Instructions to use MMars/Question_Answering_AraBERT_xtreme_ar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MMars/Question_Answering_AraBERT_xtreme_ar 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="MMars/Question_Answering_AraBERT_xtreme_ar")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("MMars/Question_Answering_AraBERT_xtreme_ar") model = AutoModelForQuestionAnswering.from_pretrained("MMars/Question_Answering_AraBERT_xtreme_ar", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from MMars/Question_Answering_AraBERT_xtreme_ar: direct link, hf CLI and curl.
- Browser
- Download file 1.68 MB
-
https://huggingface.co/MMars/Question_Answering_AraBERT_xtreme_ar/resolve/main/tokenizer.json
- Command line
-
hf download hf://MMars/Question_Answering_AraBERT_xtreme_ar/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/MMars/Question_Answering_AraBERT_xtreme_ar/resolve/main/tokenizer.json
1.68 MB
- Xet hash:
- 62df971ba04e7d3689d7e3863a402ae4c962d2398bd35844bb7903e7a069e977
- Size of remote file:
- 1.68 MB
- SHA256:
- 4f0660cad28b932ffe3dcc31d9fe35444d2e381ad41f92105eb7ec76196544b4
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