Instructions to use SRDdev/MaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/MaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/MaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/MaskedLM") model = AutoModelForMaskedLM.from_pretrained("SRDdev/MaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- dd94c95cf30ea3eeb2a77d5969d2aa0ad50849b3a9820816189984b65f3569a1
- Size of remote file:
- 268 MB
- SHA256:
- 684d4d15a249c732336fcc7fbf1ef7537c0b5c36834f7789ed443e34c8c910b3
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