Instructions to use xhan77/ssdlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use xhan77/ssdlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xhan77/ssdlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("xhan77/ssdlm") model = AutoModelForMaskedLM.from_pretrained("xhan77/ssdlm") - Notebooks
- Google Colab
- Kaggle
# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("xhan77/ssdlm")
model = AutoModelForMaskedLM.from_pretrained("xhan77/ssdlm")Quick Links
No model card
- Downloads last month
- 32
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="xhan77/ssdlm")