Instructions to use Mathnub/rubert-large-sberquad-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathnub/rubert-large-sberquad-1 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="Mathnub/rubert-large-sberquad-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mathnub/rubert-large-sberquad-1") model = AutoModelForQuestionAnswering.from_pretrained("Mathnub/rubert-large-sberquad-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Mathnub/rubert-large-sberquad-1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Mathnub/rubert-large-sberquad-1/resolve/main/training_args.bin
- Command line
-
hf download hf://Mathnub/rubert-large-sberquad-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Mathnub/rubert-large-sberquad-1/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 63e4f2dec4d7b1a42704236d11479fabe2064ff953248f8ebbde35d5dc2b7aa3
- Size of remote file:
- 3.58 kB
- SHA256:
- 3f80e767a325d6ca242960c2eecb4a518af1430a55ab0ff05e488276b89a32c6
路
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