Instructions to use vidarn/bert-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vidarn/bert-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vidarn/bert-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vidarn/bert-qa") model = AutoModelForQuestionAnswering.from_pretrained("vidarn/bert-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f36f0722fa0933de877bc06ae7ce46992777bff1a2ab63a850208026128be75
- Size of remote file:
- 649 MB
- SHA256:
- dc44d4b1c27c8edd84ea6407b48cdfb084043efe469d921ab673faee93e9f861
路
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