Instructions to use hf-tiny-model-private/tiny-random-RemBertForMaskedLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-tiny-model-private/tiny-random-RemBertForMaskedLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="hf-tiny-model-private/tiny-random-RemBertForMaskedLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForMaskedLM") model = AutoModelForMaskedLM.from_pretrained("hf-tiny-model-private/tiny-random-RemBertForMaskedLM") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7717f8c9a0a3f78197e783980206c1cf3db86fc12f6eb808e4fd8525d982d7d2
- Size of remote file:
- 62.3 MB
- SHA256:
- 80e5dede9dc1a5fd1d9a2deb9d2d0ed79bf77bccccdae549a1c36c15099ff708
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