Instructions to use badalsahani/oneAPI_QA_Model_kaggle with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use badalsahani/oneAPI_QA_Model_kaggle with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="badalsahani/oneAPI_QA_Model_kaggle")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("badalsahani/oneAPI_QA_Model_kaggle") model = AutoModelForQuestionAnswering.from_pretrained("badalsahani/oneAPI_QA_Model_kaggle") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3e0e7c67a6689c9745b03116d11a7d693bfa10286969737eba30aad0f2032da4
- Size of remote file:
- 265 MB
- SHA256:
- efe40a8f108bff1ca7fa078d7266c9969e992985400d0c0361d747d61aed7450
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.