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mjaliz
/
bslm-pair-original-multipositive

Sentence Similarity
sentence-transformers
Safetensors
xlm-roberta
feature-extraction
Generated from Trainer
dataset_size:2991559
loss:CachedMultiPositiveRankingLoss
Eval Results (legacy)
text-embeddings-inference
Model card Files
xet
 Community

SentenceTransformer based on mjaliz/bslm-pair-206k

This is a sentence-transformers model finetuned from mjaliz/bslm-pair-206k on the json dataset. 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: mjaliz/bslm-pair-206k
  • Maximum Sequence Length: 96 tokens
  • Output Dimensionality: 1024 dimensions
  • Similarity Function: Cosine Similarity
  • Supported Modality: Text
  • Training Dataset:
    • json

Model Sources

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': 'XLMRobertaModel'})
  (1): Pooling({'embedding_dimension': 1024, 'pooling_mode': 'mean', 'include_prompt': True})
)

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 SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
queries = [
    'لباس هودی',
]
documents = [
    'هودی مشکی طرح رونالدینیو لباس بارسلونا FC Barcelona',
    'هودی رونالدینیو با لباس بارسلونا و ژست خاص - کد 11311',
    'p-33828382',
]
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([[0.7550, 0.7623, 0.2425]])

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.7245
cosine_accuracy@5 0.9007
cosine_accuracy@10 0.9359
cosine_accuracy@50 0.9787
cosine_precision@1 0.7245
cosine_precision@5 0.2571
cosine_precision@10 0.151
cosine_precision@50 0.0394
cosine_recall@1 0.6032
cosine_recall@5 0.8531
cosine_recall@10 0.9069
cosine_recall@50 0.9705
cosine_ndcg@10 0.8212
cosine_mrr@10 0.8006
cosine_map@10 0.7809

Training Details

Training Dataset

json

  • Dataset: json
  • Size: 2,991,559 training samples
  • Columns: anchor, rewritten_title, original_title, original_usable, product_group_id, product_id, and query_id
  • Approximate statistics based on the first 100 samples:
    anchor rewritten_title original_title original_usable product_group_id product_id query_id
    type string string string bool int string string
    modality text text text text text
    details
    • min: 5 tokens
    • mean: 7.99 tokens
    • max: 15 tokens
    • min: 12 tokens
    • mean: 22.05 tokens
    • max: 41 tokens
    • min: 5 tokens
    • mean: 14.7 tokens
    • max: 33 tokens
    • False: ~5.77%
    • True: ~94.23%
    • 1030862751937536: ~0.96%
    • 119064850511069427: ~0.96%
    • 192511709702592144: ~0.96%
    • 263328678771954932: ~0.96%
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    • min: 6 tokens
    • mean: 6.76 tokens
    • max: 7 tokens
    • min: 15 tokens
    • mean: 18.69 tokens
    • max: 24 tokens
  • Samples:
    anchor rewritten_title original_title original_usable product_group_id product_id query_id
    مانتو تابستانه سایزبزرگ مانتو زنانه بلند کتان لمه مهتابان (Mahtaban) مدل قیطون‌دوزی شده سایزبزرگ مانتو مزونی سایزبزرگ و بلند کتان لمه True 7393488442012240316 p-13064828 q-77a5991bdb89f8ef0c9d74e1
    شال توری بهاره شال رینگی زنانه بافت توری مشکی ریزش ملایم بهاره تابستانه شال رینگی محرم بافت توری سبک بهاره تابستان True 3428136546146965774 p-35761883 q-6df2cf14552954703bc181fe
    کاور صندلی خودرو روکش صندلی خودرو ماتین کاور طرح لاماری جودون برای پراید 111 هاچبک رنگ عسلی روکش صندلی جودون مخمل سنگین پراید111هاچبک طرح لاماری عسلی False 1499769885652000917 p-22154541 q-23d3831029fb651753a41fdc
  • Loss: const_learning.multi_positive.CachedMultiPositiveRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "mini_batch_size": 64,
    "gather_across_devices": true,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0,
    "strict_product_unique": true,
    "positive_aggregation": "equal_mean",
    "candidate_normalization": "per_product"
}

