Answering User Questions About Machine Learning Models Through Standardized Model Cards
Bibliographic record
Abstract
Reusing pre-trained machine learning models is becoming very popular due to model hubs such as Hugging Face (HF). However, similar to when reusing software, many issues may arise when reusing an ML model. In many cases, users resort to asking questions on discussion forums such as the HF community forum. In this paper, we study how we can reduce the community's workload in answering these questions and increase the likelihood that questions receive a quick answer. We analyze 11,278 discussions from the HF model community that contain user questions about ML models. We focus on the effort spent handling questions, the high-level topics of discussions, and the potential for standardizing responses in model cards based on a model card template. Our findings indicate that there is not much effort involved in responding to user questions, however, 40.1% of the questions remain open without any response. A topic analysis shows that discussions are more centered around technical details on model development and troubleshooting, indicating that more input from model providers is required. We show that 42.5% of the questions could have been answered if the model provider followed a standard model card template for the model card. Based on our analysis, we recommend that model providers add more development-related details on the model's architecture, algorithm, data preprocessing and training code in existing documentation (sub)sections and add new (sub)sections to the template to address common questions about model usage and hardware requirements.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".