Instructions to use epwalsh/bert-xsmall-dummy with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use epwalsh/bert-xsmall-dummy with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="epwalsh/bert-xsmall-dummy")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("epwalsh/bert-xsmall-dummy") model = AutoModel.from_pretrained("epwalsh/bert-xsmall-dummy") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 009ba2a4d7e0d4915b05b7d3e501ba3df0e0e2b487133067629bec67b1368547
- Size of remote file:
- 81.3 kB
- SHA256:
- 33a88f3ab1c2cbb5515fe4cb8e992690bee786d2b711bc75f0f9f64450835e79
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.