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arxiv: 2309.07597 · v5 · submitted 2023-09-14 · 💻 cs.CL · cs.AI· cs.IR

C-Pack: Packed Resources For General Chinese Embeddings

Shitao Xiao , Zheng Liu , Peitian Zhang , Niklas Muennighoff , Defu Lian , Jian-Yun Nie This is my paper

Pith reviewed 2026-05-13 13:22 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.IR
keywords Chinese text embeddingsC-MTEB benchmarktext embedding modelsC-MTP datasetnatural language processingembedding trainingmultilingual embeddingssemantic similarity
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The pith

C-Pack supplies a benchmark, training dataset, and models that let Chinese text embeddings outperform all earlier ones by up to 10 percent on 35 tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents C-Pack as a bundled set of three resources aimed at general Chinese text embeddings. It includes C-MTEB, a new evaluation covering six task types across 35 datasets, C-MTP, a large collection of labeled and unlabeled Chinese text for training, and C-TEM, a family of embedding models in several sizes. The authors report that their C-TEM models exceed previous Chinese embeddings on C-MTEB by as much as 10 percent at release time. They also optimize the full training pipeline for these models and release parallel English data and models that reach state-of-the-art on the English MTEB benchmark, with the English data twice the size of the Chinese data. All components are released publicly to support further work on Chinese embeddings.

Core claim

We introduce C-Pack consisting of C-MTEB, a comprehensive Chinese embedding benchmark with 6 tasks and 35 datasets, C-MTP, a massive curated text embedding training set drawn from Chinese corpora, and C-TEM, a family of embedding models that achieve up to 10 percent higher scores than prior Chinese models on C-MTEB when trained with the integrated suite of methods.

What carries the argument

C-TEM models trained on the C-MTP dataset and evaluated on the C-MTEB benchmark.

If this is right

  • Downstream Chinese NLP systems can adopt higher-quality embeddings for retrieval, classification, and semantic similarity tasks.
  • Open release of both the benchmark and the training data allows direct replication and extension by other researchers.
  • The English models and twice-larger English data set provide a parallel resource that reaches top MTEB scores.
  • The optimized training pipeline can be applied to produce embeddings in additional languages or sizes.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same packing approach of benchmark plus data plus model could be replicated for other languages to close performance gaps.
  • If C-MTEB becomes widely adopted it may standardize evaluation and reduce hidden selection effects in future Chinese embedding papers.
  • Larger-scale training on the released C-MTP data could further widen the gap over prior methods.
  • Integration of the English and Chinese resources may support improved bilingual or multilingual embedding models.

Load-bearing premise

The C-MTEB collection of 35 datasets supplies an unbiased and comprehensive test of general Chinese embedding quality.

What would settle it

Release of a new Chinese embedding model that scores higher than the largest C-TEM variant on every C-MTEB task without using the C-MTP training data.

read the original abstract

We introduce C-Pack, a package of resources that significantly advance the field of general Chinese embeddings. C-Pack includes three critical resources. 1) C-MTEB is a comprehensive benchmark for Chinese text embeddings covering 6 tasks and 35 datasets. 2) C-MTP is a massive text embedding dataset curated from labeled and unlabeled Chinese corpora for training embedding models. 3) C-TEM is a family of embedding models covering multiple sizes. Our models outperform all prior Chinese text embeddings on C-MTEB by up to +10% upon the time of the release. We also integrate and optimize the entire suite of training methods for C-TEM. Along with our resources on general Chinese embedding, we release our data and models for English text embeddings. The English models achieve state-of-the-art performance on MTEB benchmark; meanwhile, our released English data is 2 times larger than the Chinese data. All these resources are made publicly available at https://github.com/FlagOpen/FlagEmbedding.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces C-Pack, a package of resources for general Chinese embeddings consisting of (1) C-MTEB, a benchmark spanning 6 tasks and 35 datasets, (2) C-MTP, a large curated training corpus from labeled and unlabeled Chinese text, and (3) C-TEM, a family of embedding models of varying sizes. The central claim is that the released C-TEM models outperform all prior Chinese text embeddings on C-MTEB by up to 10% at the time of release; the authors also release English data (twice the size of the Chinese data) and models that reach SOTA on MTEB.

