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
Commit History
12 days ago
-
Training kit: coherent specs and corrected recipe, after ML & Data review
Verified signaturegenerate.py: exact quotas, attachments chosen by task (13 kinds incl. policy excerpts, or your own documents/), qa/rag subject and complexity limits, identifier rules, assembled-text level check, credential pattern, reserved prompts, call budget, per-spec seeds, provider pinning; test split one verifier pass with the second recorded. train.py: pinned base revision, 2e-5 default with --init-from, unlabelled-head warning. evaluate.py: as-sampled figures with prior-weighted beside them. Card: corrected test-split and tie-break descriptions, cost split, gate notes. Weights unchanged.6f9777c -
Add training kit and the retraining recipe to the card
Verified signaturetraining/: rubric (taxonomy.json), data generation (generate.py), training (train.py), evaluation (evaluate.py) and the loader (rlcd.py), a standalone port of the recipe behind this model, tested end to end at small scale. README: 'Retrain it for your requirements'. Weights unchanged.ef729f1