PIXIE-Splade-v1.5

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PIXIE-Splade-v1.5

PIXIE-Splade-v1.5 is a bilingual (ko, en) SPLADE retriever, developed by TelePIX Co., Ltd. PIXIE stands for TelePIX Intelligent Embedding, representing TelePIX’s high-performance embedding technology. This model is specifically optimized for retrieval tasks in Korean and English, and demonstrates strong performance in aerospace domain. Through extensive fine-tuning and domain-specific evaluation, PIXIE shows robust retrieval quality for real-world use cases such as document understanding, technical QA, and information retrieval in aerospace and related high-precision fields. PIXIE-Splade-v1.5 outputs sparse lexical vectors that are directly compatible with inverted indexing (e.g., Lucene/Elasticsearch). Because each non-zero weight corresponds to a Ko-En subword/token, interpretability is built-in: you can inspect which tokens drive retrieval.

Why SPLADE for Search?

• Inverted Index Ready: Directly index weighted tokens in standard IR stacks (Lucene/Elasticsearch).
• Interpretable by Design: Top-k contributing tokens per query/document explain why a hit matched.
• Production-Friendly: Fast candidate generation at web scale; memory/latency tunable via sparsity thresholds.
• Hybrid-Retrieval Friendly: Combine with dense retrievers via score fusion.

Model Description

• Model Type: SPLADE Sparse Encoder

<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->

• Maximum Sequence Length: 5632 tokens
• Output Dimensionality: 50000 dimensions
• Similarity Function: Dot Product
• Language: Bilingual — Korean and English
• Domain Specialization: Aerospace Information Retrieval
• License: apache-2.0

Full Model Architecture

SparseEncoder(
  (0): MLMTransformer({'max_seq_length': 5632, 'do_lower_case': False, 'architecture': 'ModernBertForMaskedLM'})
  (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 50000})
)

Quality Benchmarks

PIXIE-Splade-v1.5 is a bilingual embedding model specialized for Korean and English retrieval tasks. It delivers consistently strong performance across a diverse set of domain-specific and open-domain benchmarks in both languages, demonstrating its effectiveness in real-world search applications. The table below presents the retrieval performance of several sparse embedding models evaluated on a variety of Korean and English benchmarks. We report Normalized Discounted Cumulative Gain (nDCG@10) scores, which measure how well a ranked list of documents aligns with ground truth relevance. Higher values indicate better retrieval quality.

All evaluations were conducted using the open-source Korean-MTEB-Retrieval-Evaluators codebase to ensure consistent dataset handling, indexing, retrieval, and nDCG@10 computation across models.

Benchmark Overview and Dataset Descriptions

To better interpret the evaluation results above, we briefly describe the characteristics and evaluation intent of each benchmark suite used in this comparison. Each benchmark is designed to assess different aspects of retrieval capability, ranging from domain-specific technical understanding to open-domain and multilingual generalization.

STELLA

STELLA is an aerospace-domain Information Retrieval (IR) benchmark constructed from NASA Technical Reports Server (NTRS) documents. It is designed to evaluate both:

• Lexical matching ability (does the retriever benefit from exact technical terms? | TCQ)
• Semantic matching ability (can the retriever match concepts even when technical terms are not explicitly used? | TAQ).

STELLA provides dual-type synthetic queries and a cross-lingual extension for multilingual evaluation while keeping the corpus in English.

6 Datasets of MTEB (Korean)

Descriptions of the benchmark datasets used for evaluation are as follows:

• Ko-StrategyQA
  A Korean multi-hop open-domain question answering dataset designed for complex reasoning over multiple documents.
• AutoRAGRetrieval
  A domain-diverse retrieval dataset covering finance, government, healthcare, legal, and e-commerce sectors.
• MIRACLRetrieval
  A document retrieval benchmark built on Korean Wikipedia articles.
• PublicHealthQA
  A retrieval dataset focused on medical and public health topics.
• BelebeleRetrieval
  A dataset for retrieving relevant content from web and news articles in Korean.
• MultiLongDocRetrieval
  A long-document retrieval benchmark based on Korean Wikipedia and mC4 corpus.

RTEB (English)

Retrieval Embedding Benchmark (RTEB), a new benchmark designed to reliably evaluate the retrieval accuracy of embedding models for real-world applications. Existing benchmarks struggle to measure true generalization, while RTEB addresses this with a hybrid strategy of open and private datasets. Its goal is simple: to create a fair, transparent, and application-focused standard for measuring how models perform on data they haven’t seen before.

