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
Download tokenizer.json from heman10x/rlcd-modernbert-151m: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/heman10x/rlcd-modernbert-151m/resolve/6e143429146b6bc72e267655b82695e4215b747b/tokenizer.json
- Command line
-
hf download hf://heman10x/rlcd-modernbert-151m@6e143429146b6bc72e267655b82695e4215b747b/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/heman10x/rlcd-modernbert-151m/resolve/6e143429146b6bc72e267655b82695e4215b747b/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.