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:
- 54bad963975bab42ef45ec1824c17f489907327dfd1423d205567450559cfeda
- Size of remote file:
- 62.3 MB
- SHA256:
- 4a5ba0f41944db75f75c135a34ea2c7696c19e9c7add86c94df0d61aaff2519c
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