Text Classification
Transformers
Safetensors
English
modernbert
multi-head-classification
llm-routing
llm-gateway
model-routing
prompt-classification
query-classification
data-classification
retrieval-augmented-generation
calibration
synthetic-data
Instructions to use s-ttp/aiva-rlcd-modernbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use s-ttp/aiva-rlcd-modernbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="s-ttp/aiva-rlcd-modernbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("s-ttp/aiva-rlcd-modernbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from s-ttp/aiva-rlcd-modernbert: direct link, hf CLI and curl.
- Browser
- Download file 3.58 MB
-
https://huggingface.co/s-ttp/aiva-rlcd-modernbert/resolve/main/tokenizer.json
- Command line
-
hf download hf://s-ttp/aiva-rlcd-modernbert/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/s-ttp/aiva-rlcd-modernbert/resolve/main/tokenizer.json
3.58 MB
File too large to display, you can check the raw version instead.