--- language: - en license: apache-2.0 tags: - sentence-transformers - sentence-similarity - feature-extraction - dense - generated_from_trainer - dataset_size:1200 - loss:ContrastiveLoss base_model: google/embeddinggemma-300m widget: - source_sentence: 'TaxYear: 2025 PRELIMINARY LandJustValue: $571,965 ImprovementsJustValue: $444,893 TotalJustValue: $1,016,858 SchoolAssessedValue: $657,902 CountyTaxableValue: $607,180 TotalTaxes: $5,881.02 TaxYear: 2024 LandJustValue: $529,037 ImprovementsJustValue: $522,202 TotalJustValue: $1,051,239 SchoolAssessedValue: $639,361 CountyTaxableValue: $589,361 TotalTaxes: $6,003.53 TaxYear: 2023 LandJustValue: $500,470 ImprovementsJustValue: $572,889 TotalJustValue: $1,073,359 SchoolAssessedValue: $620,739 CountyTaxableValue: $570,739 TotalTaxes: $5,956.52 TaxYear: 2022 LandJustValue: $230,519 ImprovementsJustValue: $610,503 TotalJustValue: $841,022 SchoolAssessedValue: $602,659 CountyTaxableValue: $552,659 TotalTaxes: $6,124.86 TaxYear: 2021 LandJustValue: $112,658 ImprovementsJustValue: $472,448 TotalJustValue: $585,106 SchoolAssessedValue: $585,106 CountyTaxableValue: $535,106 TotalTaxes: $6,190.98section: Tax, county: Collier, parcel_id: 82660002628' sentences: - 'area_under_air: 2111 livable_floor_area: 2111 parcel_identifier: 51978031927 property_structure_built_year: 2004 property_type: SingleFamily subdivision: INDIGO LAKES UNIT total_area: 2551' - 'monthly_tax_amount: 490.09 period_end_date: 2025-12-31 period_start_date: 2025-01-01 property_assessed_value_amount: 657902 property_building_amount: 444893 property_land_amount: 571965 property_market_value_amount: 1016858 property_taxable_value_amount: 607180 tax_year: 2025 yearly_tax_amount: 5881.02 monthly_tax_amount: 510.41 period_end_date: 2022-12-31 period_start_date: 2022-01-01 property_assessed_value_amount: 602659 property_building_amount: 610503 property_land_amount: 230519 property_market_value_amount: 841022 property_taxable_value_amount: 552659 tax_year: 2022 yearly_tax_amount: 6124.86' - 'ownership_transfer_date: 2013-07-09 purchase_price_amount: 830000 ownership_transfer_date: 2011-10-03 purchase_price_amount: 685000 ownership_transfer_date: 2009-07-01 purchase_price_amount: 432500 ownership_transfer_date: 1999-02-22 purchase_price_amount: 0 ownership_transfer_date: 2001-01-25 purchase_price_amount: 360000' - source_sentence: 'TaxYear: 2025 PRELIMINARY LandJustValue: $0 ImprovementsJustValue: $261,720 TotalJustValue: $261,720 SchoolAssessedValue: $261,720 CountyTaxableValue: $261,720 TotalTaxes: $3,142.17 TaxYear: 2024 LandJustValue: $0 ImprovementsJustValue: $261,720 TotalJustValue: $261,720 SchoolAssessedValue: $261,720 CountyTaxableValue: $261,720 TotalTaxes: $3,551.55 TaxYear: 2023 LandJustValue: $0 ImprovementsJustValue: $298,680 TotalJustValue: $298,680 SchoolAssessedValue: $298,680 CountyTaxableValue: $298,680 TotalTaxes: $4,125.27 TaxYear: 2022 LandJustValue: $0 ImprovementsJustValue: $233,985 TotalJustValue: $233,985 SchoolAssessedValue: $233,985 CountyTaxableValue: $172,700 TotalTaxes: $2,771.07 TaxYear: 2021 LandJustValue: $0 ImprovementsJustValue: $157,000 TotalJustValue: $157,000 SchoolAssessedValue: $157,000 CountyTaxableValue: $157,000 TotalTaxes: $2,342.18section: Tax, county: Collier, parcel_id: 31760000209' sentences: - 'first_name: George last_name: Lewis middle_name: P first_name: Karen last_name: Lewis middle_name: L' - 'area_under_air: 997 livable_floor_area: 997 parcel_identifier: 31731720000 