Instructions to use alenusch/par_cls_bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alenusch/par_cls_bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="alenusch/par_cls_bert")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("alenusch/par_cls_bert") model = AutoModel.from_pretrained("alenusch/par_cls_bert") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 94fc6c50b3a5baee2b696daa74f3aeb31946f463a07c9abd999b3f1e767815bb
- Size of remote file:
- 711 MB
- SHA256:
- ff01c0908f8f9987eb4bd777564be972a5412f4e882beb9d050e37ae67e13030
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.