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:
- f61f49ffce07941db3405d2a27050a634098794c2ddb553155793fe6ec5c0475
- Size of remote file:
- 711 MB
- SHA256:
- 222a0ce2298b4d0cf4ce2f30761835805534125449efe2c92e00a7d0bde8c728
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.