Instructions to use vodiylik/example_qa_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vodiylik/example_qa_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vodiylik/example_qa_model")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vodiylik/example_qa_model") model = AutoModelForQuestionAnswering.from_pretrained("vodiylik/example_qa_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d44607ddb596f6edec6550dd59f92beb10a91d09f9211db8f66b2c16758ca04d
- Size of remote file:
- 4.6 kB
- SHA256:
- df710fca2bc32c2b7fd8dbc1f52f9c058e5ae958f80827e0f7ec243a49f14ef4
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.