| --- |
| 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]]) |
| ``` |
| <!-- |
| ### Direct Usage (Transformers) |
|
|
| <details><summary>Click to see the direct usage in Transformers</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Downstream Usage (Sentence Transformers) |
|
|
| You can finetune this model on your own dataset. |
|
|
| <details><summary>Click to expand</summary> |
|
|
| </details> |
| --> |
|
|
| <!-- |
| ### Out-of-Scope Use |
|
|
| *List how the model may foreseeably be misused and address what users ought not to do with the model.* |
| --> |
|
|
| ## 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 |
| |
| *What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
| --> |
| |
| <!-- |
| ### Recommendations |
| |
| *What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
| --> |
| |
| ## 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} |
| } |
| ``` |
| |
| <!-- |
| ## Glossary |
| |
| *Clearly define terms in order to be accessible across audiences.* |
| --> |
| |
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| ## Model Card Authors |
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| *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.* |
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