| --- |
| tags: |
| - sentence-transformers |
| - sentence-similarity |
| - feature-extraction |
| - generated_from_trainer |
| - dataset_size:342061 |
| - loss:CachedMultipleNegativesRankingLoss |
| base_model: prestoai/qwen3-embedding-0.6b-arabic-ecom |
| widget: |
| - source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the |
| product that best matches it |
| |
| Query: نسونكس Spray' |
| sentences: |
| - Nasonex - Nasal Spray |
| - كابل شحن مايكرو XKIN - 2.4A |
| - حلوى الشوكولاتة - Choco Lapki |
| - source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the |
| product that best matches it |
| |
| Query: مكرونة رقم 42' |
| sentences: |
| - بنطلون رجالي - 0112 |
| - حقيبة حزام خصر - 4862 |
| - مكرونة الجيد معكوفة رقم 42 - 500 غ |
| - source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the |
| product that best matches it |
| |
| Query: سباغيتي' |
| sentences: |
| - ملعب كرة قدم - DD18 |
| - مكرونة معكوفة - Favelli |
| - مكرونة سباغيتي - Favelli |
| - source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the |
| product that best matches it |
| |
| Query: جبنة هواء' |
| sentences: |
| - جبنة - ابو الولد |
| - جبنة - Hawaa |
| - كاني طعام كلاب البالغين دجاج - 3 ك |
| - source_sentence: 'Instruct: Given an Arabic e-commerce search query, retrieve the |
| product that best matches it |
| |
| Query: شاحن تايب سي للسيارة' |
| sentences: |
| - شاحن سيارة قرين ليون مدخلين 36 وات مع كابل تايب سي - CBK |
| - صوص المكرونة هاينز - 365 غ |
| - بسكويت جولون بدون سكر شكلاتة ساندوتش |
| pipeline_tag: sentence-similarity |
| library_name: sentence-transformers |
| --- |
| |
| # SentenceTransformer based on prestoai/qwen3-embedding-0.6b-arabic-ecom |
|
|
| This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) on the pairs_with_negatives and positives datasets. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for retrieval. |
|
|
| ## Model Details |
|
|
| ### Model Description |
| - **Model Type:** Sentence Transformer |
| - **Base model:** [prestoai/qwen3-embedding-0.6b-arabic-ecom](https://huggingface.co/prestoai/qwen3-embedding-0.6b-arabic-ecom) <!-- at revision 80f273fd53c6644d65e14a2ac1fbf74b8c924097 --> |
| - **Maximum Sequence Length:** 128 tokens |
| - **Output Dimensionality:** 1024 dimensions |
| - **Similarity Function:** Cosine Similarity |
| - **Supported Modality:** Text |
| - **Training Datasets:** |
| - pairs_with_negatives |
| - positives |
| <!-- - **Language:** Unknown --> |
| <!-- - **License:** Unknown --> |
|
|
| ### Model Sources |
|
|
| - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
| - **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) |
| - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
|
|
| ### Full Model Architecture |
|
|
| ``` |
| SentenceTransformer( |
| (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'Qwen3Model'}) |
| (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'lasttoken', 'include_prompt': True}) |
| (2): Normalize({}) |
| ) |
| ``` |
|
|
| ## 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 SentenceTransformer |
| |
| # Download from the 🤗 Hub |
| model = SentenceTransformer("sentence_transformers_model_id") |
| # Run inference |
| queries = [ |
| 'Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it\nQuery: شاحن تايب سي للسيارة', |
| ] |
| documents = [ |
| 'شاحن سيارة قرين ليون مدخلين 36 وات مع كابل تايب سي - CBK', |
| 'بسكويت جولون بدون سكر شكلاتة ساندوتش', |
| 'صوص المكرونة هاينز - 365 غ', |
| ] |
| query_embeddings = model.encode_query(queries) |
| document_embeddings = model.encode_document(documents) |
| print(query_embeddings.shape, document_embeddings.shape) |
| # [1, 1024] [3, 1024] |
| |
| # Get the similarity scores for the embeddings |
| similarities = model.similarity(query_embeddings, document_embeddings) |
| print(similarities) |
| # tensor([[ 0.6773, -0.0849, -0.0875]]) |
| ``` |
| <!-- |
| ### 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.* |
| --> |
|
|
| <!-- |
