Instructions to use aiface/vi-bert-base_v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aiface/vi-bert-base_v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="aiface/vi-bert-base_v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("aiface/vi-bert-base_v1") model = AutoModelForSequenceClassification.from_pretrained("aiface/vi-bert-base_v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from aiface/vi-bert-base_v1: direct link, hf CLI and curl.
- Browser
- Download file 5.91 kB
-
https://huggingface.co/aiface/vi-bert-base_v1/resolve/main/training_args.bin
- Command line
-
hf download hf://aiface/vi-bert-base_v1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/aiface/vi-bert-base_v1/resolve/main/training_args.bin
5.91 kB
- Xet hash:
- 0ae51f83de8cf9af07e3855ff5227ab23d9431119adfde58f7618474a64e0cb5
- Size of remote file:
- 5.91 kB
- SHA256:
- bc8f3c14f3ffd3b1af338ea7ac38ca80c9638a8b0695ce8892f51fae7fc780d8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.