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:
- c98e7f3fd71925addf87dfa3ebd455eafc0f204c1adb33a866540357a2d4b63c
- Size of remote file:
- 206 MB
- SHA256:
- fdc535ca061cfec8472081523804a1a419c2852d9c28b2edaa27cbb8d4141a9d
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