Instructions to use haritzpuerto/spanbert-large-cased_SQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use haritzpuerto/spanbert-large-cased_SQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="haritzpuerto/spanbert-large-cased_SQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("haritzpuerto/spanbert-large-cased_SQuAD") model = AutoModelForQuestionAnswering.from_pretrained("haritzpuerto/spanbert-large-cased_SQuAD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83147fde23d82d8e10104a978ab5006fd2e621276f2a50c246364c4329b9d08b
- Size of remote file:
- 1.33 GB
- SHA256:
- 8ba76b516643bb7474c8bba6fb7f565c460992c32d11528898ab28f45270da15
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