Instructions to use intanm/mbert-squadv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mbert-squadv2 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "question-answering" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("question-answering", model="intanm/mbert-squadv2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mbert-squadv2") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mbert-squadv2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from intanm/mbert-squadv2: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/intanm/mbert-squadv2/resolve/main/training_args.bin
- Command line
-
hf download hf://intanm/mbert-squadv2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/intanm/mbert-squadv2/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 7a7bf0e4e52cd657626dcdf710dfd52ba1894a3a01a3940a59ed2836e67cf5ef
- Size of remote file:
- 3.58 kB
- SHA256:
- 924f43dc3924363f4a915b0b85cebb724e9f0d88af28e4dc69ed3c8b43e5249a
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