--- language: - en tags: - sentence-transformers - sentence-similarity - feature-extraction - generated_from_trainer - dataset_size:4517388 - loss:ContrastiveLoss base_model: sentence-transformers/all-MiniLM-L6-v2 widget: - source_sentence: 640 prt ashley floor 10 chula vista california 91913 sentences: - 10523 howard parks apartment 8 cockseysville md 21030 - 640 prt ashley floor 10 East Gregory PW 91913 - trailwoods radial loveland oh 4514 - source_sentence: 9036 taylorsville road louisville ky 40299-1750 sentences: - '16331 northwest gearin junctn floor num 6 apt # 4 f tigard or 97223-2808' - 19 Brian Key walk voorhees township n. j. 08026 - 9036 taylorsville boulevard louisville 40299-175 - source_sentence: 11 simek ln middletown township n j 07758 sentences: - 248 strawberry meadows place apt 1 springdale 72764-3759 - 11 Daniel Drive knl middletown township MT 41761 - 1135 s westgate ave Mileshaven ca 90049 - source_sentence: so west prospect street aloha or 97078 sentences: - '1300 Brittney Club plains lot # b new york cty NY 10459' - 527 Nicole Springs bypas rupert CA 05776 - so wdest prospect street aloha 97078 - source_sentence: 8234 harvest bend lane laurel md 20707 sentences: - 8234 harvest bend lane laurel md - 8702 wahl crse basement santee ca 92071 - 310 ella street Jamesborough ne 68310 datasets: - jarredparrett/deepparse_address_mutations_comb_3 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 - dot_accuracy - dot_accuracy_threshold - dot_f1 - dot_f1_threshold - dot_precision - dot_recall - dot_ap - manhattan_accuracy - manhattan_accuracy_threshold - manhattan_f1 - manhattan_f1_threshold - manhattan_precision - manhattan_recall - manhattan_ap - euclidean_accuracy - euclidean_accuracy_threshold - euclidean_f1 - euclidean_f1_threshold - euclidean_precision - euclidean_recall - euclidean_ap - max_accuracy - max_accuracy_threshold - max_f1 - max_f1_threshold - max_precision - max_recall - max_ap model-index: - name: SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 results: - task: type: binary-classification name: Binary Classification dataset: name: jarredparrett/deepparse address mutations comb 3 type: jarredparrett/deepparse_address_mutations_comb_3 metrics: - type: cosine_accuracy value: 0.9770643339132159 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7712496519088745 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9784053285401372 name: Cosine F1 - type: cosine_f1_threshold value: 0.7712496519088745 name: Cosine F1 Threshold - type: cosine_precision value: 0.960100255219399 name: Cosine Precision - type: cosine_recall value: 0.9974219699718995 name: Cosine Recall - type: cosine_ap value: 0.9864940067102314 name: Cosine Ap - type: dot_accuracy value: 0.9770643339132159 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.7712496519088745 name: Dot Accuracy Threshold - type: dot_f1 value: 0.9784053285401372 name: Dot F1 - type: dot_f1_threshold value: 0.7712496519088745 name: Dot F1 Threshold - type: dot_precision value: 0.960100255219399 name: Dot Precision - type: dot_recall value: 0.9974219699718995 name: Dot Recall - type: dot_ap value: 0.986499063941509 name: Dot Ap - type: manhattan_accuracy value: 0.9770395408321384 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 10.601512908935547 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.978383036334317 name: Manhattan F1 - type: manhattan_f1_threshold value: 10.611783027648926 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.9600334406666756 name: Manhattan Precision - type: manhattan_recall value: 0.9974477502721805 name: Manhattan Recall - type: manhattan_ap value: 0.9865423177462433 name: Manhattan Ap - type: euclidean_accuracy value: 0.9770643339132159 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.6763879060745239 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.9784053285401372 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.6763879060745239 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.960100255219399 