README.md

---
language:
  - az
  - en
license: apache-2.0
library_name: pytorch
tags:
  - colbert
  - late-interaction
  - retrieval
  - information-retrieval
  - azerbaijani
  - multilingual
base_model: LocalDoc/mmBERT-base-en-az
pipeline_tag: feature-extraction
---

# ColBERT-AZ

A late-interaction retrieval model for Azerbaijani built on top of [mmBERT-base-en-az](https://huggingface.co/LocalDoc/mmBERT-base-en-az). Trained via cross-encoder distillation from [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) on a mix of native Azerbaijani and translated retrieval data.

ColBERT-AZ uses **late interaction** (token-level MaxSim scoring) rather than dense single-vector retrieval, providing higher precision in retrieval compared to bi-encoder models of similar or larger size.

## Model Details

| Property | Value |
|----------|-------|
| **Parameters** | 165M |
| **Embedding dim** | 128 (per token) |
| **Backbone** | mmBERT-base-en-az (ModernBERT) |
| **Architecture** | Late interaction (ColBERT) |
| **Query max length** | 32 tokens |
| **Document max length** | 256 tokens |
| **Languages** | Azerbaijani, English |
| **Training epochs** | 1 |

## Training

### Data

ColBERT-AZ was trained on **3 million triplets** sampled from a weighted mix of four reranked datasets:

| Source | Weight | Type |
|--------|--------|------|
| [LocalDoc/msmarco-az-reranked](https://huggingface.co/datasets/LocalDoc/msmarco-az-reranked) | 50% | Translated web search |
| [LocalDoc/azerbaijani_books_retriever_corpus-reranked](https://huggingface.co/datasets/LocalDoc/azerbaijani_books_retriever_corpus-reranked) | 25% | Native literature |
| [LocalDoc/azerbaijan_legislation_queries_passages](https://huggingface.co/datasets/LocalDoc/azerbaijan_legislation_queries_passages) | 15% | Native legal |
| [LocalDoc/azerbaijani_retriever_corpus-reranked](https://huggingface.co/datasets/LocalDoc/azerbaijani_retriever_corpus-reranked) | 10% | Native general |

All datasets include reranker scores from `bge-reranker-v2-m3`, used as teacher signal for knowledge distillation.

### Recipe

| Hyperparameter | Value |
|----------------|-------|
| Optimizer | AdamW |
| Learning rate | 1e-6 |
| Weight decay | 0.01 |
| Warmup ratio | 0.10 |
| Schedule | Cosine |
| Batch size | 16 (effective 32 via gradient accumulation) |
| Negatives per query (K) | 8 |
| False negative filter threshold | 0.9 × pos_score |
| Distillation alpha (KL weight) | 0.7 |
| Contrastive temperature | 0.05 |
| Teacher temperature | 1.0 |
| Mixed precision | BF16 |
| Epochs | 1 |
| Hardware | NVIDIA RTX 5090 (32GB) |

### Loss

Combined KL distillation + InfoNCE:

```
L = α × KL(softmax(student_scores) || softmax(teacher_scores)) + (1 − α) × InfoNCE
```

where `α = 0.7` and student scores are computed via MaxSim over [pos, neg_1, ..., neg_K].

## Evaluation

### Held-out validation

Evaluated on 4,500 held-out triplets (1,500 per native source). Each query is ranked among 1 positive and 8 hard negatives.

| Source | R@1 | R@3 | MRR | NDCG@10 |
|--------|-----|-----|-----|---------|
| Books | 0.5387 | 0.7693 | 0.6821 | 0.7584 |
| Legislation | 0.6633 | 0.8433 | 0.7679 | 0.8234 |
| Retriever (general) | 0.8340 | 0.9327 | 0.8901 | 0.9167 |
| **Macro average** | **0.6787** | **0.8484** | **0.7800** | **0.8328** |

### AZ-MIRAGE benchmark

Evaluated on the [AZ-MIRAGE](https://github.com/LocalDoc-Azerbaijan/AZ-MIRAGE) retrieval benchmark (7,373 queries, 40,448 document pool):

| Metric | Score |
|--------|-------|
| P@1 | 0.3058 |
| R@5 | 0.7518 |
| R@10 | 0.8054 |
| NDCG@5 | 0.5528 |
| NDCG@10 | 0.5704 |
| MRR@10 | 0.4930 |
| F1@10 | 0.1464 |

Comparison with bi-encoder models on AZ-MIRAGE:

| Model | Params | NDCG@10 | MRR@10 | P@1 |
|-------|--------|---------|--------|-----|
| **ColBERT-AZ (this model)** | **165M** | **0.5704** | **0.4930** | **0.3058** |
| BAAI/bge-m3 | 568M | 0.5079 | 0.4204 | 0.2310 |
| google/gemini-embedding-2-preview | API | 0.5309 | 0.4372 | 0.2338 |
| perplexity/pplx-embed-v1-4b | API | 0.5225 | 0.4361 | 0.2470 |
| microsoft/harrier-oss-v1-0.6b | 600M | 0.5168 | 0.4321 | 0.2535 |
| intfloat/multilingual-e5-large | 560M | 0.4875 | 0.4043 | 0.2264 |
| intfloat/multilingual-e5-base | 278M | 0.4672 | 0.3852 | 0.2116 |
| sentence-transformers/LaBSE | 471M | 0.2472 | 0.1944 | 0.0943 |

## Usage

This repository contains:

- `config.json`, `model.safetensors`, `tokenizer.*` — encoder backbone (mmBERT-base-en-az)
- `projection.pt` — ColBERT linear projection layer (768 → 128, no bias)

ColBERT requires both the backbone and the projection layer for correct inference.

