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 from transformers import pipeline pipe = pipeline("question-answering", model="Mathnub/rubert-large-sberquad-1")# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 63e4f2dec4d7b1a42704236d11479fabe2064ff953248f8ebbde35d5dc2b7aa3
- Size of remote file:
- 3.58 kB
- SHA256:
- 3f80e767a325d6ca242960c2eecb4a518af1430a55ab0ff05e488276b89a32c6
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.