Add new SentenceTransformer model.

fd1a504 verified 8 months ago

24.7 kB

	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- loss:Matryoshka2dLoss
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: distilbert/distilroberta-base
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: A woman is reading.
	sentences:
	- A woman is writing something.
	- A man helps a boy ride a bike.
	- A group wading across a ditch
	- source_sentence: A man shoots a man.
	sentences:
	- A man with a pistol shoots another man.
	- Suicide bomber strikes in Syria
	- China and Taiwan hold historic talks
	- source_sentence: A boy is vacuuming.
	sentences:
	- A little boy is vacuuming the floor.
	- 'Breivik: Jail term ''ridiculous'''
	- Glorious triple-gold night for Britain
	- source_sentence: A man is spitting.
	sentences:
	- A man is speaking.
	- The boy is jumping into a lake.
	- 10 Things to Know for Thursday
	- source_sentence: A plane in the sky.
	sentences:
	- Two airplanes in the sky.
	- Nelson Mandela undergoes surgery
	- Nelson Mandela undergoes surgery
	pipeline_tag: sentence-similarity
	co2_eq_emissions:
	emissions: 69.2573690422145
	energy_consumed: 0.1781760038338226
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.626
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on distilbert/distilroberta-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.8395203447657347
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8424556124488326
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8432537220190851
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.8435994230515586
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8440900768179745
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8449067313707376
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.763767029856877
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7569706383510251
	name: Spearman Dot
	- type: pearson_max
	value: 0.8440900768179745
	name: Pearson Max
	- type: spearman_max
	value: 0.8449067313707376
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.8186702838538092
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.8170686920551
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.8117192659894803
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.804879002947593
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.8127154744140831
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.8058410028545979
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7396245702595934
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7256120569318246
	name: Spearman Dot
	- type: pearson_max
	value: 0.8186702838538092
	name: Pearson Max
	- type: spearman_max
	value: 0.8170686920551
	name: Spearman Max
	---

	# SentenceTransformer based on distilbert/distilroberta-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) on the [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) 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: [distilbert/distilroberta-base](https://huggingface.co./distilbert/distilroberta-base) <!-- at revision fb53ab8802853c8e4fbdbcd0529f21fc6f459b2b -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli)
	- Language: en
	<!-- - 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': 512, 'do_lower_case': False}) with Transformer model: RobertaModel
	(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("tomaarsen/distilroberta-base-nli-2d-matryoshka")
	# Run inference
	sentences = [
	'A plane in the sky.',
	'Two airplanes in the sky.',
	'Nelson Mandela undergoes surgery',
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]

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

	<!--
	### Direct Usage (Transformers)

	<details><summary>Click to see the direct usage in Transformers</summary>

	</details>
	-->

	<!--
	### Downstream Usage (Sentence Transformers)

	You can finetune this model on your own dataset.

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

	</details>
	-->

	<!--
	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
	-->

	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-dev`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8395 \|
	\| spearman_cosine \| 0.8425 \|
	\| pearson_manhattan \| 0.8433 \|
	\| spearman_manhattan \| 0.8436 \|
	\| pearson_euclidean \| 0.8441 \|
	\| spearman_euclidean \| 0.8449 \|
	\| pearson_dot \| 0.7638 \|
	\| spearman_dot \| 0.757 \|
	\| pearson_max \| 0.8441 \|
	\| spearman_max \| 0.8449 \|

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.8187 \|
	\| spearman_cosine \| 0.8171 \|
	\| pearson_manhattan \| 0.8117 \|
	\| spearman_manhattan \| 0.8049 \|
	\| pearson_euclidean \| 0.8127 \|
	\| spearman_euclidean \| 0.8058 \|
	\| pearson_dot \| 0.7396 \|
	\| spearman_dot \| 0.7256 \|
	\| pearson_max \| 0.8187 \|
	\| spearman_max \| 0.8171 \|

	<!--
	## 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.
	-->

	<!--
	### Recommendations

	What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.
	-->

	## Training Details

	### Training Dataset

	#### sentence-transformers/all-nli

	* Dataset: [sentence-transformers/all-nli](https://huggingface.co./datasets/sentence-transformers/all-nli) at [65dd388](https://huggingface.co./datasets/sentence-transformers/all-nli/tree/65dd38867b600f42241d2066ba1a35fbd097fcfe)
	* Size: 557,850 training samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 10.38 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 12