Instructions to use linhanhvlog123/mbert-viquad2.0-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use linhanhvlog123/mbert-viquad2.0-qa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="linhanhvlog123/mbert-viquad2.0-qa")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("linhanhvlog123/mbert-viquad2.0-qa") model = AutoModelForQuestionAnswering.from_pretrained("linhanhvlog123/mbert-viquad2.0-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 132476c500868fc36684e4d53b1a28d28d7bc06e2e84d4a05ddf393b8af67f0b
- Size of remote file:
- 709 MB
- SHA256:
- 3851b2cf3881745081be4c656339342795b7a7ff69cc620bf1839629bee4050d
路
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