Instructions to use hf-internal-testing/tiny-random-Data2VecTextModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-Data2VecTextModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-Data2VecTextModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-Data2VecTextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b6a728962e777dda421d18593c052097b0116f00ab83ffd119fb47258c17b33e
- Size of remote file:
- 371 kB
- SHA256:
- 69fdeaa1deceeb7d942bf0e98b2a4ec1727fabea0031ef622e0deef42559f747
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.