Instructions to use ModelTC/bart-base-squad2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ModelTC/bart-base-squad2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ModelTC/bart-base-squad2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ModelTC/bart-base-squad2") model = AutoModelForQuestionAnswering.from_pretrained("ModelTC/bart-base-squad2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7cd2f821422c6dfefcf381ee92b230c7f10845a4556b628fade926a3c2d5650
- Size of remote file:
- 1.12 GB
- SHA256:
- ece5333e6bf0883afd48d2c77a43bce7c381048f0a320e3d0eb52d47ff7dea49
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.