Instructions to use hf-internal-testing/tiny-random-mpnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-mpnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-mpnet")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-mpnet") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-mpnet") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d85b1e9da069eef87e8bf4cfb85ed18429e3dc280cc0e438dc05b96d10d464b4
- Size of remote file:
- 1.3 MB
- SHA256:
- 18576bf0c5b737d4e4738275ff8e6a7812a1ac46336dd4b44781bb4c4bbef81b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.