model was adapted on a curated subset of aligned signals and transcripts.
Exact dataset details are withheld for fairness in the competition.
Evaluation
- Task: Sentence-level decoding on hidden test data
Quick Start
from transformers import AutoModelForPreTraining
model = AutoModelForPreTraining.from_pretrained("Dukee2506/Cobbs_Head")
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