Instructions to use seyonec/ChemBERTa-zinc-base-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use seyonec/ChemBERTa-zinc-base-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="seyonec/ChemBERTa-zinc-base-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("seyonec/ChemBERTa-zinc-base-v1") model = AutoModelForMaskedLM.from_pretrained("seyonec/ChemBERTa-zinc-base-v1") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#7 opened 5 days ago
by
vigneshwar234
Adding `safetensors` variant of this model
#6 opened about 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#5 opened about 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#4 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#3 opened over 1 year ago
by
SFconvertbot
Adding `safetensors` variant of this model
#2 opened about 3 years ago
by
SFconvertbot
Just note that this model is not the preferred model according to the author
👍 2
#1 opened over 3 years ago
by
Jung