Unverified50% confidenceBenchmarkExact time
On the eight Persian retrieval tasks of FaMTEB, Yalda Embedding achieves a macro average score of 0.5488, the highest among the 0.6B models evaluated.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/26/2026
First Seen
Valid until: 1/24/2027
Sources
Related Entities
Related Claims
UnverifiedOn a stratified 1,000-item test subset, the strongest LLM prompting configuration achieved an accuracy of 0.477, while the fine-tuned DistilBERT reached 0.879.71% similarUnverifiedYalda Embedding approaches the performance of Qwen3-Embedding-4B, winning on four tasks and tying on one, with a macro average only 0.0075 behind.65% similarUnverifiedEmbeddingGemma 2's code retrieval score surpasses that of Qwen3 Embedding at the 0.6B scale, and falls within 2 points of models at the 8B level.65% similarUnverifiedIn testing on the full BEIR SciFact dataset (5,183 documents, 300 queries), Qwen3-Embedding-8B ranked first with 0.7953 nDCG@10, with EmbeddingGemma-300M close behind at 0.7861.64% similarUnverifiedYalda Embedding surpasses Qwen3-Embedding-0.6B and F2LLM-v2-0.6B on every task.64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1098459API
curl https://kongchang.com/api/v1/knowledge/claims/1098459MCP
get_claim(id=1098459)