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:
- 1220bd0ec2f924fb9d0a5f69daa781d74670e81e92f46b523410a85e8fc1a500
- Size of remote file:
- 18.2 MB
- SHA256:
- 0610e0f8cd7179012179a76ade7ec938ca955e24d5f5969824b8c94f903cd2cc
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