Instructions to use hf-tiny-model-private/tiny-random-RemBertModel 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-RemBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-tiny-model-private/tiny-random-RemBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RemBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-RemBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 371e3bd8d33f19216444118b333374835b65fa17048d4421afe619321f4ce438
- Size of remote file:
- 16.7 MB
- SHA256:
- 64c2619f4b7bdfaa22061f522f23964fe13667c85aa1da5df5b191109e4d2a2c
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