e5-base-hebrew-qa-v2-myd-r1
MYD Round-1 Hebrew fine-tune of intfloat/multilingual-e5-base on 15,142 persona↔CEO Q-A pairs extracted from the MYD synthetic dialog panel (CEOs: Rafael, Adel, Antonio).
Usage
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("oridror/e5-base-hebrew-qa-v2-myd-r1")
queries = ["query: מה זה MedBed?"]
passages = ["passage: MedBed הוא פרויקט לריפוי הוליסטי..."]
q_emb = model.encode(queries, normalize_embeddings=True)
p_emb = model.encode(passages, normalize_embeddings=True)
sim = (q_emb @ p_emb.T)[0][0]
print(sim)
Important: This model uses the E5-family prefix convention — always prepend
query:to queries andpassage:to documents at inference time. Forgetting the prefix will silently degrade quality.
Eval (n=500 held-out Hebrew Q-A pairs)
| Metric | Value |
|---|---|
| Accuracy@1 | 0.722 |
| Accuracy@5 | 0.852 |
| MRR@10 | 0.7774 |
Training
- Base:
intfloat/multilingual-e5-base - Loss: MultipleNegativesRankingLoss (in-batch negatives, scale=20)
- Epochs: 2
- LR: 2e-5 with 100 warmup steps
- Hardware: NVIDIA A100 80GB PCIe (RunPod)
- Run:
myd-r1-runpod-5-models(2026-04-22)
Data
Extracted from 6-ai/synthetic-panel/output/dialogs/all_dialogs.jsonl — 3,925 generated Hebrew dialogs (persona + CEO turns). Every persona-role turn paired with the immediately-following CEO-role turn yielded 15,642 Q-A pairs. Split: 15,142 train / 500 eval (seed 42).
License
Apache-2.0 (inherited from base model).
Part of MYD
This model is part of the MYD generative-system stack. Routed via myd-router policy as embed.he candidate.