property_legal_description_text: FAIRWAY FOREST GARDEN VILLAS A CONDOMINIUM UNIT 179 property_structure_built_year: 1987 property_type: Condominium total_area: 997' - 'monthly_tax_amount: 195.18 period_end_date: 2021-12-31 period_start_date: 2021-01-01 property_assessed_value_amount: 157000 property_building_amount: 157000 property_land_amount: 0 property_market_value_amount: 157000 property_taxable_value_amount: 157000 tax_year: 2021 yearly_tax_amount: 2342.18 monthly_tax_amount: 261.85 period_end_date: 2025-12-31 period_start_date: 2025-01-01 property_assessed_value_amount: 261720 property_building_amount: 261720 property_land_amount: 0 property_market_value_amount: 261720 property_taxable_value_amount: 261720 tax_year: 2025 yearly_tax_amount: 3142.17 monthly_tax_amount: 295.96 period_end_date: 2024-12-31 period_start_date: 2024-01-01 property_assessed_value_amount: 261720 property_building_amount: 261720 property_land_amount: 0 property_market_value_amount: 261720 property_taxable_value_amount: 261720 tax_year: 2024 yearly_tax_amount: 3551.55 monthly_tax_amount: 230.92 period_end_date: 2022-12-31 period_start_date: 2022-01-01 property_assessed_value_amount: 233985 property_building_amount: 233985 property_land_amount: 0 property_market_value_amount: 233985 property_taxable_value_amount: 172700 tax_year: 2022 yearly_tax_amount: 2771.07 monthly_tax_amount: 343.77 period_end_date: 2023-12-31 period_start_date: 2023-01-01 property_assessed_value_amount: 298680 property_building_amount: 298680 property_land_amount: 0 property_market_value_amount: 298680 property_taxable_value_amount: 298680 tax_year: 2023 yearly_tax_amount: 4125.27' - source_sentence: 'ParcelID: 31347702043 FullAddress: 9424 MONTELANICO LOOP, NAPLES 34119 Legal: ESPLANADE GOLF AND COUNTRY CLUB OF NAPLES PHASE 3 BLOCKS K1 K2 AND H3 LOT 1390 Subdivision: 281740 - ESPLANADE G&CC PH3 B-K1,K2,H3 CLUB OF NAPLES PHASE 3 BLOCKS K1 K2 AND H3 UseCode: 1 - SINGLE FAMILY RESIDENTIAL Section: 15 Township: 48 Range: 26section: Property, county: Collier, parcel_id: 31347702043' sentences: - 'monthly_tax_amount: 1296.8 period_end_date: 2023-12-31 period_start_date: 2023-01-01 property_assessed_value_amount: 1452003 property_building_amount: 1459158 property_land_amount: 1594430 property_market_value_amount: 3053588 property_taxable_value_amount: 1402003 tax_year: 2023 yearly_tax_amount: 15561.55 monthly_tax_amount: 1339.02 period_end_date: 2021-12-31 period_start_date: 2021-01-01 property_assessed_value_amount: 1368652 property_building_amount: 1188323 property_land_amount: 180329 property_market_value_amount: 1368652 property_taxable_value_amount: 1318652 tax_year: 2021 yearly_tax_amount: 16068.19 monthly_tax_amount: 1315.87 period_end_date: 2024-12-31 period_start_date: 2024-01-01 property_assessed_value_amount: 1495563 property_building_amount: 1262216 property_land_amount: 1402668 property_market_value_amount: 2664884 property_taxable_value_amount: 1445563 tax_year: 2024 yearly_tax_amount: 15790.39 monthly_tax_amount: 1187.99 period_end_date: 2025-12-31 period_start_date: 2025-01-01 property_assessed_value_amount: 1538934 property_building_amount: 1117620 property_land_amount: 1508245 property_market_value_amount: 2625865 property_taxable_value_amount: 1488212 tax_year: 2025 yearly_tax_amount: 14255.93 monthly_tax_amount: 1334.85 period_end_date: 2022-12-31 period_start_date: 2022-01-01 property_assessed_value_amount: 1409712 property_building_amount: 1553410 property_land_amount: 470644 