name: Euclidean Precision - type: euclidean_recall value: 0.9974219699718995 name: Euclidean Recall - type: euclidean_ap value: 0.9865515796011742 name: Euclidean Ap - type: max_accuracy value: 0.9770643339132159 name: Max Accuracy - type: max_accuracy_threshold value: 10.601512908935547 name: Max Accuracy Threshold - type: max_f1 value: 0.9784053285401372 name: Max F1 - type: max_f1_threshold value: 10.611783027648926 name: Max F1 Threshold - type: max_precision value: 0.960100255219399 name: Max Precision - type: max_recall value: 0.9974477502721805 name: Max Recall - type: max_ap value: 0.9865515796011742 name: Max Ap - type: cosine_accuracy value: 0.9770612347780813 name: Cosine Accuracy - type: cosine_accuracy_threshold value: 0.7710819244384766 name: Cosine Accuracy Threshold - type: cosine_f1 value: 0.9783854448042815 name: Cosine F1 - type: cosine_f1_threshold value: 0.7710819244384766 name: Cosine F1 Threshold - type: cosine_precision value: 0.9600473761629129 name: Cosine Precision - type: cosine_recall value: 0.9974377142267394 name: Cosine Recall - type: cosine_ap value: 0.9865423807819248 name: Cosine Ap - type: dot_accuracy value: 0.9770612347780813 name: Dot Accuracy - type: dot_accuracy_threshold value: 0.7710819244384766 name: Dot Accuracy Threshold - type: dot_f1 value: 0.9783854448042815 name: Dot F1 - type: dot_f1_threshold value: 0.7710819244384766 name: Dot F1 Threshold - type: dot_precision value: 0.9600473761629129 name: Dot Precision - type: dot_recall value: 0.9974377142267394 name: Dot Recall - type: dot_ap value: 0.9865613743522202 name: Dot Ap - type: manhattan_accuracy value: 0.9770395408321384 name: Manhattan Accuracy - type: manhattan_accuracy_threshold value: 10.510114669799805 name: Manhattan Accuracy Threshold - type: manhattan_f1 value: 0.9783637843035726 name: Manhattan F1 - type: manhattan_f1_threshold value: 10.637184143066406 name: Manhattan F1 Threshold - type: manhattan_precision value: 0.9599119169895931 name: Manhattan Precision - type: manhattan_recall value: 0.9975389354307954 name: Manhattan Recall - type: manhattan_ap value: 0.9865931109650937 name: Manhattan Ap - type: euclidean_accuracy value: 0.9770612347780813 name: Euclidean Accuracy - type: euclidean_accuracy_threshold value: 0.6766358613967896 name: Euclidean Accuracy Threshold - type: euclidean_f1 value: 0.9783854448042815 name: Euclidean F1 - type: euclidean_f1_threshold value: 0.6766358613967896 name: Euclidean F1 Threshold - type: euclidean_precision value: 0.9600473761629129 name: Euclidean Precision - type: euclidean_recall value: 0.9974377142267394 name: Euclidean Recall - type: euclidean_ap value: 0.9866061739963429 name: Euclidean Ap - type: max_accuracy value: 0.9770612347780813 name: Max Accuracy - type: max_accuracy_threshold value: 10.510114669799805 name: Max Accuracy Threshold - type: max_f1 value: 0.9783854448042815 name: Max F1 - type: max_f1_threshold value: 10.637184143066406 name: Max F1 Threshold - type: max_precision value: 0.9600473761629129 name: Max Precision - type: max_recall value: 0.9975389354307954 name: Max Recall - type: max_ap value: 0.9866061739963429 name: Max Ap --- # SentenceTransformer based on sentence-transformers/all-MiniLM-L6-v2 This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) on the [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) dataset. It maps sentences & paragraphs to a 384-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:** [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - **Maximum Sequence Length:** 256 tokens - **Output Dimensionality:** 384 tokens - **Similarity Function:** Cosine Similarity - **Training Dataset:** - [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) - **Language:** en ### 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': 256, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 384, '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): 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("jarredparrett/all-MiniLM-L6-v2_tuned_on_deepparse_address_mutations_comb_3") # Run