Evaluation Dataset

json

  • Dataset: json
  • Size: 25,835 evaluation samples
  • Columns: anchor, rewritten_title, original_title, original_usable, product_group_id, product_id, and query_id
  • Approximate statistics based on the first 100 samples:
    anchor rewritten_title original_title original_usable product_group_id product_id query_id
    type string string string bool int string string
    modality text text text text text
    details
    • min: 4 tokens
    • mean: 7.61 tokens
    • max: 12 tokens
    • min: 10 tokens
    • mean: 22.74 tokens
    • max: 32 tokens
    • min: 4 tokens
    • mean: 15.21 tokens
    • max: 38 tokens
    • False: ~8.65%
    • True: ~91.35%
    • 85480883611077928: ~0.96%
    • 94620141887068576: ~0.96%
    • 126939967775193646: ~0.96%
    • 414555575604870224: ~0.96%
    • 508123139848592225: ~0.96%
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    • 8962612547428606787: ~0.96%
    • min: 6 tokens
    • mean: 6.61 tokens
    • max: 7 tokens
    • min: 15 tokens
    • mean: 18.97 tokens
    • max: 24 tokens
  • Samples:
    anchor rewritten_title original_title original_usable product_group_id product_id query_id
    رژگونه توت فرنگی بالم لب توت فرنگی Anakan (آناکان) آبرسان ویتامینه گیاهی بالم لب توت فرنگی آناکان True 4981092919564861111 p-3944596 q-895b1656011116619174b327
    چای ساز کوخ چای ساز برقی Kouch (کوخ) مدل KT-2150 مشکی، 2200 وات، کتری استیل دوجداره چای ساز کوخ مدل KT-2150 True 3824352126458114029 p-18108877 q-dda85c17f5fd6238cab274f2
    میز کنسول میز کنسول FARASHAHGROUP با درب طرح چوب و قفسه بندی باز میز کنسول ارسال رایگان به مسیر های یکطرفه True 5553644345276244089 p-15061643 q-55199bfb027445c0af320d1c
  • Loss: const_learning.multi_positive.CachedMultiPositiveRankingLoss with these parameters:
    {
    "scale": 20.0,
    "similarity_fct": "cos_sim",
    "mini_batch_size": 64,
    "gather_across_devices": true,
    "directions": [
        "query_to_doc"
    ],
    "partition_mode": "joint",
    "hardness_mode": null,
    "hardness_strength": 0.0,
    "strict_product_unique": true,
    "positive_aggregation": "equal_mean",
    "candidate_normalization": "per_product"
}

Training Hyperparameters

Non-Default Hyperparameters

  • per_device_train_batch_size: 256
  • num_train_epochs: 5.0
  • learning_rate: 2e-05
  • warmup_steps: 0.1
  • weight_decay: 0.01
  • bf16: True
  • per_device_eval_batch_size: 256
  • load_best_model_at_end: True
  • dataloader_num_workers: 4

All Hyperparameters

Click to expand
  • per_device_train_batch_size: 256
  • num_train_epochs: 5.0
  • max_steps: -1
  • learning_rate: 2e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 0.1
  • optim: adamw_torch_fused
  • optim_args: None
  • weight_decay: 0.01
  • 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: 256
  • 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: False
  • 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: True
  • dataloader_num_workers: 4
  • 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
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {}
  • learning_rate_mapping: {}

