Text Classification
Transformers
Safetensors
English
qwen3_5_text
feature-extraction
darwin
ztc
zero-token-classifier
decision-engine
system-one
s1mb
calibration
probabilistic-classification
Instructions to use FINAL-Bench/Darwin-27B-ZTC-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FINAL-Bench/Darwin-27B-ZTC-v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="FINAL-Bench/Darwin-27B-ZTC-v2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("FINAL-Bench/Darwin-27B-ZTC-v2") model = AutoModel.from_pretrained("FINAL-Bench/Darwin-27B-ZTC-v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
12 days ago
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Card: S1MB #1 with the leaderboard's Borda score (89.58) and Task Avg (66.46)
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Card: S1MB english-v1 #1 (merged official results)
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13 days ago
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Add files using upload-large-folder tool
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initial commit
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