Text Classification
Transformers
Safetensors
English
modernbert
llm-as-a-judge
rag
hallucination-detection
faithfulness
nli
fact-checking
calibration
typed-decisions
Instructions to use Nefelim584/jev-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nefelim584/jev-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nefelim584/jev-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Nefelim584/jev-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder/tokenizer.json from Nefelim584/jev-lite: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/Nefelim584/jev-lite/resolve/main/encoder/tokenizer.json
- Command line
-
hf download hf://Nefelim584/jev-lite/encoder/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/Nefelim584/jev-lite/resolve/main/encoder/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.