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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) <!-- at revision 0d3e414967fde994dd99a0ccc29bcb34b5355712 -->
- **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]])
```
<!--
### 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: `energy-dev`
* 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 | 0.935 |
| 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 |
<!--
## 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
#### 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: <code>query</code> and <code>image</code>
* Approximate statistics based on the first 100 samples:
| | query | image |
|:---------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------|
| type | string | image |
| modality | text | image |
| details | <ul><li>min: 18 tokens</li><li>mean: 28.12 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 932x312 px</li><li>mean: 1717x2057 px</li><li>max: 3200x2339 px</li></ul> |
* Samples:
| query | image |
|:--------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------|
| <code>What is the objective of the research task related to reactor pressure vessel steels?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/example_image_0.jpg" width="200"> |
| <code>What recommendations does this study make regarding energy policy options?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/example_image_1.jpg" width="200"> |
| <code>What are the typical materials used for the cathode, electrolyte, and anode in conventional solid-state batteries?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/example_image_2.jpg" width="200"> |
* Loss: [<code>MultiVectorMultipleNegativesRankingLoss</code>](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: <code>query</code> and <code>image</code>
* Approximate statistics based on the first 100 samples:
| | query | image |
|:---------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------|
| type | string | image |
| modality | text | image |
| details | <ul><li>min: 18 tokens</li><li>mean: 27.27 tokens</li><li>max: 44 tokens</li></ul> | <ul><li>min: 827x1125 px</li><li>mean: 1728x2103 px</li><li>max: 3400x3042 px</li></ul> |
* Samples:
| query | image |
|:--------------------------------------------------------------------------------------|:-------------------------------------------|
| <code>What topics are covered in this index?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/image_0.jpg" width="200"> |
| <code>What are the different funding sources for projects listed in the table?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/image_1.jpg" width="200"> |
| <code>What are the main sections covered in this report?</code> | <img src="https://huggingface.co/tomaarsen/multivector-colqwen2-v1.0-hf-docqa-energy/resolve/main/assets/image_2.jpg" width="200"> |
* Loss: [<code>MultiVectorMultipleNegativesRankingLoss</code>](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
<details><summary>Click to expand</summary>
- `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
</details>
### 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}
}
```
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