README.md

---
language:
- en
license: mit
library_name: Tevatron
tags:
- vidore
datasets:
- Tevatron/docmatix-ir
- HuggingFaceM4/Docmatix
- Tevatron/msmarco-passage-aug
---

# DSE-Phi3-Docmatix-V1

DSE-Phi3-Docmatix-V1 is a bi-encoder model designed to encode document screenshots into dense vectors for document retrieval. The Document Screenshot Embedding ([DSE](https://arxiv.org/abs/2406.11251)) approach captures documents in their original visual format, preserving all information such as text, images, and layout, thus avoiding tedious parsing and potential information loss.

The model, `Tevatron/dse-phi3-docmatix-v1`, is trained using 1/10 of the `Tevatron/docmatix-ir` dataset, a variant of `HuggingFaceM4/Docmatix` specifically adapted for training PDF retrievers with Vision Language Models in open-domain question answering scenarios. For more information on dataset filtering and hard negative mining, refer to the [docmatix-ir](https://huggingface.co./datasets/Tevatron/docmatix-ir/blob/main/README.md) dataset page.

DSE has strong zero-shot effectiveness for document retrieval both with visual input and text input.
For example, DSE-Phi3-Docmatix-V1 achieves 74.1 nDCG@5 on [ViDoRE](https://huggingface.co./spaces/vidore/vidore-leaderboard) leaderboard in **zero-shot setting** (without finetuning with ViDoRe training data).

## How to train the model from scratch

Please see https://github.com/texttron/tevatron/tree/main/examples/dse

## How to Use the Model

### Load the Model and Processor

```python
import torch
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained('Tevatron/dse-phi3-docmatix-v1', trust_remote_code=True, attn_implementation="flash_attention_2", torch_dtype=torch.bfloat16, use_cache=False).to('cuda:0')

def get_embedding(last_hidden_state: torch.Tensor, attention_mask: torch.Tensor) -> torch.Tensor:
    sequence_lengths = attention_mask.sum(dim=1) - 1
    bs = last_hidden_state.shape[0]
    reps = last_hidden_state[torch.arange(bs, device=last_hidden_state.device), sequence_lengths]
    reps = torch.nn.functional.normalize(reps, p=2, dim=-1)
    return reps
```

### Encode Text Query

```python
queries = ["query: Where can we see Llama?</s>", "query: What is LLaMA model?</s>"]
query_inputs = processor(queries, return_tensors="pt", padding="longest", max_length=128, truncation=True).to('cuda:0')
with torch.no_grad():
    output = model(**query_inputs, return_dict=True, output_hidden_states=True)
query_embeddings = get_embedding(output.hidden_states[-1], query_inputs["attention_mask"])
```

### Encode Document Screenshot

```python
from PIL import Image
import requests
from io import BytesIO

# URLs of the images
url1 = "https://huggingface.co./Tevatron/dse-phi3-docmatix-v1/resolve/main/animal-llama.png"
url2 = "https://huggingface.co./Tevatron/dse-phi3-docmatix-v1/resolve/main/meta-llama.png"

# Download and open images
response1 = requests.get(url1)
response2 = requests.get(url2)

passage_image1 = Image.open(BytesIO(response1.content))
passage_image2 = Image.open(BytesIO(response2.content))

passage_images = [passage_image1, passage_image2]
passage_prompts = ["<|image_1|>\nWhat is shown in this image?</s>", "<|image_2|>\nWhat is shown in this image?</s>"]

# Process inputs and get embeddings
passage_inputs = processor(passage_prompts, images=passage_images, return_tensors="pt", padding="longest", max_length=4096, truncation=True).to('cuda:0')
passage_inputs['input_ids'] = passage_inputs['input_ids'].squeeze(0)
passage_inputs['attention_mask'] = passage_inputs['attention_mask'].squeeze(0)
passage_inputs['image_sizes'] = passage_inputs['image_sizes'].squeeze(0)
with torch.no_grad():
    output = model(**passage_inputs, return_dict=True, output_hidden_states=True)
doc_embeddings = get_embedding(output.hidden_states[-1], passage_inputs["attention_mask"])

```

### Compute Similarity

```python
from torch.nn.functional import cosine_similarity
num_queries = query_embeddings.size(0)
num_passages = doc_embeddings.size(0)

for i in range(num_queries):
    query_embedding = query_embeddings[i].unsqueeze(0)
    similarities = cosine_similarity(query_embedding, doc_embeddings)
    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
```

### Encode Document Text
This DSE checkpoint is warm-up with `Tevatron/msmarco-passage-aug`, thus the model can also effectively encode document as text input.
```python
passage_prompts = [
  "The llama (/ˈlɑːmə/; Spanish pronunciation: [ˈʎama] or [ˈʝama]) (Lama glama) is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.</s>",
  "Llama (acronym for Large Language Model Meta AI, and formerly stylized as LLaMA) is a family of autoregressive large language models (LLMs) released by Meta AI starting in February 2023.[2][3] The latest version is Llama 3.1, released in July 2024.[4]</s>"
]

passage_inputs = processor(passage_prompts, images=None, return_tensors="pt", padding="longest", max_length=4096, truncation=True).to('cuda:0')
with torch.no_grad():
    output = model(**passage_inputs, return_dict=True, output_hidden_states=True)
doc_embeddings = get_embedding(output.hidden_states[-1], passage_inputs["attention_mask"])

for i in range(num_queries):
    query_embedding = query_embeddings[i].unsqueeze(0)
    similarities = cosine_similarity(query_embedding, doc_embeddings)
    print(f"Similarities for Query {i+1}: {similarities.cpu().float().numpy()}")
```

### Citation
If you find this checkpoint is helpful, please consider cite Phi3, Docmatix and our DSE work.