Instructions to use Mathnub/rubert-tiny-sberquad-6ep-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Mathnub/rubert-tiny-sberquad-6ep-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-tiny-sberquad-6ep-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Mathnub/rubert-tiny-sberquad-6ep-1") model = AutoModelForQuestionAnswering.from_pretrained("Mathnub/rubert-tiny-sberquad-6ep-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Mathnub/rubert-tiny-sberquad-6ep-1: direct link, hf CLI and curl.
- Browser
- Download file 3.58 kB
-
https://huggingface.co/Mathnub/rubert-tiny-sberquad-6ep-1/resolve/main/training_args.bin
- Command line
-
hf download hf://Mathnub/rubert-tiny-sberquad-6ep-1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Mathnub/rubert-tiny-sberquad-6ep-1/resolve/main/training_args.bin
3.58 kB
- Xet hash:
- 7df81f9f500bbb8a3dc273a734356ed6d54cdebd14feb240a9e13fa9bdd4a131
- Size of remote file:
- 3.58 kB
- SHA256:
- 74306f89bc6148a4ec23566dabe4148076406530180cd01661b1a9b2751d2d8e
路
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