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
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
Commit History
11 days ago
-
Link PICK V5 leaderboard registration PR #233
Verified signature0d3d2fa -
Report complete PICK V5 public Decision Index score 32.09
Verified signaturebc1cac2
12 days ago
-
Add frozen V5 Decision Index inference wrapper; preserve all model weights
Verified signaturef1635cf -
Add frozen V5 Decision Index inference wrapper; preserve all model weights
Verified signature9a17cce -
docs: clarify Evaluated Questions (440 = 290 JevAlt + 150 Fast) and exact calibrated metrics
Verified signature9980b9a -
feat: publish official PICK-150M V5 (68.84% Raw / 72.1% Heldout)
Verified signature2a6e76d -
docs: replace LaTeX symbols with clean Unicode in README.md
Verified signatured1d903b -
docs: refine Model Card to focus on standalone architecture, multi-stage curriculum, and raw evaluation
Verified signature449c626 -
release: PICK-150M V4 Auto-Calibrated 8K Decision Transformer (68.21% Raw / 70.40% Calibrated)
Verified signature195c694