Instructions to use vuiseng9/bert-base-squadv1-qat-bt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vuiseng9/bert-base-squadv1-qat-bt with Transformers:
# Load model directly from transformers import AutoTokenizer, NNCFNetwork tokenizer = AutoTokenizer.from_pretrained("vuiseng9/bert-base-squadv1-qat-bt") model = NNCFNetwork.from_pretrained("vuiseng9/bert-base-squadv1-qat-bt", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e07ba9f6cda705963333a1bf69939bba7122763a59ade7d415ce732dbf196c50
- Size of remote file:
- 436 MB
- SHA256:
- aaf4e924f95c9f6a4edee55468346f11438ef68a0aac5b9d1d09e3c3967c92dd
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.