Instructions to use Jinhwan/krelectra-base-mecab with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jinhwan/krelectra-base-mecab with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("Jinhwan/krelectra-base-mecab") model = AutoModelForPreTraining.from_pretrained("Jinhwan/krelectra-base-mecab", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Jinhwan/krelectra-base-mecab: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/Jinhwan/krelectra-base-mecab/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Jinhwan/krelectra-base-mecab/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Jinhwan/krelectra-base-mecab/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- 667af9db3235175a1dbbc44e0725435fe7f26839476b7ffc6e4b2b30089b6d27
- Size of remote file:
- 443 MB
- SHA256:
- ce9c9924921d57b9baa07194b56ab2da671078535f540bf7f49b2633c1072175
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.