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DOSSIER
zxcghoul3228/DOSSIER
DOSSIER is a text classification model from zxcghoul3228. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
DOSSIER (Density of States from Stoichiometry with Encoder Representations) maps a stoichiometric chemical formula to the electronic density of states (DOS) without requiring a crystal structure.
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1.6 GB
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.pth1.6 GB · 100%
At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- modernbert
- Access
- Public
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026
- SHA
- d0f8b3b8
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Model card
From the Hugging Face model README
DOSSIER
DOSSIER (Density of States from Stoichiometry with Encoder Representations) maps a stoichiometric chemical formula to the electronic density of states (DOS) without requiring a crystal structure.
- Encoder: ModernBERT (pretrained by distillation from a universal ML interatomic potential)
- Decoder: transposed-convolution head onto a 401-point energy grid (−10 to +10 eV vs. Fermi level)
- Units: states eV⁻¹ atom⁻¹
Demo Space: zxcghoul3228/DOSSIER_demo
Files
dos_model.pth— full inference state dict (encoder + decoder)config.json/ tokenizer files — ModernBERT tokenizer and config
Inference
Load config.json with AutoModel.from_config, wrap with the deconv head,
then load_state_dict from dos_model.pth. See the Space inference.py.
- Task
- Text Classification
- Library
- transformers
- Type
- modernbert
- License
- mit
- Created
- Aug 15, 2026
- Updated
- Aug 15, 2026