--- language: [] library_name: sentence-transformers tags: - sentence-transformers - sentence-similarity - feature-extraction - dataset_size:100K - **Maximum Sequence Length:** 8192 tokens - **Output Dimensionality:** 768 tokens - **Similarity Function:** Cosine Similarity ### 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("sentence_transformers_model_id") # Run inference sentences = [ 'search_query: 傘 鬼滅の刃', 'search_query: ノースフェイス リュック', 'search_query: お札 を 折ら ない ミニ 財布', ] 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 * Dataset: `triplet-esci` * Evaluated with [TripletEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator) | Metric | Value | |:--------------------|:----------| | **cosine_accuracy** | **0.655** | | dot_accuracy | 0.343 | | manhattan_accuracy | 0.657 | | euclidean_accuracy | 0.656 | | max_accuracy | 0.657 | ## Training Details ### Training Dataset #### Unnamed Dataset * Size: 100,000 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: college cactus backpack | search_document: Teecho Waterproof Cute Backpack for Girl Casual Print School Bag Women Laptop Backpack Cactus, Teecho, Cactus | search_document: JanSport Huntington Backpack - Lightweight Laptop Bag | Edo Floral, JanSport, Edo Floral | | search_query: yellow laces for sneakers | search_document: DELELE Solid Flat Shoelaces Hollow Thick Athletic Shoe Laces Strings Light Yellow 2 Pair 63", DELELE, 05 Light Yellow | search_document: Marrywindix 29 Pairs 47" Flat Colourful Athletic Shoe Laces for Sneakers Skate Shoes Boots Sport Shoes (29 Colors), Marrywindix, Colorful | | search_query: home sign grey | search_document: Bigtime Signs Home Sweet Home Sign - 11.75 inch x 9 inch Rigid PVC Signs Decor - Printed Rustic Wood LOOK - Predrilled Hole for Easy Hanging - Family Decoration for Home, Door, Mantle, Porch, Bigtime Signs, Home Sweet Home | search_document: Yankario Funny Bathroom Wall Decor Sign, Farmhouse Rustic Bathroom Decorations Wall Art , 12" by 6" Best Seat Wood Plaque, Yankario, grey 1 | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Evaluation Dataset #### Unnamed Dataset * Size: 1,000 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: black vinyl placemat | search_document: Red-A Dining Table Placemats Set of 4 Heat-Resistant Wipeable Table Mats for Kitchen Table Decoration Waterproof Vinyl Placemats Easy to Clean,Black w/Brown, Red-A, Black | search_document: Winknowl Placemats, Set of 8 Heat Resistant Stain Resistant Non-Slip Woven Vinyl Insulation Placemats, Washable Durable Elegant Table Mats for Dining (Black), Winknowl, Black | | search_query: 1 1/2 leather belts without buckle | search_document: Vatee's Women's/Men's Real Leather Replacement Belt Strap No Buckle 1 1/2"(38mm) Wide 45" Long Black, Vatee's, 154: Black | search_document: Women Skinny Leather Belt Thin Waist Jeans Belt for Pants in Pin Buckle Belt by WHIPPY, Black/Brown, Suit Pants 24-29 Inches, WHIPPY, 2-black+brown | | search_query: 1x cat 7a