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:
- a5b1ad1e922589d07c57cfc9cf9e9071a800bd64fcaee4de26f9dbe78e11ea80
- Size of remote file:
- 954 kB
- SHA256:
- 7a800b94ca941ce2bcd7f4edd0926b8a004b3bdaf5bed606c4f03b97d25e0e08
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