Improving Pre-Trained Model Naming Practices in Hugging Face to Enhance Reusability and Trustworthiness

Core Concepts

Pre-trained model (PTM) naming practices in the Hugging Face ecosystem diverge from traditional software package naming, posing challenges for model reuse and trustworthiness. This study delineates the current PTM naming practices, identifies discrepancies between user preferences and practical implementation, and introduces an automated tool to detect naming anomalies.

Abstract

The study focuses on understanding the naming practices of pre-trained models (PTMs) in the Hugging Face ecosystem and developing an automated tool to detect naming anomalies.
Key highlights:

PTM naming is different from traditional software package naming. PTM names contain more semantic information about the model's architecture, training, and capabilities, compared to traditional package names.
Survey participants prefer naming PTMs based on architectural characteristics and intended functions, rather than training details. However, practical PTM naming practices do not always align with user preferences.
PTM users currently identify naming anomalies by manually inspecting model metadata and architecture. To automate this process, the researchers developed DARA (DNN ARchitecture Assessment), a machine learning-based tool that can effectively detect naming anomalies based on architectural information alone.
DARA achieves 92.18% accuracy in detecting anomalies in model_type and 67.47% accuracy in detecting anomalies in architecture. The results suggest that architectural information is a promising signal for identifying naming issues in PTM packages.

The study provides insights to guide future research on improving automated tools for PTM naming analysis, enhancing model reusability, and strengthening the trustworthiness of the PTM ecosystem.

Stats

"Pre-trained models are generally trained for a single objective...Therefore, [PTM] naming is critical for readability and avoiding mistakes."
"Almost all models include Architecture (A) information, though only ∼60% of respondents prefer it. Many respondents value information such as task (T), version (V), and parameter count (P); these elements are comparatively rare in real model names."

Quotes

"Pre-trained model names often include more specific information about the model's architecture, training data, and performance. This...detail is not typically included in traditional...package names."
"[Traditional] packages are not usually as directly built on previous ones, with small extensions added like for PTM. Finetuning models generally means that the outputs are similar to the base, unlike with traditional packages."
"Visualization or manual inspection of PTMs makes it 'obvious if the architecture is different to what the model name says'."

Key Insights Distilled From

Naming Practices of Pre-Trained Models in Hugging Face

by Wenxin Jiang... at arxiv.org 04-01-2024

https://arxiv.org/pdf/2310.01642.pdf

Deeper Inquiries

How can the insights from this study be leveraged to develop more comprehensive and user-friendly PTM naming conventions?

The insights from this study can be instrumental in enhancing PTM naming conventions by incorporating a more structured and standardized approach. One key aspect is to align user preferences with actual naming practices. The study revealed discrepancies between what users prefer in PTM names and what is commonly found in practice. By bridging this gap, PTM naming conventions can be made more user-friendly and intuitive. Additionally, incorporating elements that users find valuable, such as architectural lineage, model size, task, and versioning, can lead to more informative and descriptive PTM names. This can facilitate easier identification, selection, and reuse of PTMs by engineers and researchers. Moreover, establishing guidelines or best practices based on the findings of the study can help authors create more consistent and meaningful PTM names, ultimately improving the overall discoverability and usability of PTMs in the ecosystem.

How can the automated detection of naming anomalies be integrated into PTM registries and development workflows to enhance the trustworthiness of the PTM ecosystem?

Integrating automated detection of naming anomalies into PTM registries and development workflows can significantly enhance the trustworthiness of the PTM ecosystem. One approach is to incorporate the anomaly detection tool as a part of the PTM submission process in registries. Authors submitting new PTMs would run the tool to ensure that their naming conventions align with the established standards and do not contain anomalies. This proactive measure can help maintain consistency and accuracy in PTM names from the outset.
Furthermore, integrating the anomaly detection tool into the registry's search functionality can provide users with an additional layer of information. When users search for PTMs, the tool can flag any naming anomalies or inconsistencies, alerting users to potential issues. This transparency can build trust among users and promote confidence in the PTM ecosystem.
In development workflows, incorporating the automated detection tool can streamline the review process for PTM packages. Developers can use the tool to validate the naming conventions of their models before deployment, ensuring that the names accurately reflect the model's architecture and specifications. This can prevent naming errors and enhance the overall quality and reliability of PTMs in the ecosystem.

What other signals, beyond architectural information, could be used to improve the detection of naming anomalies in PTM packages?

In addition to architectural information, several other signals could be leveraged to enhance the detection of naming anomalies in PTM packages:

Training Regime: Considering the training methodology used for the PTM, such as fine-tuning, transfer learning, or self-supervised learning, can provide insights into the model's capabilities and origins. Anomalies in the training regime compared to the model's name could indicate naming discrepancies.

Dataset Characteristics: Incorporating details about the dataset used to train the PTM, such as data sources, preprocessing techniques, and domain-specific information, can help identify naming anomalies. Mismatches between the dataset characteristics and the model's name could signal inconsistencies.

Model Performance Metrics: Analyzing the performance metrics of the PTM, such as accuracy, precision, and recall, can serve as additional signals for detecting naming anomalies. Significant discrepancies between the model's performance and the implied capabilities in the name may indicate naming inconsistencies.

Model Versioning: Considering the version history of the PTM and any updates or modifications made to the model over time can provide valuable insights. Anomalies in versioning details compared to the model's name could highlight naming discrepancies that need attention.

By incorporating these additional signals into the anomaly detection process, the accuracy and effectiveness of identifying naming anomalies in PTM packages can be further improved, leading to a more reliable and trustworthy PTM ecosystem.