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tags:
- sentence-transformers
- multi-vector
- colbert
- late-interaction
- generated_from_trainer
- dataset_size:10000
- loss:CachedMultiVectorMultipleNegativesRankingLoss
base_model: Qwen/Qwen3-VL-Embedding-2B
widget:
- text: What are the traditional practices of indigenous peoples in managing their
lands and territories?
- text: How does the tracking and commission distribution work in affiliate marketing
when a user makes a purchase?
- text: How many climate adaptation activities by Indigenous peoples are identified?
- text: What is the founding novel of the cyberpunk subgenre?
- text: Where can observable equity security prices be found, as per ASC 321-10?
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- maxsim_accuracy@1
- maxsim_accuracy@3
- maxsim_accuracy@5
- maxsim_accuracy@10
- maxsim_precision@1
- maxsim_precision@3
- maxsim_precision@5
- maxsim_precision@10
- maxsim_recall@1
- maxsim_recall@3
- maxsim_recall@5
- maxsim_recall@10
- maxsim_ndcg@10
- maxsim_mrr@10
- maxsim_map@100
model-index:
- name: Multi-Vector Encoder
results:
- task:
type: multi-vector-information-retrieval
name: Multi Vector Information Retrieval
dataset:
name: vdr eval hard
type: vdr-eval-hard
metrics:
- type: maxsim_accuracy@1
value: 0.8466666666666667
name: Maxsim Accuracy@1
- type: maxsim_accuracy@3
value: 0.9566666666666667
name: Maxsim Accuracy@3
- type: maxsim_accuracy@5
value: 0.98
name: Maxsim Accuracy@5
- type: maxsim_accuracy@10
value: 0.9966666666666667
name: Maxsim Accuracy@10
- type: maxsim_precision@1
value: 0.8466666666666667
name: Maxsim Precision@1
- type: maxsim_precision@3
value: 0.31888888888888883
name: Maxsim Precision@3
- type: maxsim_precision@5
value: 0.19599999999999998
name: Maxsim Precision@5
- type: maxsim_precision@10
value: 0.09966666666666665
name: Maxsim Precision@10
- type: maxsim_recall@1
value: 0.8466666666666667
name: Maxsim Recall@1
- type: maxsim_recall@3
value: 0.9566666666666667
name: Maxsim Recall@3
- type: maxsim_recall@5
value: 0.98
name: Maxsim Recall@5
- type: maxsim_recall@10
value: 0.9966666666666667
name: Maxsim Recall@10
- type: maxsim_ndcg@10
value: 0.9277675716215076
name: Maxsim Ndcg@10
- type: maxsim_mrr@10
value: 0.9049907407407407
name: Maxsim Mrr@10
- type: maxsim_map@100
value: 0.905293771043771
name: Maxsim Map@100
---
# Multi-Vector Encoder
This is a [Multi-Vector Encoder](https://www.sbert.net/docs/multi_vector_encoder/usage/usage.html) model finetuned from [Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) on the llamaindex-vdr-en-train-preprocessed dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps inputs to sequences of 128-dimensional token-level vectors and scores them with late interaction (MaxSim), useful for semantic search with late interaction.
## Model Details
### Model Description
- **Model Type:** Multi-Vector Encoder
- **Base model:** [Qwen/Qwen3-VL-Embedding-2B](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) <!-- at revision 9f2f7e710d6d81056aa5c0a4f04764fec6bb7bda -->
- **Maximum Sequence Length:** 262144 tokens
- **Output Dimensionality:** 128 dimensions
- **Similarity Function:** maxsim
- **Supported Modalities:** Text, Image, Video, Message
- **Training Dataset:**
- llamaindex-vdr-en-train-preprocessed
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Multi-Vector Encoder Documentation](https://www.sbert.net/docs/multi_vector_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Multi-Vector Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=multi-vector)
### Full Model Architecture
```
MultiVectorEncoder(
(0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'})
(1): Dense({'in_features': 2048, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity', 'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
(2): MultiVectorMask({'skiplist_words': [], 'keep_only_token_ids': None})
(3): Normalize({'module_input_name': 'token_embeddings', 'module_output_name': 'token_embeddings'})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import MultiVectorEncoder
# Download from the 🤗 Hub
model = MultiVectorEncoder("tomaarsen/ColQwen3-VL-Embedding-2B-vdr")
