End of training

a9e10b4 verified 2 months ago

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	---
	base_model: cointegrated/rubert-tiny2
	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
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:13690
	- loss:ContrastiveLoss
	widget:
	- source_sentence: Грузоблочный тренажер Bronze Gym D-015 - жим ногами в Москве Силовые
	тренажеры Грузоблочные Bronze Gym D-015 - жим ногами
	sentences:
	- Трицепс-машина Matrix G3-S45 Главная Силовые тренажеры Трицепс-машина Matrix G3-S45
	- Верхняя тяга Iron Bull IR-TE08 nan Силовые тренажеры Грузоблочные тренажеры
	- Горизонтальный велоэргометр Matrix Lifestyle с консолью LED nan Велотренажеры
	Matrix
	- source_sentence: Эллиптический тренажер Precor EFX 731 nan Эллиптические тренажеры
	Precor
	sentences:
	- Беговая дорожка коммерческая AeroFit X3-T 10″LCD в Москве Кардиотренажеры Беговые
	дорожки AeroFit X3-T 10″LCD
	- Машина Смита Matrix G1-FW161 Главная Силовые тренажеры Машина Смита Matrix G1-FW161
	- Эллиптический тренажер CardioPower X75 Главная Эллиптические тренажеры Бренды
	- source_sentence: Велотренажер Clear Fit Envy CFB 45 Ego Главная Велотренажеры Бренды
	sentences:
	- Велотренажер Spirit Fitness MU100 реабилитационный в Москве Кардиотренажеры Велотренажеры
	Spirit Fitness MU100 реабилитационный
	- Многофункциональная блочная станция Teca SP785C Две Гребных тяги nan Силовые тренажеры
	Мультистанции
	- Беговая дорожка Sports Art T670 Главная Беговые дорожки Бренды
	- source_sentence: Горизонтальный велотренажер TRUE C400 Главная Велотренажеры Бренды
	sentences:
	- Велотренажер UltraGym UG-B002 nan Велотренажеры UltraGym
	- Грузоблочный тренажер Precor DSL505 - задние дельты/баттерфляй в Москве Силовые
	тренажеры Грузоблочные Precor DSL505 - задние дельты/баттерфляй
	- Беговая дорожка Hasttings LCT80 Беговые дорожки Hasttings Hasttings LCT80
	- source_sentence: Беговая дорожка Hasttings CT100 Главная Беговые дорожки Беговая
	дорожка Hasttings CT100
	sentences:
	- Вертикальная тяга RangeMax CST-018 nan Силовые тренажеры Грузоблочные тренажеры
	- Беговая дорожка ProForm 910 Беговые дорожки ProForm ProForm 910
	- Беговая дорожка AMMITY SPACE ATM 5000 Главная Беговые дорожки Бренды
	model-index:
	- name: SentenceTransformer based on cointegrated/rubert-tiny2
	results:
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: cv
	type: cv
	metrics:
	- type: cosine_accuracy
	value: 1.0
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.7240798473358154
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 1.0
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7240798473358154
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 1.0
	name: Cosine Precision
	- type: cosine_recall
	value: 1.0
	name: Cosine Recall
	- type: cosine_ap
	value: 1.0
	name: Cosine Ap
	- type: dot_accuracy
	value: 1.0
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 0.7240797877311707
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 1.0
	name: Dot F1
	- type: dot_f1_threshold
	value: 0.7240797877311707
	name: Dot F1 Threshold
	- type: dot_precision
	value: 1.0
	name: Dot Precision
	- type: dot_recall
	value: 1.0
	name: Dot Recall
	- type: dot_ap
	value: 1.0
	name: Dot Ap
	- type: manhattan_accuracy
	value: 1.0
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 9.055404663085938
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 1.0
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 9.055404663085938
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 1.0
	name: Manhattan Precision
	- type: manhattan_recall
	value: 1.0
	name: Manhattan Recall
	- type: manhattan_ap
	value: 1.0
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 1.0
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 0.6519391536712646
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 1.0
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 0.6519391536712646
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 1.0
	name: Euclidean Precision
	- type: euclidean_recall
	value: 1.0
	name: Euclidean Recall
	- type: euclidean_ap
	value: 1.0
	name: Euclidean Ap
	- type: max_accuracy
	value: 1.0
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 9.055404663085938
	name: Max Accuracy Threshold
	- type: max_f1
	value: 1.0
	name: Max F1
	- type: max_f1_threshold
	value: 9.055404663085938
	name: Max F1 Threshold
	- type: max_precision
	value: 1.0
	name: Max Precision
	- type: max_recall
	value: 1.0
	name: Max Recall
	- type: max_ap
	value: 1.0
	name: Max Ap
	---

