C-Pack: Comprehensive Resources to Advance General Chinese Text Embeddings

Core Concepts

C-Pack provides a comprehensive package of critical resources, including a large-scale training dataset (C-MTP), a comprehensive benchmark (C-MTEB), and state-of-the-art pre-trained models (BGE), to significantly advance the field of general Chinese text embeddings.

Abstract

C-Pack is a comprehensive package of resources that aims to advance the field of general Chinese text embeddings. It includes the following key components:

C-MTEB (Chinese Massive Text Embedding Benchmark):

A benchmark for evaluating Chinese text embeddings, covering 6 major tasks and 35 diverse datasets.
Standardized evaluation protocols and pipelines to enable fair comparisons across different embedding models.

C-MTP (Chinese Massive Text Pairs):

A large-scale dataset of 100 million text pairs, curated from a variety of web corpora and high-quality labeled datasets.
The dataset is designed to be diverse and representative, supporting the training of general-purpose Chinese text embeddings.

BGE (BAAI General Embeddings):

A family of pre-trained Chinese text embedding models in three different sizes (small, base, and large).
The models are trained using a comprehensive recipe, including pre-training, contrastive learning, and task-specific fine-tuning.
The BGE models outperform previous state-of-the-art Chinese text embeddings by a significant margin on the C-MTEB benchmark.

Training Recipe:

The complete training pipeline used to develop the BGE models is released, including pre-training, contrastive learning, and task-specific fine-tuning.
This allows the community to reproduce the state-of-the-art methods and build upon them for further improvements.

The release of C-Pack has been widely recognized and adopted by the research community. The BGE models have received over 20 million downloads, and C-MTEB has become the most popular and authoritative benchmark for evaluating Chinese text embeddings, with over 100 submissions to date. C-Pack provides a comprehensive solution for the development, evaluation, and application of general-purpose Chinese text embeddings, establishing a solid foundation for advancing this field.

Stats

C-MTP (unlabeled) contains 100 million text pairs curated from web corpora and other sources.
C-MTP (labeled) contains 838,465 text pairs from high-quality labeled datasets.
The BGE (large) model has 326 million parameters.

Quotes

"C-Pack provides a comprehensive package of critical resources, including a large-scale training dataset (C-MTP), a comprehensive benchmark (C-MTEB), and state-of-the-art pre-trained models (BGE), to significantly advance the field of general Chinese text embeddings."
"The release of C-Pack has been widely recognized and adopted by the research community. The BGE models have received over 20 million downloads, and C-MTEB has become the most popular and authoritative benchmark for evaluating Chinese text embeddings, with over 100 submissions to date."

Key Insights Distilled From

C-Pack: Packaged Resources To Advance General Chinese Embedding

by Shitao Xiao,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2309.07597.pdf

Deeper Inquiries

How can the training data and models from C-Pack be further extended or adapted to specific application domains or tasks

The training data and models from C-Pack can be further extended or adapted to specific application domains or tasks by following these strategies:

Domain-specific fine-tuning: The pre-trained models from C-Pack can be fine-tuned on domain-specific data to tailor them for specific applications. By fine-tuning on data relevant to a particular domain, the models can learn domain-specific nuances and improve performance in that specific domain.

Task-specific training: The models can be further trained on task-specific datasets to enhance their performance on specific tasks. By training the models on task-specific data, they can learn to perform well on particular tasks, such as sentiment analysis, question answering, or document classification.

Transfer learning: The models can be used as a starting point for transfer learning to adapt them to new tasks or domains. By leveraging the knowledge learned during pre-training, the models can be quickly adapted to new tasks with less data and computational resources.

Ensemble methods: Combining multiple models from C-Pack can improve performance through ensemble methods. By aggregating predictions from multiple models, the ensemble can capture diverse perspectives and improve overall performance.

Continual learning: Continual learning techniques can be applied to continuously update the models with new data and adapt them to changing environments. This approach ensures that the models stay relevant and effective over time.

What are the potential limitations or biases in the C-MTP dataset, and how can they be addressed to improve the generalizability of the text embeddings

The potential limitations or biases in the C-MTP dataset include:

Data source bias: The data collected from specific sources may introduce bias towards certain topics or writing styles, affecting the generalizability of the text embeddings. To address this, a more diverse set of sources should be included to ensure a balanced representation.

Label noise: The labeled data in C-MTP may contain noise or inaccuracies, impacting the quality of the supervision signals for training. Implementing rigorous data cleaning and validation processes can help mitigate this issue.

Task-specific bias: The labeled data in C-MTP may be biased towards specific tasks, leading to models that excel in those tasks but perform poorly on others. To improve generalizability, a broader range of tasks should be included in the dataset.

Language bias: The dataset may not capture the full diversity of the Chinese language, leading to biases in the learned embeddings. Including data from diverse dialects, regions, and language styles can help address this limitation.

To improve the generalizability of the text embeddings, it is essential to address these limitations by enhancing data diversity, ensuring data quality, and mitigating biases through careful curation and validation processes.

Given the rapid progress in large language models, how can the text embedding capabilities be integrated with or complemented by these models to achieve even more powerful and versatile language understanding

To integrate text embedding capabilities with large language models for more powerful and versatile language understanding, the following approaches can be considered:

Hybrid models: Combining text embeddings with large language models in a hybrid architecture can leverage the strengths of both approaches. The text embeddings can provide contextual information and semantic understanding, while the large language models can offer broader language knowledge and generation capabilities.

Knowledge distillation: Using text embeddings to distill knowledge from large language models can help transfer their capabilities to smaller, more efficient models. This process can extract key information from the large models and incorporate it into text embeddings for improved performance.

Fine-tuning: Fine-tuning text embeddings on specific tasks using knowledge from large language models can enhance their performance on those tasks. By leveraging the pre-trained knowledge of large models, text embeddings can adapt to new tasks more effectively.

Multi-task learning: Training text embeddings on multiple tasks simultaneously, including tasks handled by large language models, can improve their versatility and performance across a range of applications. By learning from diverse tasks, the embeddings can develop a broader understanding of language.

By integrating text embedding capabilities with large language models through these approaches, it is possible to create more powerful and versatile language understanding systems that excel in various tasks and domains.