### Loading the model

```python
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

class ColBERT(nn.Module):
    def __init__(self, model_name: str, embedding_dim: int = 128):
        super().__init__()
        self.backbone = AutoModel.from_pretrained(model_name)
        self.projection = nn.Linear(self.backbone.config.hidden_size, embedding_dim, bias=False)

    @torch.no_grad()
    def encode(self, input_ids, attention_mask, keep_mask=None):
        out = self.backbone(input_ids=input_ids, attention_mask=attention_mask, return_dict=True)
        emb = self.projection(out.last_hidden_state)
        emb = F.normalize(emb, p=2, dim=-1)
        eff_mask = attention_mask if keep_mask is None else attention_mask * keep_mask
        emb = emb * eff_mask.unsqueeze(-1).float()
        return emb, eff_mask

# Load
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("LocalDoc/colbert-az")

# Add ColBERT special tokens
tokenizer.add_special_tokens({"additional_special_tokens": ["[Q]", "[D]"]})

model = ColBERT("LocalDoc/colbert-az")
model.backbone.resize_token_embeddings(len(tokenizer))

# Load projection layer
from huggingface_hub import hf_hub_download
proj_path = hf_hub_download(repo_id="LocalDoc/colbert-az", filename="projection.pt")
model.projection.load_state_dict(torch.load(proj_path, map_location="cpu"))

model = model.to(device).eval()
```

### Encoding queries and documents

```python
# Tokenization helpers
def tokenize_query(text: str, max_len: int = 32):
    text = f"[Q] {text}"
    enc = tokenizer(text, padding="max_length", truncation=True,
                    max_length=max_len, return_tensors="pt")
    # ColBERT trick: replace pad with mask for query expansion
    pad_mask = enc["input_ids"] == tokenizer.pad_token_id
    enc["input_ids"][pad_mask] = tokenizer.mask_token_id
    enc["attention_mask"] = torch.ones_like(enc["input_ids"])
    return enc

def tokenize_doc(text: str, max_len: int = 256):
    text = f"[D] {text}"
    return tokenizer(text, padding=True, truncation=True,
                     max_length=max_len, return_tensors="pt")

# Compute MaxSim score between query and a single document
def maxsim_score(query: str, document: str) -> float:
    q_enc = {k: v.to(device) for k, v in tokenize_query(query).items()}
    d_enc = {k: v.to(device) for k, v in tokenize_doc(document).items()}

    q_emb, _ = model.encode(q_enc["input_ids"], q_enc["attention_mask"])
    d_emb, d_mask = model.encode(d_enc["input_ids"], d_enc["attention_mask"])

    # MaxSim: for each query token, take max similarity over doc tokens, then sum
    sim = torch.einsum("qld,bnd->qlbn", q_emb, d_emb)
    sim = sim.masked_fill(~d_mask.unsqueeze(0).unsqueeze(0).bool(), float("-inf"))
    max_per_token, _ = sim.max(dim=-1)
    score = max_per_token.sum(dim=1).item()
    return score

# Example
query = "Azərbaycan mədəniyyətinin tarixi"
doc = "Azərbaycan mədəniyyəti zəngin tarixə malikdir və qədim dövrlərdən başlayaraq inkişaf edib."
print(f"Score: {maxsim_score(query, doc):.4f}")
```

### Recommended retrieval pipeline

For production retrieval, use ColBERT-AZ with a proper indexing library that supports late interaction:

- [PLAID](https://github.com/stanford-futuredata/ColBERT) — official ColBERT indexing
- [pylate](https://github.com/lightonai/pylate) — modern ColBERT framework

These libraries handle efficient indexing, scalable MaxSim retrieval, and quantization for production deployment.

## Citation

```bibtex
@misc{colbert-az-2026,
  title  = {ColBERT-AZ: Late-Interaction Retrieval for Azerbaijani},
  author = {LocalDoc},
  year   = {2026},
  url    = {https://huggingface.co/LocalDoc/colbert-az}
}
```

## License

Apache 2.0

## Acknowledgements

- Built on top of [mmBERT-base-en-az](https://huggingface.co/LocalDoc/mmBERT-base-en-az)
- Distilled from [bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3)
- Original ColBERT architecture: Khattab & Zaharia (2020), [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT](https://arxiv.org/abs/2004.12832)
- ColBERTv2 distillation methodology: Santhanam et al. (2022), [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://arxiv.org/abs/2112.01488)