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
| { | |
| "bundle_version": "1.0.0", | |
| "model_architecture": "knowledgator/gliclass-modern-base-v2.0", | |
| "checkpoint_path": "artifacts/v2/model.safetensors", | |
| "safetensors_sha256": "d252823994d47a7933217fc86449493299643af6a0c0d83d6bd5a7666d3253ef", | |
| "onnx_path": "artifacts/v2/model.onnx", | |
| "onnx_sha256": "4ae01f822538b000fa0e55859d4b3e6b40871d860149397e8784428b2a42ee5e", | |
| "opset_version": 17, | |
| "max_capacity_logits": 25, | |
| "export_date": "2026-09-17T16:59:04Z" | |
| } |