	# SentenceTransformer based on cointegrated/rubert-tiny2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2). It maps sentences & paragraphs to a 312-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: [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) <!-- at revision dad72b8f77c5eef6995dd3e4691b758ba56b90c3 -->
	- Maximum Sequence Length: 2048 tokens
	- Output Dimensionality: 312 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	<!-- - Language: Unknown -->
	<!-- - License: Unknown -->

	### 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': 2048, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 312, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'Беговая дорожка Hasttings CT100 Главная Беговые дорожки Беговая дорожка Hasttings CT100',
	'Беговая дорожка AMMITY SPACE ATM 5000 Главная Беговые дорожки Бренды',
	'Беговая дорожка ProForm 910 Беговые дорожки ProForm ProForm 910',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 312]

	# Get the similarity scores for the embeddings
	similarities = model.similarity(embeddings, embeddings)
	print(similarities.shape)
	# [3, 3]
	```

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	### Direct Usage (Transformers)

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	</details>
	-->

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	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

	<details><summary>Click to expand</summary>

	</details>
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	## Evaluation

	### Metrics

	#### Binary Classification
	* Dataset: `cv`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:--------\|
	\| cosine_accuracy \| 1.0 \|
	\| cosine_accuracy_threshold \| 0.7241 \|
	\| cosine_f1 \| 1.0 \|
	\| cosine_f1_threshold \| 0.7241 \|
	\| cosine_precision \| 1.0 \|
	\| cosine_recall \| 1.0 \|
	\| cosine_ap \| 1.0 \|
	\| dot_accuracy \| 1.0 \|
	\| dot_accuracy_threshold \| 0.7241 \|
	\| dot_f1 \| 1.0 \|
	\| dot_f1_threshold \| 0.7241 \|
	\| dot_precision \| 1.0 \|
	\| dot_recall \| 1.0 \|
	\| dot_ap \| 1.0 \|
	\| manhattan_accuracy \| 1.0 \|
	\| manhattan_accuracy_threshold \| 9.0554 \|
	\| manhattan_f1 \| 1.0 \|
	\| manhattan_f1_threshold \| 9.0554 \|
	\| manhattan_precision \| 1.0 \|
	\| manhattan_recall \| 1.0 \|
	\| manhattan_ap \| 1.0 \|
	\| euclidean_accuracy \| 1.0 \|
	\| euclidean_accuracy_threshold \| 0.6519 \|
	\| euclidean_f1 \| 1.0 \|
	\| euclidean_f1_threshold \| 0.6519 \|
	\| euclidean_precision \| 1.0 \|
	\| euclidean_recall \| 1.0 \|
	\| euclidean_ap \| 1.0 \|
	\| max_accuracy \| 1.0 \|
	\| max_accuracy_threshold \| 9.0554 \|
	\| max_f1 \| 1.0 \|
	\| max_f1_threshold \| 9.0554 \|
	\| max_precision \| 1.0 \|
	\| max_recall \| 1.0 \|
	\| max_ap \| 1.0 \|

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	## Bias, Risks and Limitations

	What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.
	-->

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	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
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	## Training Details

	### Training Dataset

	#### Unnamed Dataset


	* Size: 13,690 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 14 tokens</li><li>mean: 29.13 tokens</li><li>max: 66 tokens</li></ul> \| <ul><li>min: 13 tokens</li><li>mean: 29.18 tokens</li><li>max: 63 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.51</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:------------------------------------------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Велотренажер аэродинамический Spirit Fitness AB900+ Air Bike в Москве Кардиотренажеры Велотренажеры Spirit Fitness AB900+ Air Bike</code> \| <code>Велотренажер IZHIMIO СL 1500 Главная Велотренажеры Бренды</code> \| <code>1.0</code> \|
