--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:1600000 - loss:TripletLoss datasets: [] metrics: - cosine_accuracy - dot_accuracy - manhattan_accuracy - euclidean_accuracy - max_accuracy - pearson_cosine - spearman_cosine - pearson_manhattan - spearman_manhattan - pearson_euclidean - spearman_euclidean - pearson_dot - spearman_dot - pearson_max - spearman_max - cosine_accuracy@10 - cosine_precision@10 - cosine_recall@10 - cosine_ndcg@10 - cosine_mrr@10 - cosine_map@10 - dot_accuracy@10 - dot_precision@10 - dot_recall@10 - dot_ndcg@10 - dot_mrr@10 - dot_map@10 widget: - source_sentence: 'search_query: pokemon card mewtwo' sentences: - 'search_document: Personal AM/FM Pocket Radio Portable VR-robot, Mini Digital Tuning Walkman Radio, with Rechargeable Battery, Earphone, Lock Screen for Walk/Jogging/Gym/Camping, VR-robot, Electronics' - 'search_document: Pokemon Mewtwo & Pikachu XY Evolutions TCG Card Game Decks - 60 Cards Each, Pokemon, ' - 'search_document: Ultra Pro Pokemon: Charizard Album, 2", Ultra Pro, ' - source_sentence: 'search_query: table runners 108 inches' sentences: - 'search_document: Sambosk Fall Buffalo Pumpkin Table Runner, Autumn Farmhouse Table Runners for Kitchen Dining Coffee or Indoor and Outdoor Home Parties Decor 13 x 72 Inches SK006, Sambosk, Black White' - 'search_document: EYEGUARD Readers 4 Pack of Thin and Elegant Womens Reading Glasses with Beautiful Patterns for Ladies 1.00, EYEGUARD, Mix' - 'search_document: Sunfiy 4 Pack Red Satin Table Runner 12 x 108 Inch Long Table Runners for Wedding Birthday Parties Banquets Graduations Engagements, Sunfiy, Red' - source_sentence: 'search_query: nursing shoes for women' sentences: - 'search_document: Hawkwell Women''s Lightweight Comfort Slip Resistant Nursing Shoes,White PU,10 M US, Hawkwell, 1923/White' - 'search_document: REESE''S Peanut Butter Milk Chocolate You''re Amazing Appreciation Candy Bars for Christmas and Holiday Season, 4.2 oz Bars, 12 Count, Reese''s, ' - 'search_document: adidas womens Cloudfoam Pure Running Shoe, Black/Black, 7.5 US, adidas, Black/Black/White' - source_sentence: 'search_query: mens socks black and white' sentences: - 'search_document: Fruit of the Loom Men''s Essential 6 Pack Casual Crew Socks | Arch Support | Black & White, Black, Shoe Size: 6-12, Fruit of the Loom, Black' - 'search_document: adidas Originals Men''s Trefoil Crew Socks (6-Pair), White/Black Black/White, Large, (Shoe Size 6-12), adidas Originals, White/Black' - 'search_document: Fifty Shades of Grey, , ' - source_sentence: 'search_query: karoke set 2 microphone for adults' sentences: - 'search_document: EARISE T26 Portable Karaoke Machine Bluetooth Speaker with Wireless Microphone, Rechargeable PA System with FM Radio, Audio Recording, Remote Control, Supports TF Card/USB, Perfect for Party, EARISE, ' - 'search_document: FunWorld Men''s Complete 3D Zombie Costume, Grey, One Size, Fun World, Multi' - 'search_document: Starion KS829-B Bluetooth Karaoke Machine l Pedestal Design w/Light Show l Two Karaoke Microphones, Starion, Black' pipeline_tag: sentence-similarity model-index: - name: SentenceTransformer results: - task: type: triplet name: Triplet dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.7298125 name: Cosine Accuracy - type: dot_accuracy value: 0.2831875 name: Dot Accuracy - type: manhattan_accuracy value: 0.72825 name: Manhattan Accuracy - type: euclidean_accuracy value: 0.729875 name: Euclidean Accuracy - type: max_accuracy value: 0.729875 name: Max Accuracy - task: type: semantic-similarity name: Semantic Similarity dataset: name: Unknown type: unknown metrics: - type: pearson_cosine value: 0.4148003591706621 name: Pearson Cosine - type: spearman_cosine value: 0.39973675544358156 name: Spearman Cosine - type: pearson_manhattan value: 0.37708819507475255 name: Pearson Manhattan - type: spearman_manhattan value: 0.36992167570513307 name: Spearman Manhattan - type: pearson_euclidean value: 0.3777862291730549 name: Pearson Euclidean - type: spearman_euclidean value: 0.3707889635811508 name: Spearman Euclidean - type: pearson_dot value: 0.3813644395159763 name: Pearson Dot - type: spearman_dot value: 0.3817136551173837 name: Spearman Dot - type: pearson_max value: 0.4148003591706621 name: Pearson Max - type: spearman_max value: 0.39973675544358156 name: Spearman Max - task: type: information-retrieval name: Information Retrieval dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy@10 value: 0.967 name: Cosine Accuracy@10 - type: cosine_precision@10 value: 0.6951 name: Cosine Precision@10 - type: cosine_recall@10 value: 0.6216729831257005 name: Cosine Recall@10 - type: cosine_ndcg@10 value: 0.8300106033542061 name: Cosine Ndcg@10 - type: cosine_mrr@10 value: 0.9111154761904765 name: Cosine Mrr@10 - type: cosine_map@10 value: 0.7758485833963215 name: Cosine Map@10 - type: dot_accuracy@10 value: 0.946 name: Dot Accuracy@10 - type: dot_precision@10 value: 0.6369 name: Dot Precision@10 - type: dot_recall@10 value: 0.5693415261440723 name: Dot Recall@10 - type: dot_ndcg@10 value: 0.7668657376718138 name: Dot Ndcg@10 - type: dot_mrr@10 value: 0.8754059523809526 name: Dot Mrr@10 - type: dot_map@10 value: 0.6962231903502142 name: Dot Map@10 --- # SentenceTransformer This is a [sentence-transformers](https://www.SBERT.net) model trained on the triplets dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. ## Model Details ### Model Description - **Model Type:** Sentence Transformer - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - triplets ### Model Sources - **Documentation:** [Sentence Transformers Documentation](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co./models?library=sentence-transformers) ### Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) ) ``` ## 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("lv12/esci-nomic-embed-text-v1_5_4") # Run inference sentences = [ 'search_query: karoke set 2 microphone for adults', 'search_document: Starion KS829-B Bluetooth Karaoke Machine l Pedestal Design w/Light Show l Two Karaoke Microphones, Starion, Black', 'search_document: EARISE T26 Portable Karaoke Machine Bluetooth Speaker with Wireless Microphone, Rechargeable PA System with FM Radio, Audio Recording, Remote Control, Supports TF Card/USB, Perfect for Party, EARISE, ', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Triplet * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:-----------| | **cosine_accuracy** | **0.7298** | | dot_accuracy | 0.2832 | | manhattan_accuracy | 0.7282 | | euclidean_accuracy | 0.7299 | | max_accuracy | 0.7299 | #### Semantic Similarity * Evaluated with [EmbeddingSimilarityEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator) | Metric | Value | |:--------------------|:-----------| | pearson_cosine | 0.4148 | | **spearman_cosine** | **0.3997** | | pearson_manhattan | 0.3771 | | spearman_manhattan | 0.3699 | | pearson_euclidean | 0.3778 | | spearman_euclidean | 0.3708 | | pearson_dot | 0.3814 | | spearman_dot | 0.3817 | | pearson_max | 0.4148 | | spearman_max | 0.3997 | #### Information Retrieval * Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) | Metric | Value | |:--------------------|:-----------| | cosine_accuracy@10 | 0.967 | | cosine_precision@10 | 0.6951 | | cosine_recall@10 | 0.6217 | | cosine_ndcg@10 | 0.83 | | cosine_mrr@10 | 0.9111 | | **cosine_map@10** | **0.7758** | | dot_accuracy@10 | 0.946 | | dot_precision@10 | 0.6369 | | dot_recall@10 | 0.5693 | | dot_ndcg@10 | 0.7669 | | dot_mrr@10 | 0.8754 | | dot_map@10 | 0.6962 | ## Training Details ### Training Dataset #### triplets * Dataset: triplets * Size: 1,600,000 training samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:--------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | search_query: udt hydraulic fluid | search_document: Triax Agra UTTO XL Synthetic Blend Tractor Transmission and Hydraulic Oil, 6,000 Hour Life, 50% Less wear, 36F Pour Point, Replaces All OEM Tractor Fluids (5 Gallon Pail), TRIAX, | search_document: Shell Rotella T5 Synthetic Blend 15W-40 Diesel Engine Oil (1-Gallon, Case of 3), Shell Rotella, | | search_query: cheetah print iphone xs case | search_document: iPhone Xs Case, iPhone Xs Case,Doowear Leopard Cheetah Protective Cover Shell For Girls Women,Slim Fit Anti Scratch Shockproof Soft TPU Bumper Flexible Rubber Gel Silicone Case for iPhone Xs / X-1, Ebetterr, 1 | search_document: iPhone Xs & iPhone X Case, J.west Luxury Sparkle Bling Translucent Leopard Print Soft Silicone Phone Case Cover for Girls Women Flex Slim Design Pattern Drop Protective Case for iPhone Xs/x 5.8 inch, J.west, Leopard | | search_query: platform shoes | search_document: Teva Women's Flatform Universal Platform Sandal, Black, 5 M US, Teva, Black | search_document: Vans Women's Old Skool Platform Trainers, (Black/White Y28), 5 UK 38 EU, Vans, Black/White | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.COSINE", "triplet_margin": 0.8 } ``` ### Evaluation Dataset #### triplets * Dataset: triplets * Size: 16,000 evaluation samples * Columns: anchor, positive, and negative * Approximate statistics based on the first 1000 samples: | | anchor | positive | negative | |:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | string | string | string | | details | | | | * Samples: | anchor | positive | negative | |:---------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------| | search_query: hogknobz | search_document: Black 2014-2015 HDsmallPARTS/LocEzy Saddlebag Mounting Hardware Knobs are replacement/compatible for Saddlebag Quick Release Pins on Harley Davidson Touring Motorcycles Theft Deterrent, LocEzy, | search_document: HANSWD Saddlebag Support Bars Brackets For SUZUKI YAMAHA KAWASAKI (Black), HANSWD, Black | | search_query: tile sticker key finder | search_document: Tile