conector de red rj45 sin herramientas | search_document: deleyCON 3,0m RJ45 Cable Plano Cable de Red de Categoría CAT7 Cable Ethernet U/FTP con Revestimiento Interior de Cobre - Negro, deleyCON, Negro | search_document: Conector de Odedo®, 2 unidades, categoría 6, UTP RJ45, para cable de conexión, prolongación de 6,3 mm, AWG 23-26 montaje sin herramientas, contactos dorados, odedo, Weiß | * Loss: [TripletLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#tripletloss) with these parameters: ```json { "distance_metric": "TripletDistanceMetric.EUCLIDEAN", "triplet_margin": 5 } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `per_device_train_batch_size`: 4 - `per_device_eval_batch_size`: 4 - `learning_rate`: 1e-05 - `lr_scheduler_type`: cosine_with_restarts - `warmup_ratio`: 0.1 - `dataloader_drop_last`: True - `dataloader_num_workers`: 4 - `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`: 4 - `per_device_eval_batch_size`: 4 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `eval_accumulation_steps`: None - `learning_rate`: 1e-05 - `weight_decay`: 0.0 - `adam_beta1`: 0.9 - `adam_beta2`: 0.999 - `adam_epsilon`: 1e-08 - `max_grad_norm`: 1.0 - `num_train_epochs`: 3 - `max_steps`: -1 - `lr_scheduler_type`: cosine_with_restarts - `lr_scheduler_kwargs`: {} - `warmup_ratio`: 0.1 - `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`: None - `past_index`: -1 - `disable_tqdm`: False - `remove_unused_columns`: True - `label_names`: None - `load_best_model_at_end`: False - `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`: False - `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`: False - `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 | loss | triplet-esci_cosine_accuracy | |:------:|:-----:|:-------------:|:------:|:----------------------------:| | 0.008 | 200 | 3.9805 | - | - | | 0.016 | 400 | 4.0739 | - | - | | 0.024 | 600 | 4.0571 | - | - | | 0.032 | 800 | 3.8848 | - | - | | 0.04 | 1000 | 3.8249 | 3.9825 | 0.664 | | 0.048 | 1200 | 3.7097 | - | - | | 0.056 | 1400 | 3.6869 | - | - | | 0.064 | 1600 | 3.4327 | - | - | | 0.072 | 1800 | 3.64 | - | - | | 0.08 | 2000 | 3.3813 | 3.8244 | 0.657 | | 0.088 | 2200 | 3.4011 | - | - | | 0.096 | 2400 | 3.34 | - | - | | 0.104 | 2600 | 3.2488 | - | - | | 0.112 | 2800 | 3.5031 | - | - | | 0.12 | 3000 | 3.3615 | 3.7263 | 0.674 | | 0.128 | 3200 | 3.1028 | - | - | | 0.136 | 3400 | 3.2969 | - | - | | 0.144 | 3600 | 3.0463 | - | - | | 0.152 | 3800 | 3.1194 | - | - | | 0.16 | 4000 | 3.2372 | 3.6599 | 0.673 | | 0.168 | 4200 | 3.2954 | - | - | | 0.176 | 4400 | 3.2753 | - | - | | 0.184 | 4600 | 3.179 | - | - | | 0.192 | 4800 | 3.2646 | - | - | | 0.2 | 5000 | 3.1295 | 3.6405 | 0.677 | | 0.208 | 5200 | 3.2211 | - | - | | 0.216 | 5400 | 3.2222 | - | - | | 0.224 | 5600 | 2.9471 | - | - | | 0.232 | 5800 | 3.1564 | - | - | | 0.24 | 6000 | 3.1099 | 3.6138 | 0.684 | | 0.248 | 6200 | 2.9399 | - | - | | 0.256 | 6400 | 3.1087 | - | - | | 0.264 | 6600 | 3.2675 | - | - | | 0.272 | 6800 | 