	\| <code>Эллиптический тренажер Sports Art E835 Главная Эллиптические тренажеры Бренды</code> \| <code>Степпер Matrix C7XI в Москве Кардиотренажеры Степперы Matrix C7XI</code> \| <code>0.0</code> \|
	\| <code>Мультистанция Nohrd SlimBeam nan Силовые тренажеры Мультистанции</code> \| <code>Эллиптический тренажер Koenigsmann JX-170EF в Москве Кардиотренажеры Эллиптические тренажеры Koenigsmann JX-170EF</code> \| <code>0.0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](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

	#### Unnamed Dataset


	* Size: 28 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
	* Approximate statistics based on the first 28 samples:
	\| \| sentence1 \| sentence2 \| score \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 15 tokens</li><li>mean: 27.18 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 16 tokens</li><li>mean: 28.0 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.61</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------\|:-----------------\|
	\| <code>Беговая дорожка Carbon Yukon Беговые дорожки Carbon Carbon Yukon</code> \| <code>Беговая дорожка Hasttings LCT80 Беговые дорожки Hasttings Hasttings LCT80</code> \| <code>1.0</code> \|
	\| <code>Беговая дорожка Беговая дорожка DFC BOSS I T-B1 для реабилитации Беговые дорожки DFC Беговая дорожка DFC BOSS I T-B1 для реабилитации</code> \| <code>Беговая дорожка EVO FITNESS Cosmo 5 Главная Беговые дорожки Бренды</code> \| <code>1.0</code> \|
	\| <code>Грузоблочный тренажер Precor C010ES - жим ногами/икроножные в Москве Силовые тренажеры Грузоблочные Precor C010ES - жим ногами/икроножные</code> \| <code>Кроссовер Bronze Gym D-005 Главная Силовые тренажеры Кроссовер Bronze Gym D-005</code> \| <code>1.0</code> \|
	* Loss: [<code>ContrastiveLoss</code>](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`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `num_train_epochs`: 10
	- `warmup_ratio`: 0.1
	- `fp16`: True

	#### All Hyperparameters
	<details><summary>Click to expand</summary>

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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`: 5e-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`: 10
	- `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
	- `eval_use_gather_object`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| cv_max_ap \|
	\|:------:\|:----:\|:-------------:\|:------:\|:---------:\|
	\| 0 \| 0 \| - \| - \| 0.7655 \|
	\| 1.0 \| 428 \| - \| 0.0056 \| 1.0 \|
	\| 1.1682 \| 500 \| 0.0078 \| - \| - \|
	\| 2.0 \| 856 \| - \| 0.0015 \| 1.0 \|
	\| 2.3364 \| 1000 \| 0.0019 \| - \| - \|
	\| 3.0 \| 1284 \| - \| 0.0011 \| 1.0 \|
	\| 3.5047 \| 1500 \| 0.0013 \| - \| - \|
	\| 4.0 \| 1712 \| - \| 0.0007 \| 1.0 \|
	\| 4.6729 \| 2000 \| 0.001 \| - \| - \|
	\| 5.0 \| 2140 \| - \| 0.0004 \| 1.0 \|
	\| 5.8411 \| 2500 \| 0.0008 \| - \| - \|
	\| 6.0 \| 2568 \| - \| 0.0002 \| 1.0 \|
	\| 7.0 \| 2996 \| - \| 0.0002 \| 1.0 \|
	\| 7.0093 \| 3000 \| 0.0007 \| - \| - \|
	\| 8.0 \| 3424 \| - \| 0.0001 \| 1.0 \|
	\| 8.1776 \| 3500 \| 0.0006 \| - \| - \|
	\| 9.0 \| 3852 \| - \| 0.0001 \| 1.0 \|
	\| 9.3458 \| 4000 \| 0.0005 \| - \| - \|
	\| 10.0 \| 4280 \| - \| 0.0001 \| 1.0 \|


	### Framework Versions
	- Python: 3.11.8
	- Sentence Transformers: 3.1.0
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu118
	- Accelerate: 0.34.2
	- Datasets: 3.0.0
	- Tokenizers: 0.19.1

	## 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}
	}
	```

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