Instructions to use Data-Lab/multilingual-e5-base_censor_v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Lab/multilingual-e5-base_censor_v0.2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Data-Lab/multilingual-e5-base_censor_v0.2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Data-Lab/multilingual-e5-base_censor_v0.2") model = AutoModelForSequenceClassification.from_pretrained("Data-Lab/multilingual-e5-base_censor_v0.2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Data-Lab/multilingual-e5-base_censor_v0.2: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/Data-Lab/multilingual-e5-base_censor_v0.2/resolve/main/tokenizer.json
- Command line
-
hf download hf://Data-Lab/multilingual-e5-base_censor_v0.2/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Data-Lab/multilingual-e5-base_censor_v0.2/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- 5472040f6d721c0f730c2b2502cd8b10ca5ca6ac6b4d3f104c0ad2776a9b6a7d
- Size of remote file:
- 17.1 MB
- SHA256:
- f1cc44ad7faaeec47241864835473fd5403f2da94673f3f764a77ebcb0a803ec
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