Direct Use (Inverted-Index Retrieval)

import torch
import numpy as np
from collections import defaultdict
from typing import Dict, List, Tuple
from transformers import AutoTokenizer
from sentence_transformers import SparseEncoder

model_name= 'telepix/PIXIE-Splade-v1.5'
device = "cuda" if torch.cuda.is_available() else "cpu"

def _to_dense_numpy(x) -> np.ndarray:
    if hasattr(x, "to_dense"):
        return x.to_dense().float().cpu().numpy()
    if isinstance(x, torch.Tensor):
        return x.float().cpu().numpy()
    return np.asarray(x)

def _filter_special_ids(ids: List[int], tokenizer) -> List[int]:
    special = set(getattr(tokenizer, "all_special_ids", []) or [])
    return [i for i in ids if i not in special]

def build_inverted_index(
    model: SparseEncoder,
    tokenizer,
    documents: List[str],
    batch_size: int = 8,
    min_weight: float = 0.0,
) -> Tuple[Dict[int, List[Tuple[int, float]]], List[str]]:
    with torch.no_grad():
        doc_emb = model.encode_document(documents, batch_size=batch_size)
    doc_dense = _to_dense_numpy(doc_emb)

    index: Dict[int, List[Tuple[int, float]]] = defaultdict(list)

    for doc_idx, vec in enumerate(doc_dense):
        nz = np.flatnonzero(vec > min_weight)
        nz = _filter_special_ids(nz.tolist(), tokenizer)

        for token_id in nz:
            index[token_id].append((doc_idx, float(vec[token_id])))

    return index

def splade_token_overlap_inverted(
    model: SparseEncoder,
    tokenizer,
    inverted_index: Dict[int, List[Tuple[int, float]]],
    documents: List[str],
    queries: List[str],
    top_k_docs: int = 3,
    top_k_tokens: int = 5,
    min_weight: float = 0.0,
):
    for qi, qtext in enumerate(queries):
        with torch.no_grad():
            q_vec = model.encode_query(qtext)
        q_vec = _to_dense_numpy(q_vec).ravel()

        q_nz = np.flatnonzero(q_vec > min_weight).tolist()
        q_nz = _filter_special_ids(q_nz, tokenizer)

        scores: Dict[int, float] = defaultdict(float)
        per_doc_contrib: Dict[int, Dict[int, Tuple[float, float, float]]] = defaultdict(dict)

        for tid in q_nz:
            qw = float(q_vec[tid])
            postings = inverted_index.get(tid, [])
            for doc_idx, dw in postings:
                prod = qw * dw
                scores[doc_idx] += prod
                per_doc_contrib[doc_idx][tid] = (qw, dw, prod)

        ranked = sorted(scores.items(), key=lambda x: x[1], reverse=True)[:top_k_docs]

        print("\n" + "="*60)
        print(f"[Query {qi + 1}] {qtext}")
        print("="*60)

        if not ranked:
            print("→ No matching documents found.")
            continue

        for rank, (doc_idx, score) in enumerate(ranked, start=1):
            doc = documents[doc_idx]
            print(f"\n→ Rank {rank} | Score: {score:.4f}")
            print(f"  Document: \"{doc}\"")

            contrib = per_doc_contrib[doc_idx]
            if not contrib:
                print("  (No overlapping tokens)")
                continue

            top = sorted(contrib.items(), key=lambda kv: kv[1][2], reverse=True)[:top_k_tokens]
            token_ids = [tid for tid, _ in top]
            tokens = tokenizer.convert_ids_to_tokens(token_ids)

            print(f"  [Top {top_k_tokens} Contributing Tokens]")
            print(f"  {'Token':<20} {'Score (qw*dw)':>15}")
            print(f"  {'-'*35}")
            for (tid, (qw, dw, prod)), tok in zip(top, tokens):
                clean_tok = tok.replace("##", "")
                print(f"  {clean_tok:<20} {prod:15.4f}")

if __name__ == "__main__":
    print(f"Loading model: {model_name}...")
    model = SparseEncoder(model_name).to(device)
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    documents = [
        "텔레픽스는 위성 데이터를 분석하여 해양, 농업 등 다양한 분야에 솔루션을 제공합니다.",
        "고해상도 광학 위성 영상은 국방 및 정찰 목적으로 중요하게 활용됩니다.",
        "TelePIX provides advanced solutions by analyzing satellite data for ocean and agriculture.",
        "High-resolution optical satellite imagery is critical for defense and reconnaissance.",
        "Space economy creates new value through the utilization of space-based data."
    ]

    # Cross-lingual test queries :)
    queries = [
        "텔레픽스는 어떤 산업 분야에서 위성 데이터를 활용하나요?",
        "Utilization of satellite imagery for defense",
    ]

    print("Building inverted index...")
    inverted_index = build_inverted_index(
        model=model,
        tokenizer=tokenizer,
        documents=documents,
        batch_size=4,
        min_weight=0.01, # 노이즈 제거를 위해 약간의 threshold를 줄 수 있습니다.
    )

    splade_token_overlap_inverted(
        model=model,
        tokenizer=tokenizer,
        inverted_index=inverted_index,
        documents=documents,
        queries=queries,
        top_k_docs=2,
        top_k_tokens=5
    )

License

The PIXIE-Splade-v1.5 model is licensed under Apache License 2.0.

Citation

@misc{TelePIX-PIXIE-Splade-v1.5,
  title={PIXIE-Splade-v1.5},
  author={TelePIX AI Research Team and Bongmin Kim},
  year={2026},
  url={https://huggingface.co/telepix/PIXIE-Splade-v1.5}
}

Contact

If you have any suggestions or questions about the PIXIE, please reach out to the authors at [email protected].