Instructions to use HiAmNear/cafebert-ViFE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HiAmNear/cafebert-ViFE with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="HiAmNear/cafebert-ViFE")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("HiAmNear/cafebert-ViFE") model = AutoModelForSequenceClassification.from_pretrained("HiAmNear/cafebert-ViFE", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from HiAmNear/cafebert-ViFE: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/HiAmNear/cafebert-ViFE/resolve/main/tokenizer.json
- Command line
-
hf download hf://HiAmNear/cafebert-ViFE/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/HiAmNear/cafebert-ViFE/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 83182779330bf02f2e8bedc27659fcb1bf597a905d3060f0bbdbeb663b289b43
- Size of remote file:
- 17.1 MB
- SHA256:
- b74659c780d49afad7a7b9799868f75cbd3014fb6c34956e85a793028d38094a
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