Instructions to use intanm/mBERT-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intanm/mBERT-squad 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-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("intanm/mBERT-squad") model = AutoModelForQuestionAnswering.from_pretrained("intanm/mBERT-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from intanm/mBERT-squad: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/intanm/mBERT-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://intanm/mBERT-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/intanm/mBERT-squad/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- e04a498773b787dc01e353661ca8215e4a238cc1e05fbbc37f890b146a69dc48
- Size of remote file:
- 709 MB
- SHA256:
- 8228245d2e9be9c44e02c2e8bb7944cc4a9511e185f9e7e37417eb46832cae2d
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