Text Classification
Transformers
Safetensors
modernbert
cross-encoder
context-compression
supercompress
neural-keep
text-embeddings-inference
Instructions to use arjunkshah21/sc-keep-crossencoder-v4-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use arjunkshah21/sc-keep-crossencoder-v4-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arjunkshah21/sc-keep-crossencoder-v4-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("arjunkshah21/sc-keep-crossencoder-v4-large") model = AutoModelForSequenceClassification.from_pretrained("arjunkshah21/sc-keep-crossencoder-v4-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
sc-keep-crossencoder-v4-large
Production Neural Keep weights for SuperCompress.
This is the model currently served on the hosted API (api.supercompress.dev → Fly sc-neural-keep).
- Architecture: ModernBERT-large cross-encoder (query-conditioned line keep)
- Task: keep / drop each context line given the current user query
- Weights:
model.safetensors(1,583,351,632 bytes) - License: MIT
Inference threshold
See sc_meta.json (inference_threshold). Hosted Fly default may override via SC_NEURAL_THRESHOLD.
Related
- Distill (smaller, not production):
arjunkshah21/sc-keep-crossencoder-v4-base-distill-v2 - Product: https://supercompress.dev
- Downloads last month
- 45
Model tree for arjunkshah21/sc-keep-crossencoder-v4-large
Base model
answerdotai/ModernBERT-large