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:
- e717761970f325150a14b50f682365c5140614ba1faaeaa77d30d7c115ee39b6
- Size of remote file:
- 1.06 MB
- SHA256:
- 217d1e335b1515336ba927a3cf8a00305955082bee28206de98c9d92bd71ed78
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