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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 69a9738463b04052abfb383e58dbac77e982324ec926f4a860d03868c5a90163
- Size of remote file:
- 2.98 MB
- SHA256:
- fe552c6e14e836994413a792179921ed33c4bebbbf5c8900bc58093a52e67be1
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