Update README.md

README.md

@@ -91,3 +91,91 @@ model-index:
     - type: ndcg_at_10
       value: 97.86
 ---
+
+<p align="center">
+  <img src="assets/logo.webp" width="20%" alt="ModernBERT Reranker" />
+</p>
+<h1 align="center">ModernBERT-TR Reranker</h1>
+
+A 150M-parameter Turkish cross-encoder reranker to score `(query, document)` relevance.
+
+- Base model: [`ytu-ce-cosmos/modernbert-tr-base`](https://huggingface.co/ytu-ce-cosmos/modernbert-tr-base).
+- Distilled from `Qwen/Qwen3-Reranker-8B`.
+
+## Results
+
+Reranking the top-100 of a first-stage retriever ([`ytu-ce-cosmos/modernbert-tr-embed`](https://huggingface.co/ytu-ce-cosmos/modernbert-tr-embed)) at `max_seq=512`. The uplift (Δ) is the reranker's contribution.
+
+| Task | First-stage NDCG@10 | + Reranker | Δ |
+|---|---|---|---|
+| ArguAnaTR | 37.01 | **54.75** | **+17.74** |
+| SquadTRRetrieval | 75.94 | **90.11** | +14.17 |
+| SciFactTR | 77.07 | **86.34** | +9.27 |
+| TQuadRetrieval | 87.48 | **94.00** | +6.52 |
+| CQADupstackGamingRetrievalTR | 56.44 | **61.10** | +4.66 |
+| XQuADRetrieval | 95.03 | **97.86** | +2.83 |
+| **Mean Δ** | | | **+9.20** |
+
+## How was this model trained?
+
+Question answering and counter argument distillation of `Qwen3-Reranker-8B` relevance scores into the 150M cross-encoder over Turkish question answering / information retrieval data using [listwise KL](https://proceedings.mlr.press/v130/reddi21a.html).
+
+## Usage
+
+### transformers
+
+```python
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+tok = AutoTokenizer.from_pretrained("ytu-ce-cosmos/modernbert-tr-reranker")
+model = AutoModelForSequenceClassification.from_pretrained("ytu-ce-cosmos/modernbert-tr-reranker").eval()
+
+query = "Türkiye'nin başkenti neresidir?"
+docs = ["Ankara, Türkiye'nin başkentidir.", "İstanbul en kalabalık şehirdir."]
+enc = tok([query] * len(docs), docs, padding=True, truncation="longest_first",
+          max_length=8192, return_tensors="pt")
+with torch.no_grad():
+    scores = model(**enc).logits.squeeze(-1)
+ranking = sorted(zip(docs, scores.tolist()), key=lambda x: x[1], reverse=True)
+```
+
+### sentence-transformers
+
+```python
+from sentence_transformers import CrossEncoder
+model = CrossEncoder("ytu-ce-cosmos/modernbert-tr-reranker")
+scores = model.predict([(query, d) for d in docs])
+```
+
+### ONNX Runtime
+
+The `onnx/` folder has the full graph, the output is the relevance logit:
+
+```python
+import onnxruntime, numpy as np
+sess = onnxruntime.InferenceSession("onnx/model.onnx")
+feed = {k: v.numpy() for k, v in enc.items() if k in {i.name for i in sess.get_inputs()}}
+logits = sess.run(None, feed)[0].squeeze(-1)
+```
+
+### Text Embeddings Inference (TEI)
+
+```bash
+text-embeddings-router --model-id ytu-ce-cosmos/modernbert-tr-reranker --dtype float16
+# POST /rerank  {"query": "soru", "texts": ["aday 1", "aday 2"]}
+```
+
+## Training data
+
+We used Turkish datasets msmarco-tr, squad-tr, fiqa-tr, nfcorpus-tr, quora-tr, scifact-tr for distillation by `Qwen3-Reranker-8B`, and Turkish counter-argument pairs from ArguAna machine-translated with TranslateGemma-27B. All training data was text-hash chceked against every MTEB(Turkish) test split.
+
+## Limitations
+
+- Reported NDCG is rerank-of-top-100 over a first-stage retriever; absolute scores depend on that first stage.
+- int8 ONNX reorders scores meaningfully lossy for a reranker; use fp32 for quality-sensitive ranking.
+- Due to the lack of long form data in our training, the model's performance may degrade on long context input.
+
+## License & attribution
+
+- License: `apache-2.0`.