property_market_value_amount: 2024054 property_taxable_value_amount: 1359712 tax_year: 2022 yearly_tax_amount: 16018.16' - 'area_under_air: 2313 livable_floor_area: 2313 parcel_identifier: 31347702043 property_legal_description_text: ESPLANADE GOLF AND COUNTRY CLUB OF NAPLES PHASE 3 BLOCKS K1 K2 AND H3 LOT 1390 property_structure_built_year: 2018 property_type: SingleFamily subdivision: ESPLANADE G&CC PH3 B-K1,K2,H3 CLUB OF NAPLES PHASE 3 BLOCKS K1 K2 AND H3 total_area: 2767' - 'city_name: NAPLES county_name: Collier postal_code: 34105 range: 25 section: 14 state_code: FL street_name: WOODSHIRE street_number: 1018 street_suffix_type: Ln township: 49' - source_sentence: 'OwnerLine 1: 21 VB PROPERTIES LLCsection: Owners, county: Collier, parcel_id: 23270120001' sentences: - 'first_name: Kenneth last_name: Holman middle_name: W' - 'city_name: NAPLES county_name: Collier state_code: FL street_name: WILLOWBROOK street_number: 765 street_suffix_type: Dr township: 49' - 'name: 21' - source_sentence: 'FullAddress: 5852 NORTHRIDGE DR, NAPLES 34110 Legal: CARLTON LAKES UNIT NO 2 BLK A LOT 5 NKA VILLAS I AT CARLTON LAKES (HO) UNIT A-5 Section: 19 Township: 48 Range: 26section: Address, county: Collier, parcel_id: 25540003380' sentences: - 'monthly_tax_amount: 317.4 period_end_date: 2022-12-31 period_start_date: 2022-01-01 property_assessed_value_amount: 381299 property_building_amount: 441115 property_land_amount: 134469 property_market_value_amount: 575584 property_taxable_value_amount: 331299 tax_year: 2022 yearly_tax_amount: 3808.76 monthly_tax_amount: 517.39 period_end_date: 2025-12-31 period_start_date: 2025-01-01 property_assessed_value_amount: 692367 property_building_amount: 324162 property_land_amount: 368205 property_market_value_amount: 692367 property_taxable_value_amount: 641645 tax_year: 2025 yearly_tax_amount: 6208.64 monthly_tax_amount: 320.37 period_end_date: 2021-12-31 period_start_date: 2021-01-01 property_assessed_value_amount: 370193 property_building_amount: 334803 property_land_amount: 35390 property_market_value_amount: 370193 property_taxable_value_amount: 320193 tax_year: 2021 yearly_tax_amount: 3844.46' - 'first_name: Christina last_name: Zajac middle_name: R first_name: Thomas last_name: Zajac middle_name: H' - 'city_name: NAPLES county_name: Collier lot: 5 postal_code: 34110 range: 26 section: 19 state_code: FL street_name: NORTHRIDGE street_number: 5852 street_suffix_type: Dr township: 48' pipeline_tag: sentence-similarity library_name: sentence-transformers metrics: - cosine_accuracy - cosine_accuracy_threshold - cosine_f1 - cosine_f1_threshold - cosine_precision - cosine_recall - cosine_ap - cosine_mcc model-index: - name: EmbeddingGemma-300m trained to measure coverage results: - task: type: binary-classification name: Binary Classification dataset: name: Unknown type: unknown metrics: - type: cosine_accuracy value: 0.96 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.9879488945007324 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9607843137254902 name: Cosine F1 - type: cosine_f1_threshold value: 0.98133385181427 name: Cosine F1 Threshold - type: cosine_precision value: 0.9423076923076923 name: Cosine Precision - type: cosine_recall value: 0.98 name: Cosine Recall - type: cosine_ap value: 0.9530095295398296 name: Cosine Ap - type: cosine_mcc value: 0.920736884379251 name: Cosine Mcc --- # EmbeddingGemma-300m trained to measure coverage This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) on