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+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+language:
+  - en
+  - ms
+tags:
+  - cross-encoder
+  - reranker
+  - retrieval
+  - rag
+  - malaysian
+  - manglish
+  - multilingual
+license: mit
+base_model: BAAI/bge-reranker-v2-m3
+---
+
+# Aranda-Reranker-v1
+
+Cross-encoder reranker specialized for **Malaysian text**, designed to work as Stage 2 after [Aranda-v1](https://huggingface.co/rekabytes/Aranda-v1) dense retrieval.
+
+## Pipeline
+
+```
+Query → Aranda-v1 (retrieve top-25) → Aranda-Reranker-v1 (rerank) → top-5 results
+```
+
+Aranda-v1 is fast but encodes query and documents separately. Aranda-Reranker-v1 processes query+document **together** with cross-attention, catching subtle mismatches the bi-encoder misses.
+
+## Evaluation (4,149 queries, BM + Manglish + English + Cross-lingual)
+
+### Overall Pipeline vs Aranda-v1 Alone
+
+| Metric | Aranda-v1 alone | + Aranda-Reranker-v1 | Improvement |
+|---|---:|---:|---:|
+| Recall@1 | 0.8891 | **0.9311** | **+4.2** |
+| Recall@5 | 0.9961 | 0.9867 | -0.9 |
+| Recall@10 | 0.9998 | 0.9971 | -0.3 |
+| MRR | 0.9364 | **0.9563** | **+2.0** |
+
+### Per-Language Recall@1
+
+| Language | Aranda-v1 alone | + Aranda-Reranker-v1 | Improvement |
+|---|---:|---:|---:|
+| BM | 0.8431 | **0.8874** | **+4.4** |
+| Cross-lingual | 0.8500 | **0.9833** | **+13.3** |
+| English | 0.8792 | **0.8940** | **+1.5** |
+| Manglish | 0.9290 | **0.9656** | **+3.7** |
+
+The reranker improves Recall@1 on **all four languages**, with a dramatic +13.3 point gain on cross-lingual (BM↔English) retrieval.
+
+## Training
+
+Fine-tuned from `BAAI/bge-reranker-v2-m3` on 30,925 Malaysian hard-negative triplets:
+- 20K social media (Lowyat, Twitter, Facebook)
+- 5.6K formal BM QA (mesolitica common-crawl-qa)
+- 3.5K English + cross-lingual anchors (up-sampled)
+- 1.8K holdout + negation pairs
+
+Only top 4 of 24 transformer layers were fine-tuned (9.1% of parameters). Contrastive ranking loss. LR=2e-5, bf16.
+
+## Usage
+
+```python
+from sentence_transformers import SentenceTransformer, CrossEncoder
+
+# Stage 1: Dense retrieval with Aranda-v1
+retriever = SentenceTransformer("rekabytes/Aranda-v1")
+query_emb = retriever.encode([query], normalize_embeddings=True)
+doc_embs = retrieaver.encode(documents, normalize_embeddings=True)
+scores = query_emb @ doc_embs.T
+top_25 = scores.argsort()[0][-25:][::-1]
+
+# Stage 2: Rerank with Aranda-Reranker-v1
+reranker = CrossEncoder("rekabytes/Aranda-Reranker-v1")
+candidates = [documents[i] for i in top_25]
+pairs = [[query, doc] for doc in candidates]
+rerank_scores = reranker.predict(pairs)
+final_order = rerank_scores.argsort()[::-1]
+top_5 = [candidates[i] for i in final_order[:5]]
+```
+
+## Model Details
+
+- **Architecture:** XLM-RoBERTa (24 layers, 1024 hidden) with sequence classification head
+- **Base model:** BAAI/bge-reranker-v2-m3
+- **Max sequence length:** 512 tokens (query + document)
+- **Input:** `[CLS] query [SEP] document [SEP]`
+- **Output:** Single relevance score (higher = more relevant)
+- **Latency:** ~2ms per (query, document) pair on GPU

config.json

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+{
+  "add_cross_attention": false,
+  "architectures": [
+    "XLMRobertaForSequenceClassification"
+  ],
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+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "dtype": "float32",
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+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "is_decoder": false,
+  "label2id": {
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+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 8194,
+  "model_type": "xlm-roberta",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "output_past": true,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "tie_word_embeddings": true,
+  "transformers_version": "5.12.1",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 250002
+}

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tokenizer_config.json

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+  "backend": "tokenizers",
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+  "mask_token": "<mask>",
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "XLMRobertaTokenizer",
+  "unk_token": "<unk>"
+}