Instructions to use hf-tiny-model-private/tiny-random-XLNetModel 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-XLNetModel 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-XLNetModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-XLNetModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7c6d5ebbff212b284b73c9f507692ae07d062fe13233414e2d8d403455426655
- Size of remote file:
- 4.4 MB
- SHA256:
- 69ddd25b36a58123ef598f434bdb9bb4f6654928c5b3e78e3701eaee752df747
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.