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 training_args.bin from intanm/mBERT-squad: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/intanm/mBERT-squad/resolve/main/training_args.bin
- Command line
-
hf download hf://intanm/mBERT-squad/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/intanm/mBERT-squad/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- c891664251020332250f572d6c37e1355ec7bdf8cd08b6b3b79df946dfc56dfd
- Size of remote file:
- 3.58 kB
- SHA256:
- c777769b3301431639ab58a777a4b31831bfbd340fec0a4f2df2aea5d3e0cdf1
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.