Zero-Shot Classification
sentence-transformers
Safetensors
English
qwen3_5_text
cross-encoder
nli
natural-language-inference
text-classification
hallucination-detection
rag
fact-checking
groundedness
reasoning
knowledge-distillation
grpo
qwen3.5
custom_code
Eval Results (legacy)
Instructions to use dleemiller/AbsoluteZero-nli-4b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use dleemiller/AbsoluteZero-nli-4b with sentence-transformers:
from sentence_transformers import CrossEncoder model = CrossEncoder("dleemiller/AbsoluteZero-nli-4b", trust_remote_code=True) query = "Which planet is known as the Red Planet?" passages = [ "Venus is often called Earth's twin because of its similar size and proximity.", "Mars, known for its reddish appearance, is often referred to as the Red Planet.", "Jupiter, the largest planet in our solar system, has a prominent red spot.", "Saturn, famous for its rings, is sometimes mistaken for the Red Planet." ] scores = model.predict([(query, passage) for passage in passages]) print(scores) - Notebooks
- Google Colab
- Kaggle
Commit History
4 days ago
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Citation: key, year, link
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Citation title: AbsoluteZero: Observable NLI
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Update README.md
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10 days ago
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score_many example: clean rounding + its output
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Model files: merged weights (bf16) + fast/think heads, custom model + tokenizer code
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45-dataset table: compare to finecat-nli-v2-xl
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Card: typo + date fixes, throughput, score_many, think-mode calibration numbers, limitations
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Update README.md
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Logo: slashed-zero mark (light / dark)
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OpenNLI table: add finecat-nli-v2-xl, keep the top 8
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Card: OpenNLI + 45-set results, think mode (API, demo, think vs fast), logo
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Drop the OpenNLI scoring explanation (documented on the leaderboard)
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Draft model card (TODO markers for pending OpenNLI / 45-set / think-mode results)
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initial commit
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