Instructions to use vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQC")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQC") model = AutoModelForQuestionAnswering.from_pretrained("vnktrmnb/bert-base-multilingual-cased-FT-TyDiQA_AUQC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99ced9f6f29beec0bbcf73c87a29b8fc9a2415da53918b9791369470cb8828f1
- Size of remote file:
- 709 MB
- SHA256:
- 6d1b866294beee6535dfd7bd21221fb8f07807cd5e0086bc500aece6021bdbbb
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