| --- |
| 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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