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s-ttp
/
aiva-rlcd-modernbert

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
Model card Files Files and versions
xet
Community

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
aiva-rlcd-modernbert / training
111 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 2 commits
s-ttp's picture
s-ttp
Training kit: coherent specs and corrected recipe, after ML & Data review
6f9777c verified 12 days ago
  • README.md
    10.8 kB
    Training kit: coherent specs and corrected recipe, after ML & Data review 12 days ago
  • evaluate.py
    9.37 kB
    Training kit: coherent specs and corrected recipe, after ML & Data review 12 days ago
  • generate.py
    44.9 kB
    Training kit: coherent specs and corrected recipe, after ML & Data review 12 days ago
  • rlcd.py
    2.46 kB
    Add training kit and the retraining recipe to the card 12 days ago
  • taxonomy.json
    28.6 kB
    Training kit: coherent specs and corrected recipe, after ML & Data review 12 days ago
  • train.py
    15.3 kB
    Training kit: coherent specs and corrected recipe, after ML & Data review 12 days ago