Instructions to use Mathnub/rubert-DPbase-sberquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathnub/rubert-DPbase-sberquad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Mathnub/rubert-DPbase-sberquad")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mathnub/rubert-DPbase-sberquad") model = AutoModelForQuestionAnswering.from_pretrained("Mathnub/rubert-DPbase-sberquad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mathnub/rubert-DPbase-sberquad: direct link, hf CLI and curl.
- Browser
- Download file 709 MB
-
https://huggingface.co/Mathnub/rubert-DPbase-sberquad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mathnub/rubert-DPbase-sberquad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mathnub/rubert-DPbase-sberquad/resolve/main/pytorch_model.bin
709 MB
- Xet hash:
- 1a284b6bd03d520f29d06c57fed77aef371c0819293d66620a989a9d16ac1b03
- Size of remote file:
- 709 MB
- SHA256:
- 70e74d62def410ee8204e18ab1f8286137ec7de4c530f7cae60dc70ced124431
路
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