Instructions to use Falconsai/question_answering_v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Falconsai/question_answering_v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Falconsai/question_answering_v2")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Falconsai/question_answering_v2") model = AutoModelForQuestionAnswering.from_pretrained("Falconsai/question_answering_v2") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd6332561fc47c376c32843ed559420934ec4328d42dea774a7c1d790b893db5
- Size of remote file:
- 4.16 kB
- SHA256:
- e8dd5fba4b0c8f192ad2f5be92e69c6c26e315df363baae504e67849c5fca30a
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