Skip to content
Merged
Changes from 1 commit
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
52 changes: 51 additions & 1 deletion README.md
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,57 @@ The Visual Document Retrieval Benchmarks (ViDoRe v1 and v2), is introduced to ev

![ViDoRe Examples](assets/vidore_examples.webp)

## Usage
## ⚠️ Deprecation Warning: Moving from `vidore-benchmark` to `mteb`

The `vidore-benchmark` package is deprecated. Since `mteb` now supports image-text retrieval, we recommend using `mteb` instead to evaluate your retriever on the ViDoRe benchmark.

### New Evaluation Process

Follow the instructions to setup `mteb` [here](https://github.com/embeddings-benchmark/mteb/tree/main?tab=readme-ov-file#installation). Then you have 2 options.

#### Option 1: CLI

```bash
mteb run -b "Vidore(v1)" -m "vidore/colqwen2.5-v0.2"
mteb run -b "Vidore(v2)" -m "vidore/colqwen2.5-v0.2"
```

#### Option 2: Python Script

```python
import mteb
from mteb.model_meta import ModelMeta
from mteb.models.colqwen_models import ColQwen2_5Wrapper

# === Configuration ===
MODEL_NAME = "johndoe/mycolqwen2.5"
BENCHMARKS = ["ViDoRe(v1)", "ViDoRe(v2)"]

# === Model Metadata =ニ=
custom_model_meta = ModelMeta(
loader=ColQwen2_5Wrapper,
name=MODEL_NAME,
modalities=["image", "text"],
framework="Colpali",
similarity_fn_name="max_sim",
# Optional metadata (fill in if available else None)
...
)

# === Load Model ===
custom_model = custom_model_meta.load_model(MODEL_NAME)

# === Load Tasks ===
tasks = mteb.get_benchmarks(names=BENCHMARKS)
evaluator = mteb.MTEB(tasks=tasks)

# === Run Evaluation ===
results = evaluator.run(custom_model)
```

For custom models, you should implement your own wrapper. Check the [ColPaliEngineWrapper](https://github.com/embeddings-benchmark/mteb/blob/main/mteb/models/colpali_models.py) for an example.

## [Deprecated] Usage

This packages comes with a Python API and a CLI to evaluate your own retriever on the ViDoRe benchmark. Both are compatible with `Python>=3.9`.

Expand Down