Significance. If the performance claims hold under rigorous scrutiny, the work supplies valuable, publicly released resources that address the relative scarcity of high-quality Chinese embedding benchmarks and training data. The integration of multiple training methods into C-TEM and the dual-language release could accelerate progress in multilingual embedding research.

major comments (3)
  1. [§3] §3 (C-MTEB construction): the description of how the 35 datasets were selected and filtered lacks explicit criteria for avoiding task-specific overfitting or selection effects that could favor the proposed models; a clear protocol for dataset inclusion/exclusion is needed to substantiate the claim that C-MTEB is an unbiased measure of general Chinese embedding quality.
  2. [Table 2] Table 2 (main results): the reported gains of up to +10% are presented without standard deviations across runs, statistical significance tests, or details on the exact baseline implementations and hyper-parameters; this information is load-bearing for the central empirical claim.
  3. [§4.2] §4.2 (training procedure): the statement that the authors 'integrate and optimize the entire suite of training methods' is not accompanied by sufficient ablation results or hyper-parameter schedules to allow reproduction or assessment of whether the gains derive from data scale, model architecture, or training tricks.
minor comments (2)
  1. [Abstract] The GitHub link in the abstract should be repeated in the conclusion or data-availability statement for reader convenience.
  2. [Figure 1] Figure 1 caption could explicitly state the number of parameters for each C-TEM variant shown.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback and positive recommendation. We address each major comment below and will revise the manuscript accordingly to improve clarity and reproducibility.

read point-by-point responses

  1. Referee: [§3] §3 (C-MTEB construction): the description of how the 35 datasets were selected and filtered lacks explicit criteria for avoiding task-specific overfitting or selection effects that could favor the proposed models; a clear protocol for dataset inclusion/exclusion is needed to substantiate the claim that C-MTEB is an unbiased measure of general Chinese embedding quality.

    Authors: We agree that an explicit protocol strengthens the benchmark's credibility. In the revised manuscript we will add a dedicated subsection to §3 that details the inclusion/exclusion criteria, including steps taken to ensure task diversity, domain coverage, and avoidance of selection bias toward our training data. This protocol draws on established practices from MTEB while adapting for Chinese-specific considerations. revision: yes

  2. Referee: [Table 2] Table 2 (main results): the reported gains of up to +10% are presented without standard deviations across runs, statistical significance tests, or details on the exact baseline implementations and hyper-parameters; this information is load-bearing for the central empirical claim.

    Authors: We acknowledge the value of statistical rigor. The revision will expand the experimental section and Table 2 caption with full hyper-parameter settings and exact baseline re-implementation details (including sources and any adaptations). Standard deviations are not reported in the current version because experiments used fixed seeds for reproducibility; we will add a note on this limitation and include variance estimates from additional runs where compute permits. revision: partial

  3. Referee: [§4.2] §4.2 (training procedure): the statement that the authors 'integrate and optimize the entire suite of training methods' is not accompanied by sufficient ablation results or hyper-parameter schedules to allow reproduction or assessment of whether the gains derive from data scale, model architecture, or training tricks.

    Authors: We will revise §4.2 to include expanded ablation tables and a hyper-parameter schedule appendix. These additions will isolate the contributions of data scale, contrastive objectives, and other optimizations, enabling readers to assess the sources of improvement. revision: yes

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

This is an applied resource paper; the central claims rest on standard machine-learning practices for contrastive embedding training rather than new axioms or invented entities.

axioms (1)
  • domain assumption Contrastive learning objectives on curated text pairs produce effective general-purpose embeddings
    The paper states it integrates and optimizes the entire suite of training methods for C-TEM.

pith-pipeline@v0.9.0 · 5490 in / 1242 out tokens · 55681 ms · 2026-05-13T13:22:02.053526+00:00 · methodology

discussion (0)

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