Instructions to use SRDdev/HingMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SRDdev/HingMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="SRDdev/HingMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("SRDdev/HingMaskedLM") model = AutoModelForMaskedLM.from_pretrained("SRDdev/HingMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8e4ab1b62ae12dc2a32395d806241391cec2adb5c07225284d23944ae1ac3d2
- Size of remote file:
- 268 MB
- SHA256:
- 388b8532b58708e1e3cf66350fd1457f14048f29242f66a7a1b013eadfe0988f
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