Instructions to use Mathnub/ruDistilBert-base-sberquad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathnub/ruDistilBert-base-sberquad 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/ruDistilBert-base-sberquad")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mathnub/ruDistilBert-base-sberquad") model = AutoModelForQuestionAnswering.from_pretrained("Mathnub/ruDistilBert-base-sberquad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Mathnub/ruDistilBert-base-sberquad: direct link, hf CLI and curl.
- Browser
- Download file 215 MB
-
https://huggingface.co/Mathnub/ruDistilBert-base-sberquad/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Mathnub/ruDistilBert-base-sberquad/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Mathnub/ruDistilBert-base-sberquad/resolve/main/pytorch_model.bin
215 MB
- Xet hash:
- 192966c535e0727443a4616a410ff62407637ad01c73c45f87aec08e9525a976
- Size of remote file:
- 215 MB
- SHA256:
- 8d652d8ad30c7e095f9473a4e2217856ea1f8273117143c2b8b50b0e616245c8
路
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