Text Classification
Transformers
modernbert
materials-science
density-of-states
composition
text-embeddings-inference
Instructions to use zxcghoul3228/DOSSIER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zxcghoul3228/DOSSIER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="zxcghoul3228/DOSSIER")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("zxcghoul3228/DOSSIER") model = AutoModelForSequenceClassification.from_pretrained("zxcghoul3228/DOSSIER", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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.
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