Instructions to use hf-tiny-model-private/tiny-random-MPNetModel 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-MPNetModel 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-MPNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-MPNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-MPNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f707c37bd10ac6c7731750c8526f442257e05be60d4a54522907e3f7563333ee
- Size of remote file:
- 973 kB
- SHA256:
- 1cfd08e233b45c28711248d457b4ec329fa802ed7806d3ef40e8d6c32780a98a
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