| ## 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 Datasets |
|
|
| #### pairs_with_negatives |
|
|
| * Dataset: pairs_with_negatives |
| * Size: 124,261 training samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 23 tokens</li><li>mean: 29.44 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.95 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 14.87 tokens</li><li>max: 40 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:--------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------|:-------------------------------------------------------------------| |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: واقي للوجه دهني</code> | <code>Anthelios Oil Control (Dry Touch) - La Roche Posay</code> | <code>Anthelios Invisible Mist (Dry Touch) - La Roche Posay</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: تن منارة زليتن</code> | <code>تن منارة زليتن بزيت دوار الشمس - 160 غ</code> | <code>تن فاني بزيت دوار الشمس - 160 غ</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: فانتا زجاجة صغيرة</code> | <code>مشروب فانتا برتقال زجاجة - 330 مل</code> | <code>مشروب فانتا - 1 ل (برتقال)</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 8, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| #### positives |
|
|
| * Dataset: positives |
| * Size: 217,800 training samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 23 tokens</li><li>mean: 29.61 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 13.81 tokens</li><li>max: 41 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-----------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------| |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: نحب جبنة القرية</code> | <code>ميرسين جبنة القرية 200 جم</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كابل شحن مايكرو Moxom A2.4</code> | <code>كابل شحن مايكرو Moxom - A2.4</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: dry idea</code> | <code>Dry idea (powder fresh)</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 8, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| ### Evaluation Datasets |
|
|
| #### pairs_with_negatives |
|
|
| * Dataset: pairs_with_negatives |
| * Size: 1,256 evaluation samples |
| * Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | negative | |
| |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | string | |
| | details | <ul><li>min: 23 tokens</li><li>mean: 29.56 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 16.04 tokens</li><li>max: 43 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 15.05 tokens</li><li>max: 33 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | negative | |
| |:-------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:--------------------------------------| |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: هيبوتك بوزن</code> | <code>عطر Hypnotic Poison - PERFECTO COLLECTION</code> | <code>عطر Poison Girl - Dior</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: شاحن مايكرو 2.4A</code> | <code>شحن مايكرو Smila - 2.4A</code> | <code>شحن تايب سي Smila - 2.4A</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: تمر صعيدي</code> | <code>تمر صعيدي مشفوط</code> | <code>تمر قصيم مشفوط</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 8, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| #### positives |
|
|
| * Dataset: positives |
| * Size: 2,200 evaluation samples |
| * Columns: <code>anchor</code> and <code>positive</code> |
| * Approximate statistics based on the first 1000 samples: |
| | | anchor | positive | |
| |:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| |
| | type | string | string | |
| | details | <ul><li>min: 24 tokens</li><li>mean: 29.27 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 13.61 tokens</li><li>max: 44 tokens</li></ul> | |
| * Samples: |
| | anchor | positive | |
| |:-------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------| |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: حاملة أدوات القطط</code> | <code>حاملة أدوات القطة - AA04</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كريم شمس أطفال</code> | <code>واقي شمس كريمي - Chicco</code> | |
