Model reference · open weights
uform3-image-text-multilingual is an open-weight embedding model from unum-cloud. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | unum-cloud |
|---|---|
| Type | Embedding models |
| Task | Embeddings |
| Runs with | transformers |
| Released | 2024-04-20 |
| Popularity | 6k downloads / month |
| Licence | Open weights |
About
Pocket-Sized Multimodal AI For Content Understanding and Generation In Python, JavaScript, and Swift
The uform3-image-text-multilingual-base UForm model is a tiny vision and multilingual language encoder, covering 21 languages, mapping them into a shared vector space.
This model produces up to 256-dimensional embeddings and is made of:
Unlike most CLIP-like multomodal models, this model shares 4 layers between the text and visual encoder to allow for more data- and parameter-efficient training. Also unlike most models, UForm provides checkpoints compatible with PyTorch, ONNX, and CoreML, covering the absolute majority of AI-capable devices, with pre-quantized weights and inference code. If you need a larger, more accurate, or multilingual model, check our HuggingFace Hub. For more details on running the model, check out the UForm GitHub repository.
For all evaluations, the multimodal part was used unless otherwise stated.
| Dataset | Recall@1 | Recall@5 | Recall@10 |
|---|---|---|---|
| Zero-Shot Flickr | 0.558 | 0.813 | 0.874 |
| MS-COCO ¹ | 0.401 | 0.680 | 0.781 |
¹ It's important to note, that the MS-COCO train split was present in the training data.
Recall@10 on the XTD-10 dataset:
| English | German | Spanish | French | Italian | Russian | Japanese | Korean | Turkish | Chinese | Polish |
|---|---|---|---|---|---|---|---|---|---|---|
| 96.1 | 93.5 | 95.7 | 94.1 | 94.4 | 90.4 | 90.2 | 91.3 | 95.2 | 93.8 | 95.8 |
Recall@1, Recall@5, and Recall@10 on the COCO-SM dataset:
| Target Language | OpenCLIP @ 1 | UForm @ 1 | OpenCLIP @ 5 | UForm @ 5 | OpenCLIP @ 10 | UForm @ 10 | Speakers |
|---|---|---|---|---|---|---|---|
| Arabic | 22.7 | 31.7 | 44.9 | 57.8 | 55.8 | 69.2 | 274 M |
| Armenian | 5.6 | 22.0 | 14.3 | 44.7 | 20.2 | 56.0 | 4 M |
| Chinese | 27.3 | 32.2 | 51.3 | 59.0 | 62.1 | 70.5 | 1'118 M |
| English | 37.8 | 37.7 | 63.5 | 65.0 | 73.5 | 75.9 | 1'452 M |
| French | 31.3 | 35.4 | 56.5 | 62.6 | 67.4 | 73.3 | 274 M |
| German | 31.7 | 35.1 | 56.9 | 62.2 | 67.4 | 73.3 | 134 M |
| Hebrew | 23.7 | 26.7 | 46.3 | 51.8 | 57.0 | 63.5 | 9 M |
| Hindi | 20.7 | 31.3 | 42.5 | 57.9 | 53.7 | 69.6 | 602 M |
| Indonesian | 26.9 | 30.7 | 51.4 | 57.0 | 62.7 | 68.6 | 199 M |
| Italian | 31.3 | 34.9 | 56.7 | 62.1 | 67.1 | 73.1 | 67 M |
| Japanese | 27.4 | 32.6 | 51.5 | 59.2 | 62.6 | 70.6 | 125 M |
| Korean | 24.4 | 31.5 | 48.1 | 57.8 | 59.2 | 69.2 | 81 M |
| Persian | 24.0 | 28.8 | 47.0 | 54.6 | 57.8 | 66.2 | 77 M |
| Polish | 29.2 | 33.6 | 53.9 | 60.1 | 64.7 | 71.3 | 41 M |
| Portuguese | 31.6 | 32.7 | 57.1 | 59.6 | 67.9 | 71.0 | 257 M |
| Russian | 29.9 | 33.9 | 54.8 | 60.9 | 65.8 | 72.0 | 258 M |
| Spanish | 32.6 | 35.6 | 58.0 | 62.8 | 68.8 | 73.7 | 548 M |
| Thai | 21.5 | 28.7 | 43.0 | 54.6 | 53.7 | 66.0 | 61 M |
| Turkish | 25.5 | 33.0 | 49.1 | 59.6 | 60.3 | 70.8 | 88 M |
| Ukranian | 26.0 | 30.6 | 49.9 | 56.7 | 60.9 | 68.1 | 41 M |
| Vietnamese | 25.4 | 28.3 | 49.2 | 53.9 | 60.3 | 65.5 | 85 M |
| Mean | 26.5±6.4 | 31.8±3.5 | 49.8±9.8 | 58.1±4.5 | 60.4±10.6 | 69.4±4.3 | - |
| Google Translate | 27.4±6.3 | 31.5±3.5 | 51.1±9.5 | 57.8±4.4 | 61.7±10.3 | 69.1±4.3 | - |
| Microsoft Translator | 27.2±6.4 | 31.4±3.6 | 50.8±9.8 | 57.7±4.7 | 61.4±10.6 | 68.9±4.6 | - |
| Meta NLLB | 24.9±6.7 | 32.4±3.5 | 47.5±10.3 | 58.9±4.5 | 58.2±11.2 | 70.2±4.3 | - |
For a deeper comparison of output ranking check the following table for the Normalized Discounted Cumulative Gains for the first 20 results - NDCG@20:
| | Arabic | Armenian | Chinese | French | German | Hebrew | Hindi | Indonesian | Italian | Japanese |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys uform3-image-text-multilingual for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (uform3-image-text-multilingual below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/embeddings \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"uform3-image-text-multilingual","input":"text to embed"}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.