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 pytorch_model.bin from Mathnub/rubert-large-sberquad-1: direct link, hf CLI and curl.
- Browser
- Download file 1.7 GB
-
https://huggingface.co/Mathnub/rubert-large-sberquad-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mathnub/rubert-large-sberquad-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mathnub/rubert-large-sberquad-1/resolve/main/pytorch_model.bin
1.7 GB
- Xet hash:
- 3cbff6aa97522420026ef4393908552f301fb0395c82bc1efa3037cdf3170f77
- Size of remote file:
- 1.7 GB
- SHA256:
- 52fb97930f5eb17d3766090560e183cf3121ced94f7d13086897086489d2b77b
路
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