Sticker (2020) 2-pack - Small, Adhesive Bluetooth Tracker, Item Locator and Finder for Remotes, Headphones, Gadgets and More, Tile, | search_document: Tile Pro Combo (2017) - 2 Pack (1 x Sport, 1 x Style) - Discontinued by Manufacturer, Tile, Graphite/Gold | | search_query: adobe incense burner | search_document: AM Incense Burner Frankincense Resin - Luxury Globe Charcoal Bakhoor Burners for Office & Home Decor (Brown), AM, Brown | search_document: semli Large Incense Burner Backflow Incense Burner Holder Incense Stick Holder Home Office Decor, Semli, | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.COSINE", "triplet_margin": 0.8 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 16 - `gradient_accumulation_steps`: 2 - `learning_rate`: 1e-07 - `num_train_epochs`: 5 - `lr_scheduler_type`: polynomial - `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0} - `warmup_ratio`: 0.05 - `dataloader_drop_last`: True - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: 4 - `load_best_model_at_end`: True - `gradient_checkpointing`: True - `auto_find_batch_size`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `prediction_loss_only`: True - `per_device_train_batch_size`: 64 - `per_device_eval_batch_size`: 16 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `learning_rate`: 1e-07 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 5 - `max_steps`: -1 - `lr_scheduler_type`: polynomial - `lr_scheduler_kwargs`: {'lr_end': 1e-08, 'power': 2.0} - `warmup_ratio`: 0.05 - `warmup_steps`: 0 - `log_level`: passive - `log_level_replica`: warning - `log_on_each_node`: True - `logging_nan_inf_filter`: True - `save_safetensors`: True - `save_on_each_node`: False - `save_only_model`: False - `no_cuda`: False - `use_cpu`: False - `use_mps_device`: False - `seed`: 42 - `data_seed`: None - `jit_mode_eval`: False - `use_ipex`: False - `bf16`: False - `fp16`: False - `fp16_opt_level`: O1 - `half_precision_backend`: auto - `bf16_full_eval`: False - `fp16_full_eval`: False - `tf32`: None - `local_rank`: 0 - `ddp_backend`: None - `tpu_num_cores`: None - `tpu_metrics_debug`: False - `debug`: [] - `dataloader_drop_last`: True - `dataloader_num_workers`: 4 - `dataloader_prefetch_factor`: 4 - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: True - `ignore_data_skip`: False - `fsdp`: [] - `fsdp_min_num_params`: 0 - `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} - `fsdp_transformer_layer_cls_to_wrap`: None - `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True} - `deepspeed`: None - `label_smoothing_factor`: 0.0 - `optim`: adamw_torch - `optim_args`: None - `adafactor`: False - `group_by_length`: False - `length_column_name`: length - `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 - `use_legacy_prediction_loop`: False - `push_to_hub`: False - `resume_from_checkpoint`: None - `hub_model_id`: None - `hub_strategy`: every_save - `hub_private_repo`: False - `hub_always_push`: False - `gradient_checkpointing`: True - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `fp16_backend`: auto - `push_to_hub_model_id`: None - `push_to_hub_organization`: None - `mp_parameters`: - `auto_find_batch_size`: True - `full_determinism`: False - `torchdynamo`: None - `ray_scope`: last - `ddp_timeout`: 1800 - `torch_compile`: False - `torch_compile_backend`: None - `torch_compile_mode`: None - `dispatch_batches`: None - `split_batches`: None - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `neftune_noise_alpha`: None - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs
Click to expand | Epoch | Step | Training Loss | triplets loss | cosine_accuracy | cosine_map@10 | spearman_cosine | |:------:|:----:|:-------------:|:-------------:|:---------------:|:-------------:|:---------------:| | 0.0008 | 10 | 0.7505 | - | - | - | - | | 0.0016 | 20 | 0.7499 | - | - | - | - | | 0.0024 | 30 | 0.7524 | - | - | - | - | | 0.0032 | 40 | 0.7486 | - | - | - | - | | 0.004 | 50 | 0.7493 | - | - | - | - | | 0.0048 | 60 | 0.7476 | - | - | - | - | | 0.0056 | 70 | 0.7483 | - | - | - | - | | 0.0064 | 80 | 0.7487 | - | - | - | - | | 0.0072 | 90 | 0.7496 | - | - | - | - | | 0.008 | 100 | 0.7515 | 0.7559 | 0.7263 | 0.7684 | 0.3941 | | 0.0088 | 110 | 0.7523 | - | - | - | - | | 0.0096 | 120 | 0.7517 | - | - | - | - | | 0.0104 | 130 | 0.7534 | - | - | - | - | | 0.0112 | 140 | 0.746 | - | - | - | - | | 0.012 | 150 | 0.7528 | - | - | - | - | | 0.0128 | 160 | 0.7511 | - | - | - | - | | 0.0136 | 170 | 0.7491 | - | - | - | - | | 0.0144 | 180 | 0.752 | - | - | - | - | | 0.0152 | 190 | 0.7512 | - | - | - | - | | 0.016 | 200 | 0.7513 | 0.7557 | 0.7259 | 0.7688 | 0.3942 | | 0.0168 | 210 | 0.7505 | - | - | - | - | | 0.0176 | 220 | 0.7481 | - | - | - | - | | 0.0184 | 230 | 0.7516 | - | - | - | - | | 0.0192 | 240 | 0.7504 | - | - | - | - | | 0.02 | 250 | 0.7498 | - | - | - | - | | 0.0208 | 260 | 0.7506 | - | - | - | - | | 0.0216 | 270 | 0.7486 | - | - | - | - | | 0.0224 | 280 | 0.7471 | - | - | - | - | | 0.0232 | 290 | 0.7511 | - | - | - | - | | 0.024 | 300 | 0.7506 | 0.7553 | 0.7258 | 0.7692 | 0.3943 | | 0.0248 | 310 | 0.7485 | - | - | - | - | | 0.0256 | 320 | 0.7504 | - | - | - | - | | 0.0264 | 330 | 0.7456 | - | - | - | - | | 0.0272 | 340 | 0.7461 | - | - | - | - | | 0.028 | 350 | 0.7496 | - | - | - | - | | 0.0288 | 360 | 0.7518 | - | - | - | - | | 0.0296 | 370 | 0.7514 | - | - | - | - | | 0.0304 | 380 | 0.7479 | - | - | - | - | | 0.0312 | 390 | 0.7507 | - | - | - | - | | 0.032 | 400 | 0.7511 | 0.7547 | 0.7258 | 0.7695 | 0.3945 | | 0.0328 | 410 | 0.7491 | - | - | - | - | | 0.0336 | 420 | 0.7487 | - | - | - | - | | 0.0344 | 430 | 0.7496 | - | - | - | - | | 0.0352 | 440 | 0.7464 | - | - | - | - | | 0.036 | 450 | 0.7518 | - | - | - | - | | 0.0368 | 460 | 0.7481 | - | - | - | - | | 0.0376 | 470 | 0.7493 | - | - | - | - | | 0.0384 | 480 | 0.753 | - | - | - | - | | 0.0392 | 490 | 0.7475 | - | - | - | - | | 0.04 | 500 | 0.7498 | 0.7540 | 0.7262 | 0.7700 | 0.3948 | | 0.0408 | 510 | 0.7464 | - | - | - | - | | 0.0416 | 520 | 0.7506 | - | - | - | - | | 0.0424 | 530 | 0.747 | - | - | - | - | | 0.0432 | 540 | 0.7462 | - | - | - | - | | 0.044 | 550 | 0.75 | - | - | - | - | | 0.0448 | 560 | 0.7522 | - | - | - | - | | 0.0456 | 570 | 0.7452 | - | - | - | - | | 0.0464 | 580 | 0.7475 | - | - | - | - | | 0.0472 | 590 | 0.7507 | - | - | - | - | | 0.048 | 600 | 0.7494 | 0.7531 | 0.7269 | 0.7707 | 0.3951 | | 0.0488 | 610 | 0.7525 | - | - | - | - | | 0.0496 | 620 | 0.7446 | - | - | - | - | | 0.0504 | 630 | 0.7457 | - | - | - | - | | 0.0512 | 640 | 0.7462 | - | - | - | - | | 