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lCREEl
/
pick-150m

Text Classification
Transformers
Safetensors
English
modernbert
feature-extraction
decision-model
decision-transformer
ettin
cross-encoder
zero-shot-classification
classification
calibrated-probabilities
routing
multiple-choice
tool-calling
agent-decision
text-embeddings-inference
Model card Files Files and versions
xet
Community

Instructions to use lCREEl/pick-150m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

  • Libraries
  • Transformers

    How to use lCREEl/pick-150m with Transformers:

    # Use a pipeline as a high-level helper
    from transformers import pipeline
    
    pipe = pipeline("text-classification", model="lCREEl/pick-150m")
    # pip install -U transformers accelerate
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("lCREEl/pick-150m")
    model = AutoModel.from_pretrained("lCREEl/pick-150m", device_map="auto")
  • Notebooks
  • Google Colab
  • Kaggle
pick-150m / training
15.7 kB
Ctrl+K
Ctrl+K
  • 1 contributor
History: 4 commits
lCREEl's picture
lCREEl
Add unified read-only status, resume and diagnostic entry point
48adfb0 verified about 20 hours ago
  • check_recovery.sh
    5.65 kB
    Restore diagnostic environment and checkpoint in fresh Colab runtimes about 21 hours ago
  • pipeline.sh
    3.62 kB
    Add unified read-only status, resume and diagnostic entry point about 20 hours ago
  • run_recovery.sh
    6.47 kB
    Add downloadable shell for a separate private recovery training pilot about 23 hours ago