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---
license: apache-2.0
language:
- en
- de
- es
- fr
- it
- ja
- ko
- pl
- ru
- tr
- zh
---
---
<h1 align="center">UForm</h1>
<h3 align="center">
Multi-Modal Inference Library<br/>
For Semantic Search Applications<br/>
</h3>
---
UForm is a Multi-Modal Modal Inference package, designed to encode Multi-Lingual Texts, Images, and, soon, Audio, Video, and Documents, into a shared vector space!
It extends the `transfromers` package to support Mid-fusion Models.
This is model card of the __Multilingual model__ with:
* 12 layers BERT (8 layers for unimodal encoding and rest layers for multimodal encoding)
* ViT-B/16 (image resolution is 224x224)
The model was trained on balanced multilingual dataset.
If you need English model, check [this](https://huggingface.co/unum-cloud/uform-vl-english).
## Evaluation
The following metrics were obtained with multimodal re-ranking:
**Monolingual**
| Dataset | Recall@1 | Recall@5 | Recall@10 |
| :-------- | ------: | --------: | --------: |
| Zero-Shot Flickr | 0.558 | 0.813 | 0.874 |
| MS-COCO (train split was in training data) | 0.401 | 0.680 | 0.781 |
**Multilingual ([XTD-10](https://github.com/adobe-research/Cross-lingual-Test-Dataset-XTD10))**
Metric is recall@10
| English | German | Spanish | French | Italian | Russian | Japanese | Korean | Turkish | Chinese | Polish |
| -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------: | -------:
96.3 | 92.6 | 94.5 | 94.4 | 94.4 | 90.4 | 88.3 | 92.5 | 94.4 | 93.6 | 95.0 |
## Installation
```bash
pip install uform
```
## Usage
To load the model:
```python
import uform
model = uform.get_model('unum-cloud/uform-vl-english')
```
To encode data:
```python
from PIL import Image
text = 'a small red panda in a zoo'
image = Image.open('red_panda.jpg')
image_data = model.preprocess_image(image)
text_data = model.preprocess_text(text)
image_embedding = model.encode_image(image_data)
text_embedding = model.encode_text(text_data)
joint_embedding = model.encode_multimodal(image=image_data, text=text_data)
```
To get features:
```python
image_features, image_embedding = model.encode_image(image_data, return_features=True)
text_features, text_embedding = model.encode_text(text_data, return_features=True)
```
These features can later be used to produce joint multimodal encodings faster, as the first layers of the transformer can be skipped:
```python
joint_embedding = model.encode_multimodal(
image_features=image_features,
text_features=text_features,
attention_mask=text_data['attention_mask']
)
```
There are two options to calculate semantic compatibility between an image and a text: [Cosine Similarity](#cosine-similarity) and [Matching Score](#matching-score).
### Cosine Similarity
```python
import torch.nn.functional as F
similarity = F.cosine_similarity(image_embedding, text_embedding)
```
The `similarity` will belong to the `[-1, 1]` range, `1` meaning the absolute match.
__Pros__:
- Computationally cheap.
- Only unimodal embeddings are required, unimodal encoding is faster than joint encoding.
- Suitable for retrieval in large collections.
__Cons__:
- Takes into account only coarse-grained features.
### Matching Score
Unlike cosine similarity, unimodal embedding are not enough.
Joint embedding will be needed and the resulting `score` will belong to the `[0, 1]` range, `1` meaning the absolute match.
```python
score = model.get_matching_scores(joint_embedding)
```
__Pros__:
- Joint embedding captures fine-grained features.
- Suitable for re-ranking – sorting retrieval result.
__Cons__:
- Resource-intensive.
- Not suitable for retrieval in large collections.
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