Text Classification
Transformers
Safetensors
modernbert
cross-encoder
context-compression
supercompress
neural-keep
distillation
text-embeddings-inference
Instructions to use arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2") model = AutoModelForSequenceClassification.from_pretrained("arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
sc-keep-crossencoder-v4-base-distill-v2
Distilled ModernBERT-base student of sc-keep-crossencoder-v4-large.
Not the production hosted model (prod serves v4-large). Useful for cheaper local / smaller-machine experiments.
- Teacher:
arjunkshah21/sc-keep-crossencoder-v4-large - Params: ~149.6M (
sc_meta.json) - Weights:
model.safetensors(~571MB) - License: MIT
See sc_meta.json for gate metrics and inference_threshold (0.42).
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Model tree for arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2
Base model
answerdotai/ModernBERT-base