Text Classification
Transformers
ONNX
Safetensors
GLiClass
rlcd
typesafe-ai
jev
decision-engine
system-1
modernbert
gliclass
non-autoregressive
zero-token-generation
structured-outputs
calibration
expected-calibration-error
ece
brier-score
proper-scoring-rules
webgpu
edge-ai
fast-inference
banking77
Instructions to use heman10x/rlcd-modernbert-151m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use heman10x/rlcd-modernbert-151m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="heman10x/rlcd-modernbert-151m")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("heman10x/rlcd-modernbert-151m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload bundle_manifest.json with huggingface_hub
Browse files- bundle_manifest.json +11 -0
bundle_manifest.json
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{
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"bundle_version": "1.0.0",
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"model_architecture": "knowledgator/gliclass-modern-base-v2.0",
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"checkpoint_path": "artifacts/v2/openjev_modernbert.safetensors",
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"safetensors_sha256": "7a850b9dfe81c70f5aab6aa37cb2c71025170bf0fe2ecd9ddaf731a99e2f2b99",
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"onnx_path": "artifacts/v2/openjev_modernbert.onnx",
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"onnx_sha256": "551bdddb8ac9ab49ada15c40c57818021ca82c9b102f48915986947333da7d0c",
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"opset_version": 17,
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"max_capacity_logits": 25,
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"export_date": "2026-09-17T09:03:46Z"
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}
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