Instructions to use dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa") model = AutoModelForQuestionAnswering.from_pretrained("dccuchile/bert-base-spanish-wwm-cased-finetuned-qa-mlqa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3116188c403f96d304f1c07d3cad93b0f5b04529c04544f8a4e714dc7a06ee7
- Size of remote file:
- 437 MB
- SHA256:
- c778ea192dcb50c08e7e257461a3bb049599f3244cc547dfcddec50f894a8b16
路
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