# Multimodal AutoML on Multimodal Benchmark

Source: [Multimodal AutoML on Multimodal Benchmark](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-26T05:59:00.503Z

Evaluation of Multimodal AutoML systems across a suite of specific prediction tasks, reporting performance metrics for individual components (e.g., prod, cloth, imdb) and overall averages.

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

| Scope | Recorded value |
| --- | --- |
| Task | Multimodal AutoML |
| Dataset (including any stated split) | Multimodal Benchmark |
| Metric | Prod Score |
| Unit | PERCENT |
| Direction | Higher is better |
| Results loaded | 19 of 19 |
| Coverage | All results currently returned for this benchmark |

Evaluation settings may differ between results. The original papers and recorded configurations are needed to establish comparability. A shared metric name alone does not establish an identical protocol.

## Metrics

- [Prod Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Prod%20Score>): Higher is better; unit: PERCENT.

- [QAQ Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=QAQ%20Score>): Higher is better; unit: PERCENT.

- [QAA Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=QAA%20Score>): Higher is better; unit: PERCENT.

- [Cloth Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Cloth%20Score>): Higher is better; unit: PERCENT.

- [Airbnb Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Airbnb%20Score>): Higher is better; unit: PERCENT.

- [AE Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=AE%20Score>): Higher is better; unit: PERCENT.

- [Mercari Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Mercari%20Score>): Higher is better; unit: PERCENT.

- [Jigsaw Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Jigsaw%20Score>): Higher is better; unit: PERCENT.

- [IMDb Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=IMDb%20Score>): Higher is better; unit: PERCENT.

- [Fake News Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Fake%20News%20Score>): Higher is better; unit: PERCENT.

- [Kick Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Kick%20Score>): Higher is better; unit: PERCENT.

- [JC Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=JC%20Score>): Higher is better; unit: PERCENT.

- [Wine Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Wine%20Score>): Higher is better; unit: PERCENT.

- [Pop Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Pop%20Score>): Higher is better; unit: PERCENT.

- [Channel Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Channel%20Score>): Higher is better; unit: PERCENT.

- [Salary Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Salary%20Score>): Higher is better; unit: PERCENT.

- [Book Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Book%20Score>): Higher is better; unit: PERCENT.

- [House Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=House%20Score>): Higher is better; unit: PERCENT.

- [Average Score](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=Average%20Score>): Higher is better; unit: PERCENT.

- [MRR](<https://www.sota2.com/research/sota/multimodal-automl-on-multimodal-benchmark.md?metric=MRR>): Higher is better; unit: SCALAR.

## Recorded results

| Method | Prod Score | Result date | Report paper |
| --- | --- | --- | --- |
| ELECTRA + Exponential Decay | 91.3 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Text-Embedding | 90.9 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| ELECTRA | 90.7 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| ELECTRA + Exponential Decay + Average 3 | 90.7 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Pre-Embedding | 90.7 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Stack-Ensemble | 90.7 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| ROBERTa | 89.8 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Multimodal-Embedding | 89.5 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| AG-Stack + N-Gram | 89.5 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| AG-Weighted + N-Gram | 89.2 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| AG-Weighted | 89.1 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| AG-Stack | 89.1 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| H2O AutoML | 86.9 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Weighted-Ensemble | 86.7 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| H2O AutoML + Word2Vec | 85.9 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| H2O AutoML + Pre-Embedding | 84.6 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Fuse-Late | 72.9 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| Fuse-Early | 72.8 | Not recorded | 68fa6b2a1e209131cea2c7bf |
| All-Text | 70.5 | Not recorded | 68fa6b2a1e209131cea2c7bf |

### ELECTRA + Exponential Decay

Prod Score: 91.3. Result ID: 698e6462ffc3901de09d35fe.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa673f1e209131cea15373.md>)

[Implementation](<https://github.com/google-research/electra>)

Recorded evaluation configuration: {"NLP Backbone":"ELECTRA","Fine-tuning Tricks":"Exponential Decay r = 0.8"}. This record does not assert independent reproduction.

### Text-Embedding

Prod Score: 90.9. Result ID: 698e6462ffc3901de09d3604.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"Aggregation Strategy":"Text-Embedding"}. This record does not assert independent reproduction.

### ELECTRA

Prod Score: 90.7. Result ID: 698e6462ffc3901de09d35fd.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa673f1e209131cea15373.md>)

[Implementation](<https://github.com/google-research/electra>)

Recorded evaluation configuration: {"NLP Backbone":"ELECTRA"}. This record does not assert independent reproduction.

### ELECTRA + Exponential Decay + Average 3

Prod Score: 90.7. Result ID: 698e6462ffc3901de09d35ff.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa673f1e209131cea15373.md>)

[Implementation](<https://github.com/google-research/electra>)

Recorded evaluation configuration: {"NLP Backbone":"ELECTRA","Fine-tuning Tricks":"Exponential Decay r = 0.8, Average 3"}. This record does not assert independent reproduction.

