nesoai/multivector-colqwen2-v1.0-hf-docqa-energy

colqwen2-v1.0-hf finetuned on energy document pages

This is a Multi-Vector Encoder model finetuned from vidore/colqwen2-v1.0-hf on the synthetic_doc_qa_energy_train dataset using the sentence-transformers 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 <!-- 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
• —Language: en
• —License: apache-2.0

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Documentation: Multi-Vector Encoder Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Multi-Vector Encoders on Hugging Face

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:

pip install -U sentence-transformers

Then you can load this model and run inference.

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]])

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Downstream Usage (Sentence Transformers)

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Evaluation

Metrics

Multi Vector Information Retrieval

• —Dataset: energy-dev
• —Evaluated with <code>MultiVectorInformationRetrievalEvaluator</code>

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Training Details

Training Dataset

syntheticdocqaenergytrain

• —Dataset: synthetic_doc_qa_energy_train at 438dd85
• —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/exampleimage0.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/exampleimage1.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/exampleimage2.jpg" width="200"> |
• —Loss: <code>MultiVectorMultipleNegativesRankingLoss</code> with these parameters:

  {
      "score_metric": "colbert_scores",
      "scale": 1.0,
      "score_mini_batch_size": null,
      "size_average": true,
      "gather_across_devices": false
  }

Evaluation Dataset

syntheticdocqaenergytrain

• —Dataset: synthetic_doc_qa_energy_train at 438dd85
• —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/image0.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/image1.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> with these parameters:

  {
      "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: adamwtorchfused
• —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: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': 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

• —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

@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

@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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