inference sentences = [ '8234 harvest bend lane laurel md 20707', '8234 harvest bend lane laurel md', '8702 wahl crse basement santee ca 92071', ] embeddings = model.encode(sentences) print(embeddings.shape) # [3, 384] # Get the similarity scores for the embeddings similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] ``` ## Evaluation ### Metrics #### Binary Classification * Dataset: `jarredparrett/deepparse_address_mutations_comb_3` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.9771 | | cosine_accuracy_threshold | 0.7712 | | cosine_f1 | 0.9784 | | cosine_f1_threshold | 0.7712 | | cosine_precision | 0.9601 | | cosine_recall | 0.9974 | | cosine_ap | 0.9865 | | dot_accuracy | 0.9771 | | dot_accuracy_threshold | 0.7712 | | dot_f1 | 0.9784 | | dot_f1_threshold | 0.7712 | | dot_precision | 0.9601 | | dot_recall | 0.9974 | | dot_ap | 0.9865 | | manhattan_accuracy | 0.977 | | manhattan_accuracy_threshold | 10.6015 | | manhattan_f1 | 0.9784 | | manhattan_f1_threshold | 10.6118 | | manhattan_precision | 0.96 | | manhattan_recall | 0.9974 | | manhattan_ap | 0.9865 | | euclidean_accuracy | 0.9771 | | euclidean_accuracy_threshold | 0.6764 | | euclidean_f1 | 0.9784 | | euclidean_f1_threshold | 0.6764 | | euclidean_precision | 0.9601 | | euclidean_recall | 0.9974 | | euclidean_ap | 0.9866 | | max_accuracy | 0.9771 | | max_accuracy_threshold | 10.6015 | | max_f1 | 0.9784 | | max_f1_threshold | 10.6118 | | max_precision | 0.9601 | | max_recall | 0.9974 | | **max_ap** | **0.9866** | #### Binary Classification * Dataset: `jarredparrett/deepparse_address_mutations_comb_3` * Evaluated with [BinaryClassificationEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator) | Metric | Value | |:-----------------------------|:-----------| | cosine_accuracy | 0.9771 | | cosine_accuracy_threshold | 0.7711 | | cosine_f1 | 0.9784 | | cosine_f1_threshold | 0.7711 | | cosine_precision | 0.96 | | cosine_recall | 0.9974 | | cosine_ap | 0.9865 | | dot_accuracy | 0.9771 | | dot_accuracy_threshold | 0.7711 | | dot_f1 | 0.9784 | | dot_f1_threshold | 0.7711 | | dot_precision | 0.96 | | dot_recall | 0.9974 | | dot_ap | 0.9866 | | manhattan_accuracy | 0.977 | | manhattan_accuracy_threshold | 10.5101 | | manhattan_f1 | 0.9784 | | manhattan_f1_threshold | 10.6372 | | manhattan_precision | 0.9599 | | manhattan_recall | 0.9975 | | manhattan_ap | 0.9866 | | euclidean_accuracy | 0.9771 | | euclidean_accuracy_threshold | 0.6766 | | euclidean_f1 | 0.9784 | | euclidean_f1_threshold | 0.6766 | | euclidean_precision | 0.96 | | euclidean_recall | 0.9974 | | euclidean_ap | 0.9866 | | max_accuracy | 0.9771 | | max_accuracy_threshold | 10.5101 | | max_f1 | 0.9784 | | max_f1_threshold | 10.6372 | | max_precision | 0.96 | | max_recall | 0.9975 | | **max_ap** | **0.9866** | ## Training Details ### Training Dataset #### deepparse_address_mutations_comb_3 * Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a) * Size: 4,517,388 training samples * Columns: label, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | label | sentence1 | sentence2 | |:--------|:-------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | torch.Tensor | string | string | | details | | | | * Samples: | label | sentence1 | sentence2 | |:----------------------------------------|:-----------------------------------------------------------|:-----------------------------------------------------------| | tensor(1, device='cuda:0') | 12737 chesdin landng dr chesterfield va 23838 | 12737 chesdin landng dr chesterfield va | | tensor(1, device='cuda:0') | 6080 norh oak trafficway gladstone mo 64118 | 6080 norh oak trafficway gladstone 64118-4896 | | tensor(0, device='cuda:0') | 242 pierce view cir wentzville mo 63385 | 242 pierce view cir wentzville LA 63385 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Evaluation Dataset #### deepparse_address_mutations_comb_3 * Dataset: [deepparse_address_mutations_comb_3](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3) at [7162fdc](https://huggingface.co/datasets/jarredparrett/deepparse_address_mutations_comb_3/tree/7162fdce4cfcb8114dc8f64d0631dc7a48c5ab7a) * Size: 968,012 evaluation samples * Columns: label, sentence1, and sentence2 * Approximate statistics based on the first 1000 samples: | | label | sentence1 | sentence2 | |:--------|:-------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------| | type | torch.Tensor | string | string | | details | | | | * Samples: | label | sentence1 | sentence2 | |:----------------------------------------|:------------------------------------------------------|:--------------------------------------------------------| | tensor(1, device='cuda:0') | 1 vincent avenue essex maryland 21221 | 1 vincent avenue essedx MD 21221 | | tensor(1, device='cuda:0') | 139 berg avenue hamilton tshp n.j. 08610 | 139 bcrg avenue hamilton tshp n.j. 08610 | | tensor(1, device='cuda:0') | 714 havard rd houston texas 77336 | 714 havaplns plns houston texas 77336-3120 | * Loss: [ContrastiveLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#contrastiveloss) with these parameters: ```json { "distance_metric": "SiameseDistanceMetric.COSINE_DISTANCE", "margin": 0.5, "size_average": true } ``` ### Training Hyperparameters #### Non-Default Hyperparameters - `eval_strategy`: steps - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `learning_rate`: 2e-05 - `warmup_ratio`: 0.1 - `fp16`: True - `batch_sampler`: no_duplicates #### All Hyperparameters
Click to expand - `overwrite_output_dir`: False - `do_predict`: False - `eval_strategy`: steps - `prediction_loss_only`: True - `per_device_train_batch_size`: 1024 - `per_device_eval_batch_size`: 1024 - `per_gpu_train_batch_size`: None - `per_gpu_eval_batch_size`: None - `gradient_accumulation_steps`: 1 - `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`: 3 - `max_steps`: -1 - `lr_scheduler_type`: linear - `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 - `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} - `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 - `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 - `dispatch_batches`: None - `split_batches`: 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 - `eval_use_gather_object`: False - `batch_sampler`: no_duplicates - `multi_dataset_batch_sampler`: proportional
### Training Logs | Epoch | Step | Training Loss | loss | jarredparrett/deepparse_address_mutations_comb_3_max_ap | |:------:|:-----:|:-------------:|:------:|:-------------------------------------------------------:| | 0.1133 | 500 | 0.0191 | 0.0131 | 0.8459 | | 0.2267 | 1000 | 0.0112 | 0.0091 | 0.8887 | | 0.3400 | 1500 | 0.0086 | 0.0067 | 0.9346 | | 0.4533 | 2000 | 0.0064 | 0.0044 | 0.9604 | | 0.5666 | 2500 | 0.0049 | 0.0037 | 0.9722 | | 0.6800 | 3000 | 0.0042 | 0.0033 | 0.9761 | | 0.7933 | 3500 | 0.0039 | 0.0032 | 0.9808 | | 0.9066 | 4000 | 0.0037 | 0.0029 | 0.9825 | | 1.0197 | 4500 | 0.0035 | 0.0028 | 0.9826 | | 1.1330 | 5000 | 0.0033 | 0.0028 | 0.9836 | | 1.2464 | 5500 | 0.0032 | 0.0027 | 0.9845 | | 1.3597 | 6000 | 0.0031 | 0.0026 | 0.9853 | | 1.4730 | 6500 | 0.003 | 0.0025 | 0.9857 | | 1.5864 | 7000 | 0.003 | 0.0025 | 0.9859 | | 1.6997 | 7500 | 0.0029 | 0.0025 | 0.9862 | | 1.8130 | 8000 | 0.0028 | 0.0024 | 0.9864 | | 1.9263 | 8500 | 0.0028 | 0.0024 | 0.9861 | | 2.0394 | 9000 | 0.0028 | 0.0024 | 0.9864 | | 2.1528 | 9500 | 0.0027 | 0.0024 | 0.9864 | | 2.2661 | 10000 | 0.0027 | 0.0024 | 0.9865 | | 2.3794 | 10500 | 0.0027 | 0.0023 | 0.9866 | | 2.4927 | 11000 | 0.0026 | 0.0023 | 0.9866 | | 2.6061 | 11500 | 0.0026 | 0.0023 | 0.9865 | | 2.7194 | 12000 | 0.0026 | 0.0023 | 0.9865 | | 2.8327 | 12500 | 0.0026 | 0.0023 | 0.9865 | | 2.9461 | 13000 | 0.0026 | 0.0023 | 0.9866 | | 2.9995 | 13236 | - | - | 0.9866 | ### Framework Versions - Python: 3.10.12 - Sentence Transformers: 3.1.1 - Transformers: 4.45.2 - PyTorch: 2.5.1+cu121 - Accelerate: 1.1.1 - Datasets: 3.2.0 - Tokenizers: 0.20.3 ## 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} } ```