the json 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 - **Base model:** [google/embeddinggemma-300m](https://huggingface.co/google/embeddinggemma-300m) - **Maximum Sequence Length:** 2048 tokens - **Output Dimensionality:** 768 dimensions - **Similarity Function:** Cosine Similarity - **Training Dataset:** - json - **Language:** en - **License:** apache-2.0 ### 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({'max_seq_length': 2048, 'do_lower_case': False, 'architecture': 'Gemma3TextModel'}) (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}) (2): Dense({'in_features': 768, 'out_features': 3072, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (3): Dense({'in_features': 3072, 'out_features': 768, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'}) (4): 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("mancer146/embeddinggemma-300m-haystack-contrastive-thin-fixed") # Run inference queries = [ "FullAddress: 5852 NORTHRIDGE DR, NAPLES 34110\nLegal: CARLTON LAKES UNIT NO 2 BLK A LOT 5 NKA VILLAS I AT CARLTON LAKES (HO) UNIT A-5\nSection: 19\nTownship: 48\nRange: 26section: Address,\ncounty: Collier,\nparcel_id: 25540003380", ] documents = [ 'city_name: NAPLES\ncounty_name: Collier\nlot: 5\npostal_code: 34110\nrange: 26\nsection: 19\nstate_code: FL\nstreet_name: NORTHRIDGE\nstreet_number: 5852\nstreet_suffix_type: Dr\ntownship: 48', 'monthly_tax_amount: 317.4\nperiod_end_date: 2022-12-31\nperiod_start_date: 2022-01-01\nproperty_assessed_value_amount: 381299\nproperty_building_amount: 441115\nproperty_land_amount: 134469\nproperty_market_value_amount: 575584\nproperty_taxable_value_amount: 331299\ntax_year: 2022\nyearly_tax_amount: 3808.76\n\nmonthly_tax_amount: 517.39\nperiod_end_date: 2025-12-31\nperiod_start_date: 2025-01-01\nproperty_assessed_value_amount: 692367\nproperty_building_amount: 324162\nproperty_land_amount: 368205\nproperty_market_value_amount: 692367\nproperty_taxable_value_amount: 641645\ntax_year: 2025\nyearly_tax_amount: 6208.64\n\nmonthly_tax_amount: 320.37\nperiod_end_date: 2021-12-31\nperiod_start_date: 2021-01-01\nproperty_assessed_value_amount: 370193\nproperty_building_amount: 334803\nproperty_land_amount: 35390\nproperty_market_value_amount: 370193\nproperty_taxable_value_amount: 320193\ntax_year: 2021\nyearly_tax_amount: 3844.46', 'first_name: Christina\nlast_name: Zajac\nmiddle_name: R\n\nfirst_name: Thomas\nlast_name: Zajac\nmiddle_name: H', ] query_embeddings = model.encode_query(queries) document_embeddings = model.encode_document(documents) print(query_embeddings.shape, document_embeddings.shape) # [1, 768] [3, 768] # Get the similarity scores for the embeddings similarities = model.similarity(query_embeddings, document_embeddings) print(similarities) # tensor([[0.9881, 0.8106, 0.6785]]) ``` ## Evaluation ### Metrics #### Binary Classification * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:--------------------------|:----------| | cosine_accuracy | 0.96 | | cosine_accuracy_threshold | 0.9879 | | cosine_f1 | 0.9608 | | cosine_f1_threshold | 0.9813 | | cosine_precision | 0.9423 | | cosine_recall | 0.98 | | **cosine_ap** | **0.953** | | cosine_mcc | 0.9207 | ## Training Details ### Training Dataset #### json * Dataset: json * Size: 1,200 training samples * Columns: input_text, output_text, and label * Approximate statistics based on the first 1000 samples: | | input_text | output_text | label | |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------| | type | string | string | int | | details | | | | * Samples: | input_text | output_text | label | |:-----------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------|:---------------| | OwnerLine 1: JERI HURCKES LIVING TRUSTsection: Owners,
