Text Classification
Transformers
Safetensors
French
English
modernbert
devsecops
cybersecurity
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Instructions to use Medyassino/devSECV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Medyassino/devSECV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Medyassino/devSECV2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Medyassino/devSECV2") model = AutoModelForSequenceClassification.from_pretrained("Medyassino/devSECV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from Medyassino/devSECV2: direct link, hf CLI and curl.
- Browser
- Download file 5.2 kB
-
https://huggingface.co/Medyassino/devSECV2/resolve/main/training_args.bin
- Command line
-
hf download hf://Medyassino/devSECV2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Medyassino/devSECV2/resolve/main/training_args.bin
5.2 kB
- Xet hash:
- 1309b6fde40a528a2cdd6e3327ee96873c9f606af4f05312a1c88d44df099f22
- Size of remote file:
- 5.2 kB
- SHA256:
- ffde37a4dff21fba74895ef8fac9c08e54e412ff4af05c1a986b766dde44b410
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