Instructions to use missvector/ru-asd-qa-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use missvector/ru-asd-qa-bert 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="missvector/ru-asd-qa-bert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("missvector/ru-asd-qa-bert") model = AutoModelForQuestionAnswering.from_pretrained("missvector/ru-asd-qa-bert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from missvector/ru-asd-qa-bert: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/missvector/ru-asd-qa-bert/resolve/refs%2Fpr%2F1/tokenizer.json
- Command line
-
hf download hf://missvector/ru-asd-qa-bert@refs/pr/1/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/missvector/ru-asd-qa-bert/resolve/refs%2Fpr%2F1/tokenizer.json
17.1 MB
- Xet hash:
- 31cfad7e457e392bdebe2bd63796205ff3f6ab825e13da0a03d83dfbf932c919
- Size of remote file:
- 17.1 MB
- SHA256:
- 62c24cdc13d4c9952d63718d6c9fa4c287974249e16b7ade6d5a85e7bbb75626
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.