Contrastive Language Models score a state against a set of candidate actions with a contrastive objective. Two projection heads on frozen Qwen3-8B embeddings, trained with InfoNCE; clm-serve maps Choice, Score and Noul onto candidate ranking.
A 364M Hebrew decision encoder on NeoDictaBERT: give it a state, a question and 2 to 7 free-text options and it returns a probability for each option in one forward pass, about 70 ms on a laptop CPU. Hebrew only.
Decides
choice, score, noul, rank, classify, route
choice, classify, route
Architecture
clm
nativ
Fine-tuned from
qwen/qwen3-8b
dicta-il/neodictabert
License
apache-2.0
cc-by-4.0
Availability
Open weights
Open weights
Hosted by
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Input price
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Decision accuracy
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96.2%
Calibration error
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0.010
Valid action rate
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Median latency
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p95 latency
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Figures are from each model’s manifest; accuracy and latency are what the publishers report, on their own suites and hardware. Add a third model.
Questions
What is the difference between clm and nativ-he-decision?
clm is from Contrastive-LM and nativ-he-decision from Yoav Pinto. Both have open weights you can download and run. Both answer choice, classify and route questions. Only clm answers score, noul and rank. nativ-he-decision is the smaller model, at 364M parameters to 8.0B. clm is licensed apache-2.0; nativ-he-decision, cc-by-4.0.
Which is more accurate, clm or nativ-he-decision?
Only nativ-he-decision publishes an accuracy figure (96.2% on nativ-bench user-intent task, 4 options (MASSIVE Hebrew, 2,973 items; the maker's own suite)); clm does not, so there is no comparison to make without your own test.
Which is cheaper, clm or nativ-he-decision?
clm: Free (open weights). nativ-he-decision: Free (open weights). Open weights cost nothing per call beyond your own hardware.
Can I run clm or nativ-he-decision locally?
Yes, both: systemone pull contrastive-lm/clm and systemone pull yoav-pinto/nativ-he-decision download the weights.
Evaluation suite
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nativ-bench user-intent task, 4 options (MASSIVE Hebrew, 2,973 items; the maker's own suite)
