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
File size: 135 Bytes
963f7c0 | 1 2 3 4 | version https://git-lfs.github.com/spec/v1
oid sha256:a9fc4d580c68a7dc1b298b8a962d4536f99f4c833215e48197b3532198996b7a
size 1330256689
|