Instructions to use cuongtk2002/distilbert-base-multilingual-cased-JaQuAD with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cuongtk2002/distilbert-base-multilingual-cased-JaQuAD with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="cuongtk2002/distilbert-base-multilingual-cased-JaQuAD")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("cuongtk2002/distilbert-base-multilingual-cased-JaQuAD") model = AutoModelForQuestionAnswering.from_pretrained("cuongtk2002/distilbert-base-multilingual-cased-JaQuAD", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f5c5467c4ac343a721ccd47cd7cf1194d9c5883206386a26c54625051019f94b
- Size of remote file:
- 4.6 kB
- SHA256:
- 3b23ae05548043329a0fd37e3e8cf6f5b9e3654d6a1dade2efc6415d75cdd1b6
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