Training Logs

Click to expand
Epoch Step Training Loss Validation Loss dev_cosine_ndcg@10
0.0257 100 1.8373 - -
0.0513 200 1.4902 - -
0.0770 300 1.3684 - -
0.1027 400 1.3063 - -
0.1284 500 1.2667 - -
0.1540 600 1.2234 - -
0.1797 700 1.1961 - -
0.2054 800 1.1584 - -
0.2311 900 1.1445 - -
0.2567 1000 1.1288 0.6568 0.7796
0.2824 1100 1.1125 - -
0.3081 1200 1.0952 - -
0.3338 1300 1.0887 - -
0.3594 1400 1.0705 - -
0.3851 1500 1.0569 - -
0.4108 1600 1.0564 - -
0.4365 1700 1.0422 - -
0.4621 1800 1.0348 - -
0.4878 1900 1.0256 - -
0.5135 2000 1.0304 0.5899 0.7965
0.5392 2100 1.0091 - -
0.5648 2200 0.9997 - -
0.5905 2300 0.9949 - -
0.6162 2400 0.9925 - -
0.6418 2500 0.9828 - -
0.6675 2600 0.9773 - -
0.6932 2700 0.9660 - -
0.7189 2800 0.9637 - -
0.7445 2900 0.9615 - -
0.7702 3000 0.9556 0.5464 0.8011
0.7959 3100 0.9519 - -
0.8216 3200 0.9488 - -
0.8472 3300 0.9433 - -
0.8729 3400 0.9390 - -
0.8986 3500 0.9346 - -
0.9243 3600 0.9347 - -
0.9499 3700 0.9297 - -
0.9756 3800 0.9111 - -
1.0013 3900 0.8751 - -
1.0270 4000 0.8671 0.5363 0.8072
1.0526 4100 0.8883 - -
1.0783 4200 0.9159 - -
1.1040 4300 0.9148 - -
1.1297 4400 0.9113 - -
1.1553 4500 0.8999 - -
1.1810 4600 0.8930 - -
1.2067 4700 0.8841 - -
1.2323 4800 0.8862 - -
1.2580 4900 0.8849 - -
1.2837 5000 0.8828 0.5030 0.8050
1.3094 5100 0.8746 - -
1.3350 5200 0.8755 - -
1.3607 5300 0.8699 - -
1.3864 5400 0.8636 - -
1.4121 5500 0.8661 - -
1.4377 5600 0.8630 - -
1.4634 5700 0.8614 - -
1.4891 5800 0.8549 - -
1.5148 5900 0.8617 - -
1.5404 6000 0.8479 0.4931 0.8080
1.5661 6100 0.8432 - -
1.5918 6200 0.8462 - -
1.6175 6300 0.8460 - -
1.6431 6400 0.8432 - -
1.6688 6500 0.8379 - -
1.6945 6600 0.8338 - -
1.7202 6700 0.8341 - -
1.7458 6800 0.8350 - -
1.7715 6900 0.8317 - -
1.7972 7000 0.8334 0.4852 0.8122
1.8228 7100 0.8302 - -
1.8485 7200 0.8274 - -
1.8742 7300 0.8287 - -
1.8999 7400 0.8264 - -
1.9255 7500 0.8226 - -
1.9512 7600 0.8234 - -
1.9769 7700 0.7968 - -
2.0026 7800 0.7554 - -
2.0282 7900 0.7589 - -
2.0539 8000 0.7911 0.4779 0.8134
2.0796 8100 0.8214 - -
2.1053 8200 0.8227 - -
2.1309 8300 0.8186 - -
2.1566 8400 0.8113 - -
2.1823 8500 0.8050 - -
2.2080 8600 0.8023 - -
2.2336 8700 0.8007 - -
2.2593 8800 0.8009 - -
2.2850 8900 0.8041 - -
2.3107 9000 0.7984 0.4763 0.8165
2.3363 9100 0.7986 - -
2.3620 9200 0.7948 - -
2.3877 9300 0.7915 - -
2.4134 9400 0.7930 - -
2.4390 9500 0.7929 - -
2.4647 9600 0.7892 - -
2.4904 9700 0.7856 - -
2.5160 9800 0.7904 - -
2.5417 9900 0.7846 - -
2.5674 10000 0.7755 0.4642 0.8167
2.5931 10100 0.7845 - -
2.6187 10200 0.7839 - -
2.6444 10300 0.7787 - -
2.6701 10400 0.7766 - -
2.6958 10500 0.7745 - -
2.7214 10600 0.7757 - -
2.7471 10700 0.7764 - -
2.7728 10800 0.7716 - -
