File size: 3,615 Bytes
81e773d 3f213a9 81e773d 28a4a53 81e773d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
---
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**
| 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.
|