Instructions to use haritzpuerto/spanbert-large-cased_NaturalQuestionsShort with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haritzpuerto/spanbert-large-cased_NaturalQuestionsShort with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="haritzpuerto/spanbert-large-cased_NaturalQuestionsShort")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("haritzpuerto/spanbert-large-cased_NaturalQuestionsShort") model = AutoModelForQuestionAnswering.from_pretrained("haritzpuerto/spanbert-large-cased_NaturalQuestionsShort") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6923212e858ce155a647bde6632ae9cbd5cb4c5c9f4480d597e9b0ecca60d762
- Size of remote file:
- 1.33 GB
- SHA256:
- a9fc4d580c68a7dc1b298b8a962d4536f99f4c833215e48197b3532198996b7a
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