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
File size: 327 Bytes
bd3b758 | 1 2 3 4 5 6 7 8 9 10 | {
"model_dir": "checkpoints/sc-keep-crossencoder-v4-large",
"task": "query_conditioned_line_keep",
"keep_threshold": 0.95,
"keep_floor": 0.98,
"max_len": 128,
"base_model": "answerdotai/ModernBERT-large",
"approx_params_M": 395,
"notes": "v4 large MPS \u2014 P(keep)>=keep_threshold; gated on launch_bench_30"
} |