Fill-Mask
Transformers
Safetensors
modernbert
chemistry
molecules
selfies
ape-tokenizer
masked-language-modeling
Instructions to use HauserGroup/ModernMolBERT-base-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HauserGroup/ModernMolBERT-base-v0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HauserGroup/ModernMolBERT-base-v0")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HauserGroup/ModernMolBERT-base-v0") model = AutoModelForMaskedLM.from_pretrained("HauserGroup/ModernMolBERT-base-v0", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download __pycache__/tokenization_ape.cpython-313.pyc from HauserGroup/ModernMolBERT-base-v0: direct link, hf CLI and curl.
- Browser
- Download file 32.6 kB
-
https://huggingface.co/HauserGroup/ModernMolBERT-base-v0/resolve/main/__pycache__/tokenization_ape.cpython-313.pyc
- Command line
-
hf download hf://HauserGroup/ModernMolBERT-base-v0/__pycache__/tokenization_ape.cpython-313.pyc
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curl -L -o tokenization_ape.cpython-313.pyc https://huggingface.co/HauserGroup/ModernMolBERT-base-v0/resolve/main/__pycache__/tokenization_ape.cpython-313.pyc
32.6 kB
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