Instructions to use sehandev/koelectra-qa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sehandev/koelectra-qa 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="sehandev/koelectra-qa")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("sehandev/koelectra-qa") model = AutoModelForQuestionAnswering.from_pretrained("sehandev/koelectra-qa", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sehandev/koelectra-qa: direct link, hf CLI and curl.
- Browser
- Download file 449 MB
-
https://huggingface.co/sehandev/koelectra-qa/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sehandev/koelectra-qa/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sehandev/koelectra-qa/resolve/main/pytorch_model.bin
449 MB
- Xet hash:
- 014ba90878022a39d92e1bafbdecf38378cd6dc8855241b3232b17cd48beff0e
- Size of remote file:
- 449 MB
- SHA256:
- f7841d869d24ecd03bdbfa67888356425b00fc450e59af83634bf93110625df8
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.