Token Classification
Transformers
ONNX
Safetensors
modernbert
text-compression
context-compression
yscompress
Instructions to use decompute/yscompress-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use decompute/yscompress-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="decompute/yscompress-v1")# Load model directly from transformers import AutoTokenizer, YSCompressor tokenizer = AutoTokenizer.from_pretrained("decompute/yscompress-v1") model = YSCompressor.from_pretrained("decompute/yscompress-v1") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: transformers | |
| tags: | |
| - token-classification | |
| - text-compression | |
| - context-compression | |
| - modernbert | |
| - yscompress | |
| # YSCompress v1 | |
| YSCompress v1 is an internal context/token compression model package. | |
| ## Intended use | |
| This package is intended for context compression and token-level keep/discard scoring in Decompute/Recompute inference systems. | |
| ## Loading | |
| ```python | |
| from transformers import AutoConfig | |
| cfg = AutoConfig.from_pretrained("decompute/yscompress-v1", token="hf_...") | |
| print(cfg.model_type) # modernbert | |
| ``` | |
| ## Runtime note | |
| `model_type` is intentionally retained as `modernbert` for Transformers compatibility. The deployment-facing architecture alias is `YSCompressor`. | |
| ## Package contents | |
| The package includes model weights, tokenizer assets, model configuration, optional ONNX assets if present, license, and third-party notices. | |