# Run inference: each input becomes a sequence of per-token vectors (variable length).
queries = [
'What are the new anthropological perspectives on development as discussed by Quarles Van Ufford and Giri in 2003?',
]
documents = [
'https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_0.jpg',
'https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_1.jpg',
'https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_2.jpg',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings[0].shape, document_embeddings[0].shape)
# (48, 128) (352, 128)
# Get the MaxSim similarity scores
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[37.0712, 33.5348, 23.2705]])
```
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## Evaluation
### Metrics
#### Multi Vector Information Retrieval
* Dataset: `vdr-eval-hard`
* Evaluated with [<code>MultiVectorInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/multi_vector_encoder/evaluation.html#sentence_transformers.multi_vector_encoder.evaluation.MultiVectorInformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| maxsim_accuracy@1 | 0.8467 |
| maxsim_accuracy@3 | 0.9567 |
| maxsim_accuracy@5 | 0.98 |
| maxsim_accuracy@10 | 0.9967 |
| maxsim_precision@1 | 0.8467 |
| maxsim_precision@3 | 0.3189 |
| maxsim_precision@5 | 0.196 |
| maxsim_precision@10 | 0.0997 |
| maxsim_recall@1 | 0.8467 |
| maxsim_recall@3 | 0.9567 |
| maxsim_recall@5 | 0.98 |
| maxsim_recall@10 | 0.9967 |
| **maxsim_ndcg@10** | **0.9278** |
| maxsim_mrr@10 | 0.905 |
| maxsim_map@100 | 0.9053 |
<!--
## Bias, Risks and Limitations
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### Recommendations
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## Training Details
### Training Dataset
#### llamaindex-vdr-en-train-preprocessed
* Dataset: llamaindex-vdr-en-train-preprocessed
* Size: 10,000 training samples
* Columns: <code>query</code>, <code>image</code>, and <code>negative_0</code>
* Approximate statistics based on the first 100 samples:
| | query | image | negative_0 |
|:---------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| type | string | image | image |
| modality | text | image | image |
| details | <ul><li>min: 27 tokens</li><li>mean: 35.59 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 1000x756 px</li><li>mean: 1437x1628 px</li><li>max: 2044x1869 px</li></ul> | <ul><li>min: 1008x756 px</li><li>mean: 1450x1637 px</li><li>max: 2044x1851 px</li></ul> |
* Samples:
| query | image | negative_0 |
|:-----------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------|:---------------------------------------------------|
| <code>What are the new anthropological perspectives on development as discussed by Quarles Van Ufford and Giri in 2003?</code> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_0.jpg" width="200"> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/example_image_0.jpg" width="200"> |
| <code>What are the three main positions anthropologists have taken in relation to development, as discussed by David Lewis?</code> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_1.jpg" width="200"> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/example_image_0.jpg" width="200"> |
| <code>Who are the three sisters known as the Fates in Greek mythology?</code> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/image_2.jpg" width="200"> | <img src="https://huggingface.co/tomaarsen/ColQwen3-VL-Embedding-2B-vdr/resolve/main/assets/example_image_1.jpg" width="200"> |
* Loss: [<code>CachedMultiVectorMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/multi_vector_encoder/losses.html#cachedmultivectormultiplenegativesrankingloss) with these parameters:
```json
{
"score_metric": "colbert_scores",
"mini_batch_size": 1,
"mini_batch_num_tokens": null,
"score_mini_batch_size": 1,
"scale": 1.0,
"size_average": true,
"gather_across_devices": false
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `num_train_epochs`: 1
- `learning_rate`: 2e-05
- `warmup_steps`: 0.1
- `max_grad_norm`: 30.0
- `bf16`: True
- `learning_rate_mapping`: {'^(model\\.)?1\\.linear': 0.0002}
