Token Classification
Transformers
ONNX
Safetensors
English
kompress_v2
text-compression
modernbert
lora
kompress
Instructions to use chopratejas/kompress-v2-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chopratejas/kompress-v2-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="chopratejas/kompress-v2-base")# pip install -U transformers accelerate # Load model directly from transformers import HeadroomCompressorV2 model = HeadroomCompressorV2.from_pretrained("chopratejas/kompress-v2-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Commit History
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model card: metrics re-measured on the leak-free v2.1 test split
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Add weight-only int8 ONNX (eval on dataset_v2/test n=500: f1=0.9130 must_keep_recall=0.9765 keep_rate=0.8097, fp32 agreement 99.6%)
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Add fp32 ONNX export for Headroom lightweight (no-torch) path
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kompress-v2-base initial release
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initial commit
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