time-embed-bge-m3-lms-temporal-v2-0-multisource – AI Model by kev-KOH | AlphaNeural AI | AlphaNeural AI

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kev-KOH/
time-embed-bge-m3-lms-temporal-v2-0-multisource

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feature-extraction

sentence-transformers

text-embeddings

temporal-retrieval

text-embeddings-inference

endpoints_compatible

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Time-Embed BGE-M3 LMS Temporal v2.0 Multisource

한국어 LMS 검색에서 상대 시간 표현과 검색 의도를 함께 구분하도록 BAAI/bge-m3를 미세조정한 연구 모델입니다. 모델 구조와 손실 함수는 변경하지 않고 학습 데이터 구성을 개선했습니다.

Training data

총 234,296개 grouped training row를 사용했습니다.

Korean temporal inventory C5: 120,000
Real-query temporal LMS data: 54,296
LMS intent-control replay: 60,000

각 학습 step은 query 1개, positive 1개, negative 7개(train_group_size=8)를 사용합니다. 평가 Dev/Test의 문장 및 row ID와 학습 데이터의 중복은 모두 0건입니다.

Training

Base model: BAAI/bge-m3
Selected checkpoint: step 1250
Learning rate: 1e-6
Per-device batch size: 1
Gradient accumulation: 32
Precision: BF16
Gradient checkpointing: enabled
Seed: 42
Hardware: RTX 4070 Ti SUPER 16GB

Results

Temporal Dev

Metric	Result
Pairwise accuracy	0.951020
Margin p10	0.109466
Hard-negative violation rate	0.048980
Random-pair p95	0.576171
Near-one rate	0.014184

One-time frozen Inventory Test

Metric	Result
Pairwise accuracy	0.972028
Margin mean	0.293959
Margin p10	0.115584
Hard-negative violation rate	0.027972
Random-pair p95	0.542047
Near-one rate	0.013530

Semantic retention versus Base

LMS intent delta: +0.159000
KLUE-STS delta: +0.003883
KorSTS delta: +0.011948
KLUE-NLI delta: +0.030642
SQuADKorV1 Retrieval delta: -0.013460

Usage

python

from FlagEmbedding import BGEM3FlagModel

model = BGEM3FlagModel(
    "kev-KOH/time-embed-bge-m3-lms-temporal-v2-0-multisource",
    use_fp16=True,
)
embeddings = model.encode(
    ["일주일 전에 올라온 강의자료 찾아줘", "7일 전에 등록된 자료 보여줘"]
)["dense_vecs"]

This repository contains model weights and tokenizer/configuration files only. Frozen test examples and private evaluation artifacts are not included.