Instructions to use salti/bert-base-multilingual-cased-finetuned-squad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use salti/bert-base-multilingual-cased-finetuned-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="salti/bert-base-multilingual-cased-finetuned-squad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("salti/bert-base-multilingual-cased-finetuned-squad") model = AutoModelForQuestionAnswering.from_pretrained("salti/bert-base-multilingual-cased-finetuned-squad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from salti/bert-base-multilingual-cased-finetuned-squad: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/salti/bert-base-multilingual-cased-finetuned-squad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://salti/bert-base-multilingual-cased-finetuned-squad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/salti/bert-base-multilingual-cased-finetuned-squad/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 6df34832c1fb5734698d7be6c29d14dc75719fd48d4952765827847f0c2e7c16
- Size of remote file:
- 709 MB
- SHA256:
- 67de22e07182063ee6bf508ad7e0e64600ecea8479c4a5a1ab9eab06558b16a4
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.