Sophea-Nemo-Reranker is a Greek + English cross-encoder reranker, fine-tuned from NVIDIA llama-nemotron-rerank-1b-v2 for Greek and English reranking. It is the second (precision) stage of a two-stage Greek RAG stack; the matching first stage is Sophea-Nemo-Embedding.
- Creator: Kiefer SA
- Base model:
nvidia/llama-nemotron-rerank-1b-v2(LlamaBidirectionalForSequenceClassification, bidirectional, single relevance logit;trust_remote_code) - Languages: Greek + English
- Input template: a single sequence —
question:{q} \n \n passage:{p} - Training: BCE on labeled (query, doc) pairs, 1 epoch, max-len 4096, bf16
Greek is out-of-distribution for the base. The base reranker lists 26 languages and Greek is not one of them — this fine-tune adapts its trained relevance head to Greek.
Why it matters — base vs fine-tuned
Reranking the fixed top-50 candidates over 5 domains (nDCG@10), the fine-tune improves on the base Nemotron reranker on every domain:
Read. Mean nDCG@10 0.770 → 0.817 (+0.047), better on all five domains, with the largest gains on the hardest ones (medical +0.06, finance +0.05, val +0.08). In the two-stage stack it is also the strongest reranker we measured — edging a fine-tuned Qwen3-0.6B cross-encoder and beating the base by a clear margin.
Size comparison — vs base Qwen3-Reranker (off-the-shelf)
Reranking the same fixed top-50 candidates, same protocol (150 queries/domain), nDCG@10 — your fine-tuned 1B reranker edges the base Qwen3-Reranker-8B and clearly beats 4B / 0.6B, at a fraction of the size:
Read. Mean nDCG@10 0.817 (ours, 1B) vs 0.813 (Qwen3-Reranker-8B), 0.799 (4B), 0.769 (0.6B) — and ours is strongest on the hardest domain (medical 0.709 vs 8B's 0.672). A small, Greek-tuned model beats a general reranker 8× its size.
End-to-end two-stage pipeline
The two models are designed to run together: the embedder retrieves the top-50 candidates, the
reranker re-orders them. Measured end-to-end on the same 5-domain Greek+English eval (150 queries/set,
correct query:/passage: prompts):
Read. The full stack reaches mean nDCG@10 0.818 (Recall@10 0.927) — the
strongest configuration measured. The reranker adds the most where the embedder is weakest
(finance +0.061, legal +0.037), i.e. it sharpens precision on the hard
cases. On energy it slightly regresses (-0.022) and on val the gain is marginal,
because the embedder alone is already very strong there and leaves little headroom — for the easiest,
already-high-recall domains you can skip the rerank stage if latency matters.
Usage (transformers)
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
name = "KIEFERSA/Sophea-Nemo-Reranker"
tok = AutoTokenizer.from_pretrained(name, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(
name, num_labels=1, dtype=torch.bfloat16, trust_remote_code=True).eval()
def score(query, passage):
text = f"question:{query} \n \n passage:{passage}"
enc = tok(text, return_tensors="pt", truncation=True, max_length=4096)
with torch.no_grad():
return model(**enc).logits.squeeze(-1).item() # higher = more relevant
# rank a candidate list by descending score
Feed the base's own question:/passage: template — it has a trained relevance head, so matching
its template adapts that head rather than fighting it. Needs trust_remote_code=True.
Serving with vLLM
vllm serve KIEFERSA/Sophea-Nemo-Reranker --runner pooling --trust-remote-code
Exposes /rerank, /v1/rerank (Jina), /v2/rerank (Cohere) and /score:
curl http://localhost:8000/rerank -H 'Content-Type: application/json' -d '{
"model": "KIEFERSA/Sophea-Nemo-Reranker",
"query": "Ποια είναι η πρωτεύουσα της Ελλάδας;",
"documents": ["Η Αθήνα είναι η πρωτεύουσα της Ελλάδας.", "Το Παρίσι είναι στη Γαλλία."]
}'
License
Fine-tune of nvidia/llama-nemotron-rerank-1b-v2 — governed by the NVIDIA Open Model License and
the Llama 3.2 Community License; commercial use permitted under those terms (note: not
Apache-2.0). Review the base model's license before use.