Instructions to use hf-tiny-model-private/tiny-random-IBertModel 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-IBertModel 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-IBertModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-IBertModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-IBertModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f985de799c587911b15444a95e464d6cfaa57045e7294d27ee7344538bf74e45
- Size of remote file:
- 815 kB
- SHA256:
- f36d18b46a9a1185251f6bc072f45b5815c227c4b5c6b9f3ef4185279d84bff8
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