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:
- a9ab223554d0e27e94faf3d117b89bf4138e593f98097c98ed43b66da4fbdb2c
- Size of remote file:
- 268 MB
- SHA256:
- 486a2cc1f691c0defde28ab7687b3b2d978dfd5fc63f7711fa3a2c05315d7060
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