Instructions to use CaffeineThief/cysecbert-ttp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CaffeineThief/cysecbert-ttp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CaffeineThief/cysecbert-ttp")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CaffeineThief/cysecbert-ttp") model = AutoModelForSequenceClassification.from_pretrained("CaffeineThief/cysecbert-ttp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from CaffeineThief/cysecbert-ttp: direct link, hf CLI and curl.
- Browser
- Download file 5.43 kB
-
https://huggingface.co/CaffeineThief/cysecbert-ttp/resolve/main/training_args.bin
- Command line
-
hf download hf://CaffeineThief/cysecbert-ttp/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/CaffeineThief/cysecbert-ttp/resolve/main/training_args.bin
5.43 kB
- Xet hash:
- 1f66afab921163d719a4b65b6e5f190e9b86492b31bfe62441ffbdfdf1d9767b
- Size of remote file:
- 5.43 kB
- SHA256:
- 60d4495459b6ce9903fa394514ddc7dced8c47e5a1130619e4217fd20f0de788
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.