Instructions to use UdS-LSV/smole-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UdS-LSV/smole-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="UdS-LSV/smole-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("UdS-LSV/smole-bert") model = AutoModelForMaskedLM.from_pretrained("UdS-LSV/smole-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bc53fc16fd772ac3298a0960fa4d873b785f302b7868feb356030ede8694b88c
- Size of remote file:
- 86.8 MB
- SHA256:
- c5c01cca8e1518154b2638e24c56ba142050d7ea217324e3cccb6bf1cda92b5d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.