| | <code>Instruct: Given an Arabic e-commerce search query, retrieve the product that best matches it<br>Query: كانديسارتان هيدروكلوروثيازيد</code> | <code>Candesartan and Hydrochlorothiazide 16mg/12.5mg</code> | |
| * Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: |
| ```json |
| { |
| "scale": 20.0, |
| "similarity_fct": "cos_sim", |
| "mini_batch_size": 8, |
| "gather_across_devices": false, |
| "directions": [ |
| "query_to_doc" |
| ], |
| "partition_mode": "joint", |
| "hardness_mode": null, |
| "hardness_strength": 0.0 |
| } |
| ``` |
|
|
| ### Training Hyperparameters |
| #### Non-Default Hyperparameters |
|
|
| - `per_device_train_batch_size`: 32 |
| - `learning_rate`: 0.0001 |
| - `num_train_epochs`: 1 |
| - `warmup_steps`: 0.05 |
| - `fp16`: True |
|
|
| #### All Hyperparameters |
| <details><summary>Click to expand</summary> |
|
|
| - `do_predict`: False |
| - `prediction_loss_only`: True |
| - `per_device_train_batch_size`: 32 |
| - `per_device_eval_batch_size`: 8 |
| - `gradient_accumulation_steps`: 1 |
| - `eval_accumulation_steps`: None |
| - `torch_empty_cache_steps`: None |
| - `learning_rate`: 0.0001 |
| - `weight_decay`: 0.0 |
| - `adam_beta1`: 0.9 |
| - `adam_beta2`: 0.999 |
| - `adam_epsilon`: 1e-08 |
| - `max_grad_norm`: 1.0 |
| - `num_train_epochs`: 1 |
| - `max_steps`: -1 |
| - `lr_scheduler_type`: linear |
| - `lr_scheduler_kwargs`: None |
| - `warmup_ratio`: None |
| - `warmup_steps`: 0.05 |
| - `log_level`: passive |
| - `log_level_replica`: warning |
| - `log_on_each_node`: True |
| - `logging_nan_inf_filter`: True |
| - `enable_jit_checkpoint`: False |
| - `save_on_each_node`: False |
| - `save_only_model`: False |
| - `restore_callback_states_from_checkpoint`: False |
| - `use_cpu`: False |
| - `seed`: 42 |
| - `data_seed`: None |
| - `bf16`: False |
| - `fp16`: True |
| - `bf16_full_eval`: False |
| - `fp16_full_eval`: False |
| - `tf32`: None |
| - `local_rank`: -1 |
| - `ddp_backend`: None |
| - `debug`: [] |
| - `dataloader_drop_last`: False |
| - `dataloader_num_workers`: 0 |
| - `dataloader_prefetch_factor`: None |
| - `disable_tqdm`: False |
| - `remove_unused_columns`: True |
| - `label_names`: None |
| - `load_best_model_at_end`: False |
| - `ignore_data_skip`: False |
| - `fsdp`: [] |
| - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': 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 |
| - `deepspeed`: None |
| - `label_smoothing_factor`: 0.0 |
| - `optim`: adamw_torch_fused |
| - `optim_args`: None |
| - `group_by_length`: False |
| - `length_column_name`: length |
| - `project`: huggingface |
| - `trackio_space_id`: trackio |
| - `ddp_find_unused_parameters`: None |
| - `ddp_bucket_cap_mb`: None |
| - `ddp_broadcast_buffers`: False |
| - `dataloader_pin_memory`: True |
| - `dataloader_persistent_workers`: False |
| - `skip_memory_metrics`: True |
| - `push_to_hub`: False |
| - `resume_from_checkpoint`: None |
| - `hub_model_id`: None |
| - `hub_strategy`: every_save |
| - `hub_private_repo`: None |
| - `hub_always_push`: False |
| - `hub_revision`: None |
| - `gradient_checkpointing`: False |
| - `gradient_checkpointing_kwargs`: None |
| - `include_for_metrics`: [] |
| - `eval_do_concat_batches`: True |
| - `auto_find_batch_size`: False |
| - `full_determinism`: False |
| - `ddp_timeout`: 1800 |
| - `torch_compile`: False |
| - `torch_compile_backend`: None |
| - `torch_compile_mode`: None |
| - `include_num_input_tokens_seen`: no |
| - `neftune_noise_alpha`: None |
| - `optim_target_modules`: None |
| - `batch_eval_metrics`: False |
| - `eval_on_start`: False |
| - `use_liger_kernel`: False |
| - `liger_kernel_config`: None |
| - `eval_use_gather_object`: False |
| - `average_tokens_across_devices`: True |
| - `use_cache`: False |
| - `prompts`: None |
| - `batch_sampler`: batch_sampler |
| - `multi_dataset_batch_sampler`: proportional |
| - `router_mapping`: {} |
| - `learning_rate_mapping`: {} |
|
|
| </details> |
|
|
| ### Training Logs |
| | Epoch | Step | Training Loss | pairs with negatives loss | positives loss | |
| |:------:|:----:|:-------------:|:-------------------------:|:--------------:| |
| | 0.0023 | 25 | 0.4772 | - | - | |
| | 0.0047 | 50 | 0.4593 | - | - | |
| | 0.0070 | 75 | 0.3945 | - | - | |
| | 0.0094 | 100 | 0.3752 | - | - | |
| | 0.0117 | 125 | 0.4048 | - | - | |
| | 0.0140 | 150 | 0.4563 | - | - | |
| | 0.0164 | 175 | 0.3492 | - | - | |
| | 0.0187 | 200 | 0.4171 | 0.3915 | 0.1481 | |
| | 0.0210 | 225 | 0.4297 | - | - | |
| | 0.0234 | 250 | 0.4365 | - | - | |
| | 0.0257 | 275 | 0.4344 | - | - | |
| | 0.0281 | 300 | 0.4184 | - | - | |
| | 0.0304 | 325 | 0.4198 | - | - | |
| | 0.0327 | 350 | 0.4293 | - | - | |
| | 0.0351 | 375 | 0.4759 | - | - | |
| | 0.0374 | 400 | 0.3312 | 0.3695 | 0.1180 | |
| | 0.0398 | 425 | 0.3887 | - | - | |
| | 0.0421 | 450 | 0.4402 | - | - | |
| | 0.0444 | 475 | 0.4105 | - | - | |
| | 0.0468 | 500 | 0.3923 | - | - | |
| | 0.0491 | 525 | 0.3163 | - | - | |
| | 0.0514 | 550 | 0.3565 | - | - | |
| | 0.0538 | 575 | 0.3707 | - | - | |
| | 0.0561 | 600 | 0.3008 | 0.3388 | 0.1086 | |
| | 0.0585 | 625 | 0.3594 | - | - | |
| | 0.0608 | 650 | 0.3936 | - | - | |
| | 0.0631 | 675 | 0.3207 | - | - | |
| | 0.0655 | 700 | 0.3371 | - | - | |
| | 0.0678 | 725 | 0.3385 | - | - | |
| | 0.0702 | 750 | 0.2718 | - | - | |
| | 0.0725 | 775 | 0.4429 | - | - | |
| | 0.0748 | 800 | 0.2684 | 0.3453 | 0.1043 | |
| | 0.0772 | 825 | 0.2539 | - | - | |
| | 0.0795 | 850 | 0.3239 | - | - | |
| | 0.0818 | 875 | 0.2944 | - | - | |
| | 0.0842 | 900 | 0.3067 | - | - | |
| | 0.0865 | 925 | 0.3113 | - | - | |
| | 0.0889 | 950 | 0.3387 | - | - | |
| | 0.0912 | 975 | 0.2735 | - | - | |
| | 0.0935 | 1000 | 0.2985 | 0.3211 | 0.0891 | |
| | 0.0959 | 1025 | 0.3553 | - | - | |
| | 0.0982 | 1050 | 0.2568 | - | - | |
| | 0.1006 | 1075 | 0.3447 | - | - | |
| | 0.1029 | 1100 | 0.3239 | - | - | |
| | 0.1052 | 1125 | 0.3015 | - | - | |
| | 0.1076 | 1150 | 0.3865 | - | - | |
| | 0.1099 | 1175 | 0.2982 | - | - | |
| | 0.1122 | 1200 | 0.3105 | 0.3232 | 0.0829 | |
| | 0.1146 | 1225 | 0.2964 | - | - | |
| | 0.1169 | 1250 | 0.2417 | - | - | |
| | 0.1193 | 1275 | 0.2686 | - | - | |
| | 0.1216 | 1300 | 0.2932 | - | - | |
| | 0.1239 | 1325 | 0.2383 | - | - | |
| | 0.1263 | 1350 | 0.3108 | - | - | |
| | 0.1286 | 1375 | 0.3216 | - | - | |
| | 0.1310 | 1400 | 0.2083 | 0.3091 | 0.0894 | |
| | 0.1333 | 1425 | 0.2933 | - | - | |
| | 0.1356 | 1450 | 0.2038 | - | - | |
| | 0.1380 | 1475 | 0.2515 | - | - | |
| | 0.1403 | 1500 | 0.2643 | - | - | |
| | 0.1426 | 1525 | 0.2484 | - | - | |
| | 0.1450 | 1550 | 0.3216 | - | - | |
| | 0.1473 | 1575 | 0.3265 | - | - | |
| | 0.1497 | 1600 | 0.2626 | 0.3166 | 0.0775 | |
| | 0.1520 | 1625 | 0.2811 | - | - | |
| | 0.1543 | 1650 | 0.2792 | - | - | |
| | 0.1567 | 1675 | 0.2888 | - | - | |
| | 0.1590 | 1700 | 0.3243 | - | - | |
| | 0.1614 | 1725 | 0.2318 | - | - | |
| | 0.1637 | 1750 | 0.2943 | - | - | |
| | 0.1660 | 1775 | 0.2494 | - | - | |
| | 0.1684 | 1800 | 0.3478 | 0.3113 | 0.0751 | |
| | 0.1707 | 1825 | 0.3265 | - | - | |
| | 0.1730 | 1850 | 0.2933 | - | - | |
| | 0.1754 | 1875 | 0.2671 | - | - | |
| | 0.1777 | 1900 | 0.2927 | - | - | |
| | 0.1801 | 1925 | 0.2939 | - | - | |
| | 0.1824 | 1950 | 0.2356 | - | - | |
| | 0.1847 | 1975 | 0.2413 | - | - | |
| | 0.1871 | 2000 | 0.2026 | 0.2921 | 0.0650 | |
|
|
|
|
| ### Training Time |
| - **Training**: 2.2 hours |
|
|
| ### Framework Versions |
| - Python: 3.12.13 |
| - Sentence Transformers: 5.4.1 |
| - Transformers: 5.0.0 |
| - PyTorch: 2.10.0+cu128 |
| - Accelerate: 1.13.0 |
| - Datasets: 5.0.0 |
| - 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", |
| } |
| ``` |
|
|
| #### CachedMultipleNegativesRankingLoss |
| ```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} |
| } |
| ``` |
|
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