README.md

---
library_name: transformers
language:
- vi
- en
base_model:
- 5CD-AI/Vintern-1B-v2
datasets:
- vidore/colpali_train_set
- 5CD-AI/Viet-Doc-VQA
- 5CD-AI/Viet-OCR-VQA
- 5CD-AI/Viet-Doc-VQA-II
tags:
- colpali
---
<div align="center">
  <img src="colvintern.png" width="400"/>
</div>

## ColVintern-1B-v1 🇻🇳 ❄️ - Colpali version for Vietnamese.

**What's new in ColVintern-1B-v1!**
- We coded and successfully trained the **Colpali pipeline** for **Vintern-1B-v2**. The model supports RAG by extracting embedding vectors for questions and images containing related information.
- This is the first experimental version, trained on the [**Colpali dataset**](https://huggingface.co/datasets/vidore/colpali_train_set) for English and **5%** of the image-based question-answer pairs we have for Vietnamese.
- The model achieves results nearly equivalent to Colpali version 1, with strong support for Vietnamese texts and only 1 billion parameters compared to current 2B-3B Colpali models.

## Colpali Benchmarks

We tested on the [**ViDoRe benchmark**](https://huggingface.co/collections/vidore/vidore-benchmark-667173f98e70a1c0fa4db00d) from the Colpali paper. The **TabF** and **Shift** test datasets were not used because they are in French. We plan to expand to multiple languages in the near future.

|                              | ArxivQ | DocQ | InfoQ | TATQ | AI   | Energy | Gov.  | Health. | Avg.   |
|:------------------------------:|:--------:|:------:|:-------:|:------:|:------:|:--------:|:-------:|:---------:|:--------:|
| **Unstructured** Text only        |        |       |       |   |     |      |    |    |        |
| - BM25                        | -      | 34.1 | -     | 44.0 | 90.4 | 78.3   | 78.8  | 82.6    | -      |
| - BGE-M3                      | -      | 28.4 | - | 36.1 | 88.4 | 76.8 | 77.7 | 84.6 | -      |
| **Unstructured** + OCR          |        |      |       |      |      |        |       |         |     |
| - BM25                        | 31.6   | 36.8 | 62.9  | 62.7 | 92.8 | 85.9   | 83.9  | 87.2    | 68.0   |
| - BGE-M3                      | 31.4   | 25.7 | 60.1 | 50.5 | 90.2 | 83.6 | 84.9 | 91.1 |  64.7   |   
| **Unstructured** + Captioning     |        |      |       |      |      |        |       |         |  |
| - BM25                        | 40.1   | 38.4 | 70.0  | 61.5 | 88.0 | 84.7   | 82.7  | 89.2    |   69.3    |
| - BGE-M3                      | 35.7   | 32.9 | 71.9 | 43.8 | 88.8 | 83.3 | 80.4 | 91.3 |   66.0    |
| **Contrastive VLMs**                   |        |      |       |      |      |        |       |         |        |
| - Jina-CLIP                   | 25.4   | 11.9 | 35.5  | 3.3 | 15.2 | 19.7   | 21.4  | 20.8    | 19.2   |
| - Nomic-vision                | 17.1   | 10.7 | 30.1  | 2.7 | 12.9 | 10.9   | 11.4  | 15.7    | 13.9   |
| - SigLIP (Vanilla)            | 43.2   | 30.3 | 64.1  | 26.2 | 62.5 | 65.7   | 66.1  | 79.1    | 54.7   |
| **Colpali**                               |        |      |       |      |      |        |       |         |        |
| - SigLIP (Vanilla)            | 43.2   | 30.3 | 64.1  | 26.2 | 62.5 | 65.7   | 66.1  | 79.1    | 54.7   |
| - BiSigLIP (+fine-tuning)     | 58.5   | 32.9 | 70.5  | 30.5 | 74.3 | 73.7 | 74.2 | 82.3 | 62.1 |
| - BiPali (+LLM)               | 56.5   | 30.0 | 67.4  | 33.4 | 71.2 | 61.9 | 73.8 | 73.6 | 58.5 |
| - ColPali (+Late Inter.)      | **79.1**   | **54.4** | 81.8  | **65.8** | **96.2** | **91.0** | **92.7** | 94.4 | **81.3** |
| **Ours**                               |        |      |       |      |      |        |       |         |        |
| - ColVintern-1B (+Late Inter.)  | 71.6   | 48.3 | **84.6**  | 59.6 | 92.9 | 88.7 | 89.4 | **95.2** | 78.8 |

We are expanding the training dataset for upcoming versions, including adding hard negative mining techniques, increasing GPU VRAM, etc., to achieve better results.

## Examples

Input Images:
<div style="display: flex; gap: 20px;">
  <img src="ex1.jpg" width="300"/>
  <img src="ex2.jpg" width="300"/>
</div>

Input Queries:
```
queries = ["Cảng Hải Phòng thông báo gì ?","Phí giao hàng bao nhiêu ?"]
```

Output Scores:
| Query                                | Image 1 Score | Image 2 Score |
|--------------------------------------|---------------|---------------|
| Chuyện gì xảy ra với quốc lộ 5 TP Hải Phòng ? | 62.4333       | 59.9523       |
| Phí giao hàng bao nhiêu ?           | 60.7748       | 62.8654       |



## Quickstart: 

Colab: https://colab.research.google.com/drive/1-y8HLiyS0oCj7Vpy4i7FsJ1A6kU7ROca?usp=sharing

```python
import torch
from PIL import Image
from transformers import AutoModel, AutoTokenizer, AutoProcessor
import matplotlib.pyplot as plt

model_name = "5CD-AI/ColVintern-1B-v1"

processor =  AutoProcessor.from_pretrained(
    model_name,
    trust_remote_code=True
)
model = AutoModel.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    low_cpu_mem_usage=True,
    trust_remote_code=True,
).eval().cuda()

#!wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex1.jpg
#!wget https://huggingface.co/5CD-AI/ColVintern-1B-v1/resolve/main/ex2.jpg

images = [Image.open("ex1.jpg"),Image.open("ex2.jpg")]
batch_images = processor.process_images(images)

queries = [
    "Cảng Hải Phòng thông báo gì ?",
    "Phí giao hàng bao nhiêu ?",
]

batch_queries = processor.process_queries(queries) 

batch_images["pixel_values"] =  batch_images["pixel_values"].cuda().bfloat16()
batch_images["input_ids"] = batch_images["input_ids"].cuda() 
batch_images["attention_mask"] = batch_images["attention_mask"].cuda().bfloat16()
batch_queries["input_ids"] = batch_queries["input_ids"].cuda() 
batch_queries["attention_mask"] = batch_queries["attention_mask"].cuda().bfloat16()

with torch.no_grad():
    image_embeddings = model(**batch_images)
    query_embeddings = model(**batch_queries)

scores = processor.score_multi_vector(query_embeddings, image_embeddings)

max_scores, max_indices = torch.max(scores, dim=1)
# In ra kết quả cho mỗi câu hỏi
for i, query in enumerate(queries):
    print(f"Câu hỏi: '{query}'")
    print(f"Điểm số: {max_scores[i].item()}\n")
    plt.figure(figsize=(5,5))
    plt.imshow(images[max_indices[i]])
    plt.show()
```

## Citation 

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