Instructions to use hf-tiny-model-private/tiny-random-Data2VecTextModel 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-Data2VecTextModel 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-Data2VecTextModel")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextModel") model = AutoModel.from_pretrained("hf-tiny-model-private/tiny-random-Data2VecTextModel") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f7d852796f685a0cc5d0e2e7c247c9672a26573d2a16921ae093a1aef156ed38
- Size of remote file:
- 371 kB
- SHA256:
- 0aef06833dcaad28a6b03ef35fe5db92f0d8729561d1d41e3cdd98cec456e6b6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.