### Pre-Embedding

Prod Score: 90.7. Result ID: 698e6462ffc3901de09d3603.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68e11e209131cea1eeb1.md>)

Recorded evaluation configuration: {"Aggregation Strategy":"Pre-Embedding"}. This record does not assert independent reproduction.

### Stack-Ensemble

Prod Score: 90.7. Result ID: 698e6462ffc3901de09d3607.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Implementation](<https://github.com/sxjscience/automl_multimodal_benchmark>)

Recorded evaluation configuration: {"Aggregation Strategy":"Stack-Ensemble"}. This record does not assert independent reproduction.

### ROBERTa

Prod Score: 89.8. Result ID: 698e6462ffc3901de09d35fc.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa66131e209131cea0e18b.md>)

[Implementation](<https://github.com/pytorch/fairseq>)

Recorded evaluation configuration: {"NLP Backbone":"ROBERTa"}. This record does not assert independent reproduction.

### Multimodal-Embedding

Prod Score: 89.5. Result ID: 698e6462ffc3901de09d3605.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"Aggregation Strategy":"Multimodal-Embedding"}. This record does not assert independent reproduction.

### AG-Stack + N-Gram

Prod Score: 89.5. Result ID: 698e6462ffc3901de09d360b.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67331e209131cea14b2a.md>)

[Implementation](<https://www.sota2.com/github.com/awslabs/autogluon>)

Recorded evaluation configuration: {"AutoML Framework":"AutoGluon","Aggregation":"Stack","Feature Engineering":"N-Gram"}. This record does not assert independent reproduction.

### AG-Weighted + N-Gram

Prod Score: 89.2. Result ID: 698e6462ffc3901de09d360a.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67331e209131cea14b2a.md>)

[Implementation](<https://www.sota2.com/github.com/awslabs/autogluon>)

Recorded evaluation configuration: {"AutoML Framework":"AutoGluon","Aggregation":"Weighted","Feature Engineering":"N-Gram"}. This record does not assert independent reproduction.

### AG-Weighted

Prod Score: 89.1. Result ID: 698e6462ffc3901de09d3608.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67331e209131cea14b2a.md>)

[Implementation](<https://www.sota2.com/github.com/awslabs/autogluon>)

Recorded evaluation configuration: {"AutoML Framework":"AutoGluon","Aggregation":"Weighted"}. This record does not assert independent reproduction.

### AG-Stack

Prod Score: 89.1. Result ID: 698e6462ffc3901de09d3609.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa67331e209131cea14b2a.md>)

[Implementation](<https://www.sota2.com/github.com/awslabs/autogluon>)

Recorded evaluation configuration: {"AutoML Framework":"AutoGluon","Aggregation":"Stack"}. This record does not assert independent reproduction.

### H2O AutoML

Prod Score: 86.9. Result ID: 698e6462ffc3901de09d360c.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"AutoML Framework":"H2O AutoML"}. This record does not assert independent reproduction.

### Weighted-Ensemble

Prod Score: 86.7. Result ID: 698e6462ffc3901de09d3606.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"Aggregation Strategy":"Weighted-Ensemble"}. This record does not assert independent reproduction.

### H2O AutoML + Word2Vec

Prod Score: 85.9. Result ID: 698e6462ffc3901de09d360d.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"AutoML Framework":"H2O AutoML","Feature Engineering":"Word2Vec"}. This record does not assert independent reproduction.

### H2O AutoML + Pre-Embedding

Prod Score: 84.6. Result ID: 698e6462ffc3901de09d360e.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

Recorded evaluation configuration: {"AutoML Framework":"H2O AutoML","Feature Engineering":"Pre-Embedding"}. This record does not assert independent reproduction.

### Fuse-Late

Prod Score: 72.9. Result ID: 698e6462ffc3901de09d3602.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Implementation](<https://github.com/sxjscience/automl_multimodal_benchmark>)

Recorded evaluation configuration: {"Fusion Strategy":"Fuse-Late","Note":"Identified as Multimodal-Net"}. This record does not assert independent reproduction.

### Fuse-Early

Prod Score: 72.8. Result ID: 698e6462ffc3901de09d3601.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa68f41e209131cea1f426.md>)

Recorded evaluation configuration: {"Fusion Strategy":"Fuse-Early"}. This record does not assert independent reproduction.

### All-Text

Prod Score: 70.5. Result ID: 698e6462ffc3901de09d3600.

[Reporting paper](<https://www.sota2.com/research/paper/68fa6b2a1e209131cea2c7bf.md>)

[Method origin paper](<https://www.sota2.com/research/paper/68fa709e1e209131cea4c470.md>)

[Implementation](<https://github.com/google-research/text-to-text-transfer-transformer>)

Recorded evaluation configuration: {"Fusion Strategy":"All-Text"}. This record does not assert independent reproduction.

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