Instructions to use SajjadAyoubi/bert-base-fa-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SajjadAyoubi/bert-base-fa-qa 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="SajjadAyoubi/bert-base-fa-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("SajjadAyoubi/bert-base-fa-qa") model = AutoModelForQuestionAnswering.from_pretrained("SajjadAyoubi/bert-base-fa-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SajjadAyoubi/bert-base-fa-qa: direct link, hf CLI and curl.
- Browser
- Download file 649 MB
-
https://huggingface.co/SajjadAyoubi/bert-base-fa-qa/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://SajjadAyoubi/bert-base-fa-qa/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SajjadAyoubi/bert-base-fa-qa/resolve/main/flax_model.msgpack
649 MB
- Xet hash:
- cdd76373b3af7cdf870a7476692ff66a2b1ae9fc590b472cac3e9082b1f8cfe9
- Size of remote file:
- 649 MB
- SHA256:
- 60a38512e1192e5c53f22d26248ab1d8621b924447d51febe141ae59c5a4f8a3
路
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