Text Classification
Transformers
Safetensors
modernbert
cross-encoder
context-compression
supercompress
neural-keep
text-embeddings-inference
# 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")Quick Links
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
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Model tree for arjunkshah21/sc-keep-crossencoder-v4-large
Base model
answerdotai/ModernBERT-large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="arjunkshah21/sc-keep-crossencoder-v4-large")