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:
- fff24b30ad1f6599c5af0fed46a9a35df703ba9207141b898794ad0176cec92f
- Size of remote file:
- 2.96 MB
- SHA256:
- 3a8c31fdd8f464f1a91398613d26bb347af972bafeeca7d4fcd9225f5c811420
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