0.052 | 650 | 0.7478 | - | - | - | - | | 0.0528 | 660 | 0.7459 | - | - | - | - | | 0.0536 | 670 | 0.7465 | - | - | - | - | | 0.0544 | 680 | 0.7495 | - | - | - | - | | 0.0552 | 690 | 0.7513 | - | - | - | - | | 0.056 | 700 | 0.7445 | 0.7520 | 0.7274 | 0.7705 | 0.3954 | | 0.0568 | 710 | 0.7446 | - | - | - | - | | 0.0576 | 720 | 0.746 | - | - | - | - | | 0.0584 | 730 | 0.7452 | - | - | - | - | | 0.0592 | 740 | 0.7459 | - | - | - | - | | 0.06 | 750 | 0.7419 | - | - | - | - | | 0.0608 | 760 | 0.7462 | - | - | - | - | | 0.0616 | 770 | 0.7414 | - | - | - | - | | 0.0624 | 780 | 0.7444 | - | - | - | - | | 0.0632 | 790 | 0.7419 | - | - | - | - | | 0.064 | 800 | 0.7438 | 0.7508 | 0.7273 | 0.7712 | 0.3957 | | 0.0648 | 810 | 0.7503 | - | - | - | - | | 0.0656 | 820 | 0.7402 | - | - | - | - | | 0.0664 | 830 | 0.7435 | - | - | - | - | | 0.0672 | 840 | 0.741 | - | - | - | - | | 0.068 | 850 | 0.7386 | - | - | - | - | | 0.0688 | 860 | 0.7416 | - | - | - | - | | 0.0696 | 870 | 0.7473 | - | - | - | - | | 0.0704 | 880 | 0.7438 | - | - | - | - | | 0.0712 | 890 | 0.7458 | - | - | - | - | | 0.072 | 900 | 0.7446 | 0.7494 | 0.7279 | 0.7718 | 0.3961 | | 0.0728 | 910 | 0.7483 | - | - | - | - | | 0.0736 | 920 | 0.7458 | - | - | - | - | | 0.0744 | 930 | 0.7473 | - | - | - | - | | 0.0752 | 940 | 0.7431 | - | - | - | - | | 0.076 | 950 | 0.7428 | - | - | - | - | | 0.0768 | 960 | 0.7385 | - | - | - | - | | 0.0776 | 970 | 0.7438 | - | - | - | - | | 0.0784 | 980 | 0.7406 | - | - | - | - | | 0.0792 | 990 | 0.7426 | - | - | - | - | | 0.08 | 1000 | 0.7372 | 0.7478 | 0.7282 | 0.7725 | 0.3965 | | 0.0808 | 1010 | 0.7396 | - | - | - | - | | 0.0816 | 1020 | 0.7398 | - | - | - | - | | 0.0824 | 1030 | 0.7376 | - | - | - | - | | 0.0832 | 1040 | 0.7417 | - | - | - | - | | 0.084 | 1050 | 0.7408 | - | - | - | - | | 0.0848 | 1060 | 0.7415 | - | - | - | - | | 0.0856 | 1070 | 0.7468 | - | - | - | - | | 0.0864 | 1080 | 0.7427 | - | - | - | - | | 0.0872 | 1090 | 0.7371 | - | - | - | - | | 0.088 | 1100 | 0.7375 | 0.7460 | 0.7279 | 0.7742 | 0.3970 | | 0.0888 | 1110 | 0.7434 | - | - | - | - | | 0.0896 | 1120 | 0.7441 | - | - | - | - | | 0.0904 | 1130 | 0.7378 | - | - | - | - | | 0.0912 | 1140 | 0.735 | - | - | - | - | | 0.092 | 1150 | 0.739 | - | - | - | - | | 0.0928 | 1160 | 0.7408 | - | - | - | - | | 0.0936 | 1170 | 0.7346 | - | - | - | - | | 0.0944 | 1180 | 0.7389 | - | - | - | - | | 0.0952 | 1190 | 0.7367 | - | - | - | - | | 0.096 | 1200 | 0.7358 | 0.7440 | 0.729 | 0.7747 | 0.3975 | | 0.0968 | 1210 | 0.7381 | - | - | - | - | | 0.0976 | 1220 | 0.7405 | - | - | - | - | | 0.0984 | 1230 | 0.7348 | - | - | - | - | | 0.0992 | 1240 | 0.737 | - | - | - | - | | 0.1 | 1250 | 0.7393 | - | - | - | - | | 0.1008 | 1260 | 0.7411 | - | - | - | - | | 0.1016 | 1270 | 0.7359 | - | - | - | - | | 0.1024 | 1280 | 0.7276 | - | - | - | - | | 0.1032 | 1290 | 0.7364 | - | - | - | - | | 0.104 | 1300 | 0.7333 | 0.7418 | 0.7293 | 0.7747 | 0.3979 | | 0.1048 | 1310 | 0.7367 | - | - | - | - | | 0.1056 | 1320 | 0.7352 | - | - | - | - | | 0.1064 | 1330 | 0.7333 | - | - | - | - | | 0.1072 | 1340 | 0.737 | - | - | - | - | | 0.108 | 1350 | 0.7361 | - | - | - | - | | 0.1088 | 1360 | 0.7299 | - | - | - | - | | 0.1096 | 1370 | 0.7339 | - | - | - | - | | 0.1104 | 1380 | 0.7349 | - | - | - | - | | 0.1112 | 1390 | 0.7318 | - | - | - | - | | 0.112 | 1400 | 0.7336 | 0.7394 | 0.7292 | 0.7749 | 0.3983 | | 0.1128 | 1410 | 0.7326 | - | - | - | - | | 0.1136 | 1420 | 0.7317 | - | - | - | - | | 0.1144 | 1430 | 0.7315 | - | - | - | - | | 0.1152 | 1440 | 0.7321 | - | - | - | - | | 0.116 | 1450 | 0.7284 | - | - | - | - | | 0.1168 | 1460 | 0.7308 | - | - | - | - | | 0.1176 | 1470 | 0.7287 | - | - | - | - | | 0.1184 | 1480 | 0.727 | - | - | - | - | | 0.1192 | 1490 | 0.7298 | - | - | - | - | | 0.12 | 1500 | 0.7306 | 0.7368 | 0.7301 | 0.7755 | 0.3988 | | 0.1208 | 1510 | 0.7269 | - | - | - | - | | 0.1216 | 1520 | 0.7299 | - | - | - | - | | 0.1224 | 1530 | 0.7256 | - | - | - | - | | 0.1232 | 1540 | 0.721 | - | - | - | - | | 0.124 | 1550 | 0.7274 | - | - | - | - | | 0.1248 | 1560 | 0.7251 | - | - | - | - | | 0.1256 | 1570 | 0.7248 | - | - | - | - | | 0.1264 | 1580 | 0.7244 | - | - | - | - | | 0.1272 | 1590 | 0.7275 | - | - | - | - | | 0.128 | 1600 | 0.7264 | 0.7339 | 0.7298 | 0.7756 | 0.3991 | | 0.1288 | 1610 | 0.7252 | - | - | - | - | | 0.1296 | 1620 | 0.7287 | - | - | - | - | | 0.1304 | 1630 | 0.7263 | - | - | - | - | | 0.1312 | 1640 | 0.7216 | - | - | - | - | | 0.132 | 1650 | 0.7231 | - | - | - | - | | 0.1328 | 1660 | 0.728 | - | - | - | - | | 0.1336 | 1670 | 0.7309 | - | - | - | - | | 0.1344 | 1680 | 0.7243 | - | - | - | - | | 0.1352 | 1690 | 0.7239 | - | - | - | - | | 0.136 | 1700 | 0.7219 | 0.7309 | 0.7302 | 0.7768 | 0.3994 | | 0.1368 | 1710 | 0.7212 | - | - | - | - | | 0.1376 | 1720 | 0.7217 | - | - | - | - | | 0.1384 | 1730 | 0.7118 | - | - | - | - | | 0.1392 | 1740 | 0.7226 | - | - | - | - | | 0.14 | 1750 | 0.7185 | - | - | - | - | | 0.1408 | 1760 | 0.7228 | - | - | - | - | | 0.1416 | 1770 | 0.7257 | - | - | - | - | | 0.1424 | 1780 | 0.7177 | - | - | - | - | | 0.1432 | 1790 | 0.722 | - | - | - | - | | 0.144 | 1800 | 0.712 | 0.7276 | 0.7307 | 0.7763 | 0.3997 | | 0.1448 | 1810 | 0.7193 | - | - | - | - | | 0.1456 | 1820 | 0.7138 | - | - | - | - | | 0.1464 | 1830 | 0.7171 | - | - | - | - | | 0.1472 | 1840 | 0.7191 | - | - | - | - | | 0.148 | 1850 | 0.7172 | - | - | - | - | | 0.1488 | 1860 | 0.7168 | - | - | - | - | | 0.1496 | 1870 | 0.7111 | - | - | - | - | | 0.1504 | 1880 | 0.7203 | - | - | - | - | | 0.1512 | 1890 | 0.7095 | - | - | - | - | | 0.152 | 1900 | 0.7064 | 0.7240 | 0.7301 | 0.7762 | 0.3998 | | 0.1528 | 1910 | 0.7147 | - | - | - | - | | 0.1536 | 1920 | 0.7098 | - | - | - | - | | 0.1544 | 1930 | 0.7193 | - | - | - | - | | 0.1552 | 1940 | 0.7096 | - | - | - | - | | 0.156 | 1950 | 0.7107 | - | - | - | - | | 0.1568 | 1960 | 0.7146 | - | - | - | - | | 0.1576 | 1970 | 0.7106 | - | - | - | - | | 0.1584 | 1980 | 0.7079 | - | - | - | - | | 0.1592 | 1990 | 0.7097 | - | - | - | - | | 0.16 | 2000 | 0.71 | 0.7202 | 0.7298 | 0.7758 | 0.3997 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.1 - Transformers: 4.38.2 - PyTorch: 2.1.2+cu121 - Accelerate: 0.27.2 - Datasets: 2.19.1 - Tokenizers: 0.15.2 ## 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", } ``` #### TripletLoss ```bibtex @misc{hermans2017defense, title={In Defense of the Triplet Loss for Person Re-Identification}, author={Alexander Hermans and Lucas Beyer and Bastian Leibe}, year={2017}, eprint={1703.07737}, archivePrefix={arXiv}, primaryClass={cs.CV} } ```