--- language: - en license: apache-2.0 tags: - sentence-transformers - multi-vector - colbert - late-interaction - generated_from_trainer - dataset_size:3475 - loss:MultiVectorMultipleNegativesRankingLoss base_model: vidore/colqwen2-v1.0-hf widget: - text: What is the aim of this book according to the introduction? - text: What is the purpose of a wet-bulb thermometer in a sling psychrometer? - text: What are the different switching states for DCC and FCC topologies of a converter? - text: What is the topic discussed in this page? - text: What do these graphs show? datasets: - vidore/syntheticDocQA_energy_train 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: colqwen2-v1.0-hf finetuned on energy document pages results: - task: type: multi-vector-information-retrieval name: Multi Vector Information Retrieval dataset: name: energy dev type: energy-dev metrics: - type: maxsim_accuracy@1 value: 0.935 name: Maxsim Accuracy@1 - type: maxsim_accuracy@3 value: 0.9675 name: Maxsim Accuracy@3 - type: maxsim_accuracy@5 value: 0.9725 name: Maxsim Accuracy@5 - type: maxsim_accuracy@10 value: 0.9825 name: Maxsim Accuracy@10 - type: maxsim_precision@1 value: 0.935 name: Maxsim Precision@1 - type: maxsim_precision@3 value: 0.3225 name: Maxsim Precision@3 - type: maxsim_precision@5 value: 0.1945 name: Maxsim Precision@5 - type: maxsim_precision@10 value: 0.09824999999999999 name: Maxsim Precision@10 - type: maxsim_recall@1 value: 0.935 name: Maxsim Recall@1 - type: maxsim_recall@3 value: 0.9675 name: Maxsim Recall@3 - type: maxsim_recall@5 value: 0.9725 name: Maxsim Recall@5 - type: maxsim_recall@10 value: 0.9825 name: Maxsim Recall@10 - type: maxsim_ndcg@10 value: 0.9592186005800499 name: Maxsim Ndcg@10 - type: maxsim_mrr@10 value: 0.9517777777777776 name: Maxsim Mrr@10 - type: maxsim_map@100 value: 0.952218176489611 name: Maxsim Map@100 --- # colqwen2-v1.0-hf finetuned on energy document pages This is a [Multi-Vector Encoder](https://www.sbert.net/docs/multi_vector_encoder/usage/usage.html) model finetuned from [vidore/colqwen2-v1.0-hf](https://huggingface.co/vidore/colqwen2-v1.0-hf) on the [synthetic_doc_qa_energy_train](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train) 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:** [vidore/colqwen2-v1.0-hf](https://huggingface.co/vidore/colqwen2-v1.0-hf) - **Maximum Sequence Length:** 32768 tokens - **Output Dimensionality:** 128 dimensions - **Similarity Function:** maxsim - **Supported Modalities:** Text, Image - **Training Dataset:** - [synthetic_doc_qa_energy_train](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train) - **Language:** en - **License:** apache-2.0 ### 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': 'retrieval', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'embeddings'}, 'image': {'method': 'forward', 'method_output_name': 'embeddings'}}, 'module_output_name': 'token_embeddings', 'architecture': 'ColQwen2ForRetrieval'}) (1): MultiVectorMask({'skiplist_words': [], 'keep_only_token_ids': None}) ) ``` ## 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/multivector-colqwen2-v1.0-hf-docqa-energy") # Run inference: each input becomes a sequence of per-token vectors (variable length). queries = [ 'What topics are covered in this index?', ] documents = [ 'https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/image_0.jpg', 'https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/image_1.jpg', 'https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/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) # (20, 128) (759, 128) # Get the MaxSim similarity scores similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[15.7523, 8.2611, 11.6049]]) ``` ## Evaluation ### Metrics #### Multi Vector Information Retrieval * Dataset: `energy-dev` * Evaluated with [MultiVectorInformationRetrievalEvaluator](https://sbert.net/docs/package_reference/multi_vector_encoder/evaluation.html#sentence_transformers.multi_vector_encoder.evaluation.MultiVectorInformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | maxsim_accuracy@1 | 0.935 | | maxsim_accuracy@3 | 0.9675 | | maxsim_accuracy@5 | 0.9725 | | maxsim_accuracy@10 | 0.9825 | | maxsim_precision@1 | 0.935 | | maxsim_precision@3 | 0.3225 | | maxsim_precision@5 | 0.1945 | | maxsim_precision@10 | 0.0982 | | maxsim_recall@1 | 0.935 | | maxsim_recall@3 | 0.9675 | | maxsim_recall@5 | 0.9725 | | maxsim_recall@10 | 0.9825 | | **maxsim_ndcg@10** | **0.9592** | | maxsim_mrr@10 | 0.9518 | | maxsim_map@100 | 0.9522 | ## Training Details ### Training Dataset #### synthetic_doc_qa_energy_train * Dataset: [synthetic_doc_qa_energy_train](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train) at [438dd85](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train/tree/438dd859839b0a48eba43c0e9f853195f2e61384) * Size: 3,475 training samples * Columns: query and image * Approximate statistics based on the first 100 samples: | | query | image | |:---------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------| | type | string | image | | modality | text | image | | details | | | * Samples: | query | image | |:--------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------| | What is the objective of the research task related to reactor pressure vessel steels? | | | What recommendations does this study make regarding energy policy options? | | | What are the typical materials used for the cathode, electrolyte, and anode in conventional solid-state batteries? | | * Loss: [MultiVectorMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/multi_vector_encoder/losses.html#multivectormultiplenegativesrankingloss) with these parameters: ```json { "score_metric": "colbert_scores", "scale": 1.0, "score_mini_batch_size": null, "size_average": true, "gather_across_devices": false } ``` ### Evaluation Dataset #### synthetic_doc_qa_energy_train * Dataset: [synthetic_doc_qa_energy_train](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train) at [438dd85](https://huggingface.co/datasets/vidore/syntheticDocQA_energy_train/tree/438dd859839b0a48eba43c0e9f853195f2e61384) * Size: 400 evaluation samples * Columns: query and image * Approximate statistics based on the first 100 samples: | | query | image | |:---------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------| | type | string | image | | modality | text | image | | details | | | * Samples: | query | image | |:--------------------------------------------------------------------------------------|:-------------------------------------------| | What topics are covered in this index? | | | What are the different funding sources for projects listed in the table? | | | What are the main sections covered in this report? | | * Loss: [MultiVectorMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/multi_vector_encoder/losses.html#multivectormultiplenegativesrankingloss) with these parameters: ```json { "score_metric": "colbert_scores", "scale": 1.0, "score_mini_batch_size": null, "size_average": true, "gather_across_devices": false } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `num_train_epochs`: 1 - `learning_rate`: 2e-05 - `warmup_steps`: 0.05 - `bf16`: True - `save_only_model`: True - `load_best_model_at_end`: True #### All Hyperparameters
Click to expand - `per_device_train_batch_size`: 8 - `num_train_epochs`: 1 - `max_steps`: -1 - `learning_rate`: 2e-05 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: None - `warmup_steps`: 0.05 - `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`: 1.0 - `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`: True - `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`: True - `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`: {} - `max_length`: None
