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")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("heman10x/rlcd-modernbert-151m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx from heman10x/rlcd-modernbert-151m: direct link, hf CLI and curl.
- Browser
- Download file 606 MB
-
https://huggingface.co/heman10x/rlcd-modernbert-151m/resolve/62fa0cef551363499d2a7031f48c36ac2cb73c31/model.onnx
- Command line
-
hf download hf://heman10x/rlcd-modernbert-151m@62fa0cef551363499d2a7031f48c36ac2cb73c31/model.onnx
-
curl -L -o model.onnx https://huggingface.co/heman10x/rlcd-modernbert-151m/resolve/62fa0cef551363499d2a7031f48c36ac2cb73c31/model.onnx
606 MB
- Xet hash:
- 1c4deb4cb032516ce2aeb3dd9759e36168f9edeece36235ec0c699e0e696afae
- Size of remote file:
- 606 MB
- SHA256:
- 551bdddb8ac9ab49ada15c40c57818021ca82c9b102f48915986947333da7d0c
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