county: Collier,
parcel_id: 82660021104 | name: JERI HURCKES LIVING TRUST | 1 | | OwnerLine 1: GUALARIO, ANTHONY=& DIANAsection: Owners,
county: Collier,
parcel_id: 16054320005 | first_name: Anthony
last_name: Gualario | 0 | | Date: 02/11/14
Amount: $496,300
BookPage: 5009-963section: Sales,
county: Collier,
parcel_id: 69770005923 | ownership_transfer_date: 2014-02-11
purchase_price_amount: 0 | 0 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.1, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 3 - `per_device_eval_batch_size`: 3 - `gradient_accumulation_steps`: 2 - `learning_rate`: 2e-05 - `num_train_epochs`: 5 - `warmup_ratio`: 0.05 - `fp16`: True - `prompts`: {'input_text': 'STS', 'output_text': 'STS'} #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 3 - `per_device_eval_batch_size`: 3 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 2 - `eval_accumulation_steps`: None - `torch_empty_cache_steps`: None - `learning_rate`: 2e-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`: 5 - `max_steps`: -1 - `lr_scheduler_type`: linear - `lr_scheduler_kwargs`: {} - `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 - `restore_callback_states_from_checkpoint`: 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`: True - `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`: False - `dataloader_num_workers`: 0 - `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, 'non_blocking': False, 'gradient_accumulation_kwargs': None} - `parallelism_config`: None - `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`: None - `hub_always_push`: False - `hub_revision`: None - `gradient_checkpointing`: False - `gradient_checkpointing_kwargs`: None - `include_inputs_for_metrics`: False - `include_for_metrics`: [] - `eval_do_concat_batches`: True - `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 - `include_tokens_per_second`: False - `include_num_input_tokens_seen`: False - `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`: False - `prompts`: {'input_text': 'STS', 'output_text': 'STS'} - `batch_sampler`: batch_sampler - `multi_dataset_batch_sampler`: proportional - `router_mapping`: {} - `learning_rate_mapping`: {}
### Training Logs | Epoch | Step | Training Loss | cosine_ap | |:-----:|:----:|:-------------:|:---------:| | 0.4 | 40 | - | 0.8426 | | 0.8 | 80 | - | 0.8858 | | 1.2 | 120 | - | 0.8194 | | 1.6 | 160 | - | 0.8856 | | 2.0 | 200 | - | 0.9643 | | 2.4 | 240 | - | 0.9469 | | 2.8 | 280 | - | 0.9426 | | 3.2 | 320 | - | 0.9084 | | 3.6 | 360 | - | 0.9337 | | 4.0 | 400 | - | 0.9449 | | 4.4 | 440 | - | 0.9555 | | 4.8 | 480 | - | 0.9525 | | 5.0 | 500 | 0.0006 | - | | -1 | -1 | - | 0.9530 | ### Framework Versions - Python: 3.11.13 - Sentence Transformers: 5.1.2 - Transformers: 4.57.0.dev0 - PyTorch: 2.6.0+cu124 - Accelerate: 1.9.0 - Datasets: 4.1.1 - Tokenizers: 0.22.1 ## 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", } ``` #### ContrastiveLoss ```bibtex @inproceedings{hadsell2006dimensionality, author={Hadsell, R. and Chopra, S. and LeCun, Y.}, booktitle={2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)}, title={Dimensionality Reduction by Learning an Invariant Mapping}, year={2006}, volume={2}, number={}, pages={1735-1742}, doi={10.1109/CVPR.2006.100} } ```