### Training Logs | Epoch | Step | Training Loss | Validation Loss | energy-dev_maxsim_ndcg@10 | |:-------:|:-------:|:-------------:|:---------------:|:-------------------------:| | -1 | -1 | - | - | 0.9571 | | 0.0115 | 5 | 0.0964 | - | - | | 0.0230 | 10 | 0.0489 | - | - | | 0.0345 | 15 | 0.1147 | - | - | | 0.0460 | 20 | 0.0682 | - | - | | 0.0575 | 25 | 0.0311 | - | - | | 0.0690 | 30 | 0.0563 | - | - | | 0.0805 | 35 | 0.0086 | - | - | | 0.0920 | 40 | 0.0599 | - | - | | 0.1011 | 44 | - | 0.0606 | 0.9554 | | 0.1034 | 45 | 0.0014 | - | - | | 0.1149 | 50 | 0.0163 | - | - | | 0.1264 | 55 | 0.0684 | - | - | | 0.1379 | 60 | 0.0364 | - | - | | 0.1494 | 65 | 0.0973 | - | - | | 0.1609 | 70 | 0.0744 | - | - | | 0.1724 | 75 | 0.0444 | - | - | | 0.1839 | 80 | 0.0047 | - | - | | 0.1954 | 85 | 0.1064 | - | - | | 0.2023 | 88 | - | 0.0516 | 0.9548 | | 0.2069 | 90 | 0.1071 | - | - | | 0.2184 | 95 | 0.0783 | - | - | | 0.2299 | 100 | 0.0627 | - | - | | 0.2414 | 105 | 0.0181 | - | - | | 0.2529 | 110 | 0.0073 | - | - | | 0.2644 | 115 | 0.0430 | - | - | | 0.2759 | 120 | 0.0013 | - | - | | 0.2874 | 125 | 0.0500 | - | - | | 0.2989 | 130 | 0.0044 | - | - | | 0.3034 | 132 | - | 0.0442 | 0.9548 | | 0.3103 | 135 | 0.0891 | - | - | | 0.3218 | 140 | 0.0260 | - | - | | 0.3333 | 145 | 0.0302 | - | - | | 0.3448 | 150 | 0.0229 | - | - | | 0.3563 | 155 | 0.1208 | - | - | | 0.3678 | 160 | 0.0367 | - | - | | 0.3793 | 165 | 0.0361 | - | - | | 0.3908 | 170 | 0.0409 | - | - | | 0.4023 | 175 | 0.0103 | - | - | | 0.4046 | 176 | - | 0.0427 | 0.9559 | | 0.4138 | 180 | 0.0072 | - | - | | 0.4253 | 185 | 0.0649 | - | - | | 0.4368 | 190 | 0.0405 | - | - | | 0.4483 | 195 | 0.0026 | - | - | | 0.4598 | 200 | 0.0352 | - | - | | 0.4713 | 205 | 0.0342 | - | - | | 0.4828 | 210 | 0.0098 | - | - | | 0.4943 | 215 | 0.0057 | - | - | | 0.5057 | 220 | 0.0235 | 0.0416 | 0.9567 | | 0.5172 | 225 | 0.0026 | - | - | | 0.5287 | 230 | 0.0119 | - | - | | 0.5402 | 235 | 0.0013 | - | - | | 0.5517 | 240 | 0.0417 | - | - | | 0.5632 | 245 | 0.0118 | - | - | | 0.5747 | 250 | 0.0060 | - | - | | 0.5862 | 255 | 0.0069 | - | - | | 0.5977 | 260 | 0.0620 | - | - | | 0.6069 | 264 | - | 0.0410 | 0.9583 | | 0.6092 | 265 | 0.0700 | - | - | | 0.6207 | 270 | 0.0287 | - | - | | 0.6322 | 275 | 0.1266 | - | - | | 0.6437 | 280 | 0.0015 | - | - | | 0.6552 | 285 | 0.0147 | - | - | | 0.6667 | 290 | 0.0145 | - | - | | 0.6782 | 295 | 0.0976 | - | - | | 0.6897 | 300 | 0.0027 | - | - | | 0.7011 | 305 | 0.0341 | - | - | | 0.7080 | 308 | - | 0.0404 | 0.9583 | | 0.7126 | 310 | 0.0570 | - | - | | 0.7241 | 315 | 0.0302 | - | - | | 0.7356 | 320 | 0.0047 | - | - | | 0.7471 | 325 | 0.0238 | - | - | | 0.7586 | 330 | 0.0514 | - | - | | 0.7701 | 335 | 0.0022 | - | - | | 0.7816 | 340 | 0.0579 | - | - | | 0.7931 | 345 | 0.0030 | - | - | | 0.8046 | 350 | 0.0407 | - | - | | 0.8092 | 352 | - | 0.0404 | 0.9577 | | 0.8161 | 355 | 0.0363 | - | - | | 0.8276 | 360 | 0.0570 | - | - | | 0.8391 | 365 | 0.0031 | - | - | | 0.8506 | 370 | 0.0603 | - | - | | 0.8621 | 375 | 0.0067 | - | - | | 0.8736 | 380 | 0.0022 | - | - | | 0.8851 | 385 | 0.0129 | - | - | | 0.8966 | 390 | 0.0072 | - | - | | 0.9080 | 395 | 0.0052 | - | - | | 0.9103 | 396 | - | 0.0405 | 0.9574 | | 0.9195 | 400 | 0.0165 | - | - | | 0.9310 | 405 | 0.0060 | - | - | | 0.9425 | 410 | 0.0020 | - | - | | 0.9540 | 415 | 0.0144 | - | - | | 0.9655 | 420 | 0.0572 | - | - | | 0.9770 | 425 | 0.1479 | - | - | | 0.9885 | 430 | 0.0381 | - | - | | **1.0** | **435** | **0.0337** | **0.0405** | **0.9592** | | -1 | -1 | - | - | 0.9592 | * The bold row denotes the saved checkpoint. ### Training Time - **Training**: 14.2 minutes - **Evaluation**: 24.0 minutes - **Total**: 38.2 minutes ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.7.0.dev0 - Transformers: 5.14.1 - PyTorch: 2.11.0+cu128 - Accelerate: 1.5.2 - Datasets: 3.5.0 - Tokenizers: 0.22.2 ## 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", } ``` #### MultiVectorMultipleNegativesRankingLoss ```bibtex @misc{henderson2017efficient, title={Efficient Natural Language Response Suggestion for Smart Reply}, author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, year={2017}, eprint={1705.00652}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```