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `per_device_train_batch_size`: 64
- `num_train_epochs`: 1
- `max_steps`: -1
- `learning_rate`: 2e-05
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: None
- `warmup_steps`: 0.1
- `optim`: adamw_torch_fused
- `optim_args`: None
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `optim_target_modules`: None
- `gradient_accumulation_steps`: 1
- `average_tokens_across_devices`: True
- `max_grad_norm`: 30.0
- `label_smoothing_factor`: 0.0
- `bf16`: True
- `fp16`: False
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `use_liger_kernel`: False
- `liger_kernel_config`: None
- `use_cache`: False
- `neftune_noise_alpha`: None
- `torch_empty_cache_steps`: None
- `auto_find_batch_size`: False
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `include_num_input_tokens_seen`: no
- `log_level`: passive
- `log_level_replica`: warning
- `disable_tqdm`: False
- `project`: huggingface
- `trackio_space_id`: None
- `trackio_bucket_id`: None
- `trackio_static_space_id`: None
- `per_device_eval_batch_size`: 8
- `prediction_loss_only`: True
- `eval_on_start`: False
- `eval_do_concat_batches`: True
- `eval_use_gather_object`: False
- `eval_accumulation_steps`: None
- `include_for_metrics`: []
- `batch_eval_metrics`: False
- `save_only_model`: False
- `save_on_each_node`: False
- `enable_jit_checkpoint`: False
- `push_to_hub`: False
- `hub_private_repo`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_always_push`: False
- `hub_revision`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `restore_callback_states_from_checkpoint`: False
- `full_determinism`: False
- `seed`: 42
- `data_seed`: None
- `use_cpu`: False
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `parallelism_config`: None
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `dataloader_prefetch_factor`: None
- `remove_unused_columns`: True
- `label_names`: None
- `train_sampling_strategy`: random
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `ddp_static_graph`: None
- `ddp_backend`: None
- `ddp_timeout`: 1800
- `fsdp`: None
- `fsdp_config`: None
- `deepspeed`: None
- `debug`: []
- `skip_memory_metrics`: True
- `do_predict`: False
- `resume_from_checkpoint`: None
- `warmup_ratio`: None
- `local_rank`: -1
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {'^(model\\.)?1\\.linear': 0.0002}
- `max_length`: None
</details>
### Training Logs
| Epoch | Step | Training Loss | vdr-eval-hard_maxsim_ndcg@10 |
|:------:|:----:|:-------------:|:----------------------------:|
| -1 | -1 | - | 0.8244 |
| 0.0510 | 8 | 2.2737 | - |
| 0.1019 | 16 | 0.6985 | 0.9132 |
| 0.1529 | 24 | 0.3765 | - |
| 0.2038 | 32 | 0.3179 | 0.9133 |
| 0.2548 | 40 | 0.2909 | - |
| 0.3057 | 48 | 0.2984 | 0.9184 |
| 0.3567 | 56 | 0.2649 | - |
| 0.4076 | 64 | 0.2771 | 0.9232 |
| 0.4586 | 72 | 0.2404 | - |
| 0.5096 | 80 | 0.2456 | 0.9271 |
| 0.5605 | 88 | 0.2933 | - |
| 0.6115 | 96 | 0.2549 | 0.9224 |
| 0.6624 | 104 | 0.2636 | - |
| 0.7134 | 112 | 0.2815 | 0.9202 |
| 0.7643 | 120 | 0.2829 | - |
| 0.8153 | 128 | 0.2592 | 0.9206 |
| 0.8662 | 136 | 0.2180 | - |
| 0.9172 | 144 | 0.2647 | 0.9206 |
| 0.9682 | 152 | 0.2411 | - |
| 1.0 | 157 | - | 0.9278 |
### Training Time
- **Training**: 2.4 hours
- **Evaluation**: 26.8 minutes
- **Total**: 2.8 hours
### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.7.0.dev0
- Transformers: 5.14.1
- PyTorch: 2.13.0+cu130
- Accelerate: 1.14.0
- Datasets: 5.0.0
- Tokenizers: 0.22.2
## Additional Resources
- [Sentence Transformers Documentation](https://www.sbert.net): the full documentation site, including training, evaluation, and pre-trained model catalogs.
- [PyLate](https://github.com/lightonai/pylate): the upstream library whose features were absorbed into Sentence Transformers for multi-vector / late-interaction models.
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CachedMultiVectorMultipleNegativesRankingLoss
```bibtex
@misc{gao2021scaling,
title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
year={2021},
eprint={2101.06983},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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