Instructions to use iashour/CANDLE_cvss_vector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use iashour/CANDLE_cvss_vector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="iashour/CANDLE_cvss_vector")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("iashour/CANDLE_cvss_vector") model = AutoModelForSequenceClassification.from_pretrained("iashour/CANDLE_cvss_vector", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 77d0f11159b421c60819865b50239b761f1febb465ff149fe1651decad39fd5b
- Size of remote file:
- 438 MB
- SHA256:
- 3bb6ed31bec35a204065883494a65ee00f24cb856453aeb8e271721f10ee8d2a
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