3.2149 | - | - | | 0.28 | 7000 | 2.9484 | 3.6086 | 0.673 | | 0.288 | 7200 | 3.0829 | - | - | | 0.296 | 7400 | 3.1864 | - | - | | 0.304 | 7600 | 3.1201 | - | - | | 0.312 | 7800 | 3.0698 | - | - | | 0.32 | 8000 | 2.9968 | 3.5750 | 0.668 | | 0.328 | 8200 | 3.0636 | - | - | | 0.336 | 8400 | 3.1293 | - | - | | 0.344 | 8600 | 3.1282 | - | - | | 0.352 | 8800 | 3.1415 | - | - | | 0.36 | 9000 | 2.7868 | 3.5211 | 0.693 | | 0.368 | 9200 | 2.9714 | - | - | | 0.376 | 9400 | 2.9409 | - | - | | 0.384 | 9600 | 2.9071 | - | - | | 0.392 | 9800 | 2.9154 | - | - | | 0.4 | 10000 | 2.9709 | 3.5510 | 0.683 | | 0.408 | 10200 | 2.741 | - | - | | 0.416 | 10400 | 2.678 | - | - | | 0.424 | 10600 | 2.8429 | - | - | | 0.432 | 10800 | 2.9782 | - | - | | 0.44 | 11000 | 2.9548 | 3.5369 | 0.681 | | 0.448 | 11200 | 2.8452 | - | - | | 0.456 | 11400 | 2.8578 | - | - | | 0.464 | 11600 | 2.9211 | - | - | | 0.472 | 11800 | 2.897 | - | - | | 0.48 | 12000 | 2.7651 | 3.6031 | 0.687 | | 0.488 | 12200 | 2.9472 | - | - | | 0.496 | 12400 | 2.6198 | - | - | | 0.504 | 12600 | 2.8444 | - | - | | 0.512 | 12800 | 2.7384 | - | - | | 0.52 | 13000 | 2.7827 | 3.6082 | 0.68 | | 0.528 | 13200 | 2.6882 | - | - | | 0.536 | 13400 | 2.6722 | - | - | | 0.544 | 13600 | 2.7218 | - | - | | 0.552 | 13800 | 2.7278 | - | - | | 0.56 | 14000 | 2.7156 | 3.5606 | 0.677 | | 0.568 | 14200 | 2.5748 | - | - | | 0.576 | 14400 | 2.5414 | - | - | | 0.584 | 14600 | 2.6918 | - | - | | 0.592 | 14800 | 2.5429 | - | - | | 0.6 | 15000 | 2.5724 | 3.5178 | 0.694 | | 0.608 | 15200 | 2.7594 | - | - | | 0.616 | 15400 | 2.4841 | - | - | | 0.624 | 15600 | 2.4667 | - | - | | 0.632 | 15800 | 2.6253 | - | - | | 0.64 | 16000 | 2.5001 | 3.5428 | 0.683 | | 0.648 | 16200 | 2.5707 | - | - | | 0.656 | 16400 | 2.4924 | - | - | | 0.664 | 16600 | 2.5419 | - | - | | 0.672 | 16800 | 2.487 | - | - | | 0.68 | 17000 | 2.4747 | 3.5825 | 0.681 | | 0.688 | 17200 | 2.4194 | - | - | | 0.696 | 17400 | 2.5714 | - | - | | 0.704 | 17600 | 2.4069 | - | - | | 0.712 | 17800 | 2.5767 | - | - | | 0.72 | 18000 | 2.5952 | 3.6268 | 0.684 | | 0.728 | 18200 | 2.4023 | - | - | | 0.736 | 18400 | 2.3989 | - | - | | 0.744 | 18600 | 2.379 | - | - | | 0.752 | 18800 | 2.4943 | - | - | | 0.76 | 19000 | 2.3762 | 3.5686 | 0.701 | | 0.768 | 19200 | 2.4825 | - | - | | 0.776 | 19400 | 2.4451 | - | - | | 0.784 | 19600 | 2.5374 | - | - | | 0.792 | 19800 | 2.4569 | - | - | | 0.8 | 20000 | 2.2353 | 3.6429 | 0.681 | | 0.808 | 20200 | 2.3447 | - | - | | 0.816 | 20400 | 2.3083 | - | - | | 0.824 | 20600 | 2.2126 | - | - | | 0.832 | 20800 | 2.3935 | - | - | | 0.84 | 21000 | 2.5115 | 3.6387 | 0.68 | | 0.848 | 21200 | 2.1469 | - | - | | 0.856 | 21400 | 2.2717 | - | - | | 0.864 | 21600 | 2.2993 | - | - | | 0.872 | 21800 | 2.3519 | - | - | | 0.88 | 22000 | 2.2947 | 3.5908 | 0.671 | | 0.888 | 22200 | 2.3249 | - | - | | 0.896 | 22400 | 2.2452 | - | - | | 0.904 | 22600 | 2.114 | - | - | | 0.912 | 22800 | 2.208 | - | - | | 0.92 | 23000 | 2.4168 | 3.6659 | 0.671 | | 0.928 | 23200 | 