Instructions to use nhanv/electra-large-vn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nhanv/electra-large-vn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="nhanv/electra-large-vn")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("nhanv/electra-large-vn") model = AutoModelForMaskedLM.from_pretrained("nhanv/electra-large-vn", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from nhanv/electra-large-vn: direct link, hf CLI and curl.
- Browser
- Download file 1.47 GB
-
https://huggingface.co/nhanv/electra-large-vn/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://nhanv/electra-large-vn/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/nhanv/electra-large-vn/resolve/main/flax_model.msgpack
1.47 GB
- Xet hash:
- 65f5c41d1d4f76cef302a75daf1ee3f899a7eacdc39552f7aa6559af125ac7c0
- Size of remote file:
- 1.47 GB
- SHA256:
- 4579de6a7758c21b47b96b15583818753560a26010abf16db83dc0f45ed92074
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.