Instructions to use hf-internal-testing/tiny-random-RoCBertModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-RoCBertModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-RoCBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-RoCBertModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-RoCBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 50a86c493024d00e59ad5c7bc01627d5b663efe69b49e6a616be1e91df3c4d7a
- Size of remote file:
- 2.96 MB
- SHA256:
- a8c1165bae0d24a2a54a74f17ff9cafd81d3afffb2f13f590958a07c1e03f39c
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