Instructions to use hf-tiny-model-private/tiny-random-RoCBertModel 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-RoCBertModel 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-RoCBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-RoCBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c2ffce3681b725d41d7d3123cb93e69a96b76bf85449e96cc5f5440f4f4643ea
- Size of remote file:
- 2.98 MB
- SHA256:
- 6e0c27e94645c9e333e8b75b069b1df01ae371d27d8aa7fa2944a25d58be9b64
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