2.2098 | - | - | | 0.936 | 23400 | 2.1805 | - | - | | 0.944 | 23600 | 2.122 | - | - | | 0.952 | 23800 | 2.1364 | - | - | | 0.96 | 24000 | 2.1464 | 3.6284 | 0.671 | | 0.968 | 24200 | 2.1298 | - | - | | 0.976 | 24400 | 2.2657 | - | - | | 0.984 | 24600 | 2.304 | - | - | | 0.992 | 24800 | 2.175 | - | - | | 1.0 | 25000 | 2.1349 | 3.6532 | 0.681 | | 1.008 | 25200 | 2.0151 | - | - | | 1.016 | 25400 | 2.0881 | - | - | | 1.024 | 25600 | 1.9897 | - | - | | 1.032 | 25800 | 2.1987 | - | - | | 1.04 | 26000 | 1.9913 | 3.6511 | 0.672 | | 1.048 | 26200 | 1.9088 | - | - | | 1.056 | 26400 | 1.9555 | - | - | | 1.064 | 26600 | 1.6892 | - | - | | 1.072 | 26800 | 2.0404 | - | - | | 1.08 | 27000 | 1.6976 | 3.6465 | 0.671 | | 1.088 | 27200 | 1.894 | - | - | | 1.096 | 27400 | 1.8056 | - | - | | 1.104 | 27600 | 1.6426 | - | - | | 1.112 | 27800 | 2.0203 | - | - | | 1.12 | 28000 | 1.697 | 3.6182 | 0.681 | | 1.1280 | 28200 | 1.5562 | - | - | | 1.1360 | 28400 | 1.6061 | - | - | | 1.144 | 28600 | 1.5201 | - | - | | 1.152 | 28800 | 1.4388 | - | - | | 1.16 | 29000 | 1.5198 | 3.5924 | 0.676 | | 1.168 | 29200 | 1.6404 | - | - | | 1.176 | 29400 | 1.6058 | - | - | | 1.184 | 29600 | 1.6063 | - | - | | 1.192 | 29800 | 1.4398 | - | - | | 1.2 | 30000 | 1.4952 | 3.6587 | 0.665 | | 1.208 | 30200 | 1.5077 | - | - | | 1.216 | 30400 | 1.3177 | - | - | | 1.224 | 30600 | 1.095 | - | - | | 1.232 | 30800 | 1.2841 | - | - | | 1.24 | 31000 | 1.3544 | 3.6066 | 0.684 | | 1.248 | 31200 | 1.2188 | - | - | | 1.256 | 31400 | 1.1761 | - | - | | 1.264 | 31600 | 1.2601 | - | - | | 1.272 | 31800 | 1.2057 | - | - | | 1.28 | 32000 | 1.0478 | 3.6371 | 0.681 | | 1.288 | 32200 | 1.0888 | - | - | | 1.296 | 32400 | 1.1335 | - | - | | 1.304 | 32600 | 1.1297 | - | - | | 1.312 | 32800 | 1.0302 | - | - | | 1.32 | 33000 | 1.0583 | 3.6186 | 0.685 | | 1.328 | 33200 | 1.0623 | - | - | | 1.336 | 33400 | 0.9047 | - | - | | 1.3440 | 33600 | 1.0706 | - | - | | 1.3520 | 33800 | 1.0877 | - | - | | 1.3600 | 34000 | 0.8205 | 3.6613 | 0.653 | | 1.3680 | 34200 | 0.9596 | - | - | | 1.376 | 34400 | 0.8855 | - | - | | 1.384 | 34600 | 0.9186 | - | - | | 1.392 | 34800 | 0.8087 | - | - | | 1.4 | 35000 | 0.9732 | 3.6558 | 0.662 | | 1.408 | 35200 | 0.8753 | - | - | | 1.416 | 35400 | 0.8257 | - | - | | 1.424 | 35600 | 0.8689 | - | - | | 1.432 | 35800 | 0.8596 | - | - | | 1.44 | 36000 | 0.9202 | 3.6872 | 0.66 | | 1.448 | 36200 | 0.8993 | - | - | | 1.456 | 36400 | 0.8889 | - | - | | 1.464 | 36600 | 0.9138 | - | - | | 1.472 | 36800 | 0.8212 | - | - | | 1.48 | 37000 | 0.7591 | 3.6708 | 0.666 | | 1.488 | 37200 | 0.769 | - | - | | 1.496 | 37400 | 0.8656 | - | - | | 1.504 | 37600 | 0.9134 | - | - | | 1.512 | 37800 | 0.7212 | - | - | | 1.52 | 38000 | 0.8118 | 3.6249 | 0.672 | | 1.528 | 38200 | 0.7454 | - | - | | 1.536 | 38400 | 0.7491 | - | - | | 1.544 | 38600 | 0.8148 | - | - | | 1.552 | 38800 | 0.6845 | - | - | | 1.56 | 39000 | 0.6169 | 3.6868 | 0.679 | | 1.568 | 39200 | 0.7377 | - | - | | 1.576 | 39400 | 0.7296 | - | - | | 1.584 | 39600 | 0.7204 | - | - | | 1.592 | 39800 | 0.6748 | - | - | | 1.6 | 40000 | 0.6494 | 3.7054 | 0.673 | | 1.608 | 40200 | 0.7435 | - | - | | 1.616 | 40400 | 0.6196 | - | - | | 1.624 | 40600 | 0.6977 | - | - | | 1.6320 | 40800 | 0.7442 | - | - | | 1.6400 | 41000 | 0.5824 | 3.7500 | 0.66 | | 1.6480 | 41200 | 0.6144 | - | - | | 1.6560 | 41400 | 0.5909 | - | - | | 1.6640 | 41600 | 0.6717 | - | - | | 1.6720 | 41800 | 0.6436 | - | - | | 1.6800 | 42000 | 0.6161 | 3.6769 | 0.676 | | 1.688 | 42200 | 0.5282 | - | - | | 1.696 | 42400 | 0.6647 | - | - | | 1.704 | 42600 | 0.649 | - | - | | 1.712 | 42800 | 0.6284 | - | - | | 1.72 | 43000 | 0.7055 | 3.6992 | 0.671 | | 1.728 | 43200 | 0.6249 | - | - | | 1.736 | 43400 | 0.5722 | - | - | | 1.744 | 43600 | 0.621 | - | - | | 1.752 | 43800 | 0.6129 | - | - | | 1.76 | 44000 | 0.501 | 3.7589 | 0.662 | | 1.768 | 44200 | 0.5566 | - | - | | 1.776 | 44400 | 0.576 | - | - | | 1.784 | 44600 | 0.6428 | - | - | | 1.792 | 44800 | 0.5629 | - | - | | 1.8 | 45000 | 0.5134 | 3.7530 | 0.659 | | 1.808 | 45200 | 0.4681 | - | - | | 1.8160 | 45400 | 0.6268 | - | - | | 1.8240 | 45600 | 0.5877 | - | - | | 1.8320 | 45800 | 0.5219 | - | - | | 1.8400 | 46000 | 0.545 | 3.7755 | 0.658 | | 1.8480 | 46200 | 0.4539 | - | - | | 1.8560 | 46400 | 0.5255 | - | - | | 1.8640 | 46600 | 0.5573 | - | - | | 1.8720 | 46800 | 0.5508 | - | - | | 1.88 | 47000 | 0.5391 | 3.7489 | 0.653 | | 1.888 | 47200 | 0.4276 | - | - | | 1.896 | 47400 | 0.4906 | - | - | | 1.904 | 47600 | 0.3771 | - | - | | 1.912 | 47800 | 0.4959 | - | - | | 1.92 | 48000 | 0.5377 | 3.7770 | 0.658 | | 1.928 | 48200 | 0.4807 | - | - | | 1.936 | 48400 | 0.5239 | - | - | | 1.944 | 48600 | 0.4441 | - | - | | 1.952 | 48800 | 0.4536 | - | - | | 1.96 | 49000 | 0.5265 | 3.7507 | 0.669 | | 1.968 | 49200 | 0.3817 | - | - | | 1.976 | 49400 | 0.4468 | - | - | | 1.984 | 49600 | 0.5766 | - | - | | 1.992 | 49800 | 0.4789 | - | - | | 2.0 | 50000 | 0.4853 | 3.7328 | 0.663 | | 2.008 | 50200 | 0.3744 | - | - | | 2.016 | 50400 | 0.4662 | - | - | | 2.024 | 50600 | 0.394 | - | - | | 2.032 | 50800 | 0.3938 | - | - | | 2.04 | 51000 | 0.3586 | 3.8004 | 0.656 | | 2.048 | 51200 | 0.3522 | - | - | | 2.056 | 51400 | 0.4173 | - | - | | 2.064 | 51600 | 0.3177 | - | - | | 2.072 | 51800 | 0.4113 | - | - | | 2.08 | 52000 | 0.3027 | 3.7366 | 0.665 | | 2.088 | 52200 | 0.3693 | - | - | | 2.096 | 52400 | 0.4268 | - | - | | 2.104 | 52600 | 0.3678 | - | - | | 2.112 | 52800 | 0.4192 | - | - | | 2.12 | 53000 | 0.3105 | 3.7831 | 0.661 | | 2.128 | 53200 | 0.3228 | - | - | | 2.136 | 53400 | 0.2408 | - | - | | 2.144 | 53600 | 0.2804 | - | - | | 2.152 | 53800 | 0.3034 | - | - | | 2.16 | 54000 | 0.3562 | 3.7866 | 0.656 | | 2.168 | 54200 | 0.3526 | - | - | | 2.176 | 54400 | 0.414 | - | - | | 2.184 | 54600 | 0.3678 | - | - | | 2.192 | 54800 | 0.2965 | - | - | | 2.2 | 55000 | 0.3691 | 3.8108 | 0.655 |
### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.0.0 - 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} } ```