Instructions to use facebook/esm2_t33_650M_UR50D with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/esm2_t33_650M_UR50D with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="facebook/esm2_t33_650M_UR50D")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D") model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t33_650M_UR50D") - Inference
- Notebooks
- Google Colab
- Kaggle
TemporalMesh Transformer: 29.4 PPL at 48% compute — beats Mamba, new open-source architecture
#6 opened 1 day ago
by
vigneshwar234
maximum sequence length
#5 opened about 2 years ago
by
j3rk0
How to get esm2_t33_650M_UR50D Fixed embedding using in the downstram task?
#4 opened almost 3 years ago
by
xigua666
Is CLS token included in pre-training?
#3 opened almost 3 years ago
by
pipparichter