Instructions to use Diego2703/Phising_trained_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Diego2703/Phising_trained_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Diego2703/Phising_trained_model", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Diego2703/Phising_trained_model", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Diego2703/Phising_trained_model: direct link, hf CLI and curl.
- Browser
- Download file 10.5 MB
-
https://huggingface.co/Diego2703/Phising_trained_model/resolve/main/tokenizer.json
- Command line
-
hf download hf://Diego2703/Phising_trained_model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Diego2703/Phising_trained_model/resolve/main/tokenizer.json
10.5 MB
- Xet hash:
- de3d515370bdd99217be8486e38dca3f58c45b951a5d02cbfbe35ef62ab02cdf
- Size of remote file:
- 10.5 MB
- SHA256:
- 3ee8746f75300bd21fced16093893e714fcdc0411ea86bbf391d0e00dc6dc1d1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.