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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 099d17ebe127a07da0be49e89caaf93a613394855b3ef3135d972d6ad17ef0db
- Size of remote file:
- 1.42 GB
- SHA256:
- 170d4ce8f7ad41b8989d9db04cdd55f83f7827c3bbae2293b55c6c0374db6199
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