2.7985 10900 0.7769 - -
2.8241 11000 0.7725 0.4640 0.8194
2.8498 11100 0.7717 - -
2.8755 11200 0.7738 - -
2.9012 11300 0.7715 - -
2.9268 11400 0.7670 - -
2.9525 11500 0.7699 - -
2.9782 11600 0.7339 - -
3.0039 11700 0.6915 - -
3.0295 11800 0.7036 - -
3.0552 11900 0.7434 - -
3.0809 12000 0.7703 0.4626 0.8193
3.1065 12100 0.7731 - -
3.1322 12200 0.7698 - -
3.1579 12300 0.7599 - -
3.1836 12400 0.7585 - -
3.2092 12500 0.7577 - -
3.2349 12600 0.7522 - -
3.2606 12700 0.7561 - -
3.2863 12800 0.7572 - -
3.3119 12900 0.7556 - -
3.3376 13000 0.7535 0.4566 0.8195
3.3633 13100 0.7527 - -
3.3890 13200 0.7466 - -
3.4146 13300 0.7532 - -
3.4403 13400 0.7507 - -
3.4660 13500 0.7513 - -
3.4917 13600 0.7456 - -
3.5173 13700 0.7496 - -
3.5430 13800 0.7464 - -
3.5687 13900 0.7360 - -
3.5944 14000 0.7480 0.4513 0.8188
3.6200 14100 0.7444 - -
3.6457 14200 0.7422 - -
3.6714 14300 0.7408 - -
3.6970 14400 0.7392 - -
3.7227 14500 0.7424 - -
3.7484 14600 0.7402 - -
3.7741 14700 0.7364 - -
3.7997 14800 0.7384 - -
3.8254 14900 0.7371 - -
3.8511 15000 0.7391 0.4489 0.8197
3.8768 15100 0.7407 - -
3.9024 15200 0.7379 - -
3.9281 15300 0.7336 - -
3.9538 15400 0.7392 - -
3.9795 15500 0.6932 - -
4.0051 15600 0.6537 - -
4.0308 15700 0.6760 - -
4.0565 15800 0.7136 - -
4.0822 15900 0.7395 - -
4.1078 16000 0.7446 0.4479 0.8219
4.1335 16100 0.7377 - -
4.1592 16200 0.7317 - -
4.1849 16300 0.7277 - -
4.2105 16400 0.7286 - -
4.2362 16500 0.7252 - -
4.2619 16600 0.7299 - -
4.2875 16700 0.7288 - -
4.3132 16800 0.7274 - -
4.3389 16900 0.7278 - -
4.3646 17000 0.7239 0.4475 0.8201
4.3902 17100 0.7195 - -
4.4159 17200 0.7270 - -
4.4416 17300 0.7246 - -
4.4673 17400 0.7254 - -
4.4929 17500 0.7224 - -
4.5186 17600 0.7239 - -
4.5443 17700 0.7224 - -
4.5700 17800 0.7152 - -
4.5956 17900 0.7253 - -
4.6213 18000 0.7178 0.4453 0.8209
4.6470 18100 0.7208 - -
4.6727 18200 0.7174 - -
4.6983 18300 0.7182 - -
4.7240 18400 0.7198 - -
4.7497 18500 0.7200 - -
4.7754 18600 0.7164 - -
4.8010 18700 0.7182 - -
4.8267 18800 0.7177 - -
4.8524 18900 0.7184 - -
4.8780 19000 0.7229 0.4440 0.8211
4.9037 19100 0.7175 - -
4.9294 19200 0.7171 - -
4.9551 19300 0.7187 - -
4.9807 19400 0.6665 - -
5.0 19475 - 0.4457 0.8212
  • The bold row denotes the saved checkpoint.

Training Time

  • Training: 4.0 days
  • Evaluation: 15.4 minutes
  • Total: 4.0 days

Framework Versions

  • Python: 3.11.14
  • Sentence Transformers: 5.6.0
  • Transformers: 5.12.1
  • PyTorch: 2.12.1+cu130
  • Accelerate: 1.14.0
  • Datasets: 5.0.0
  • 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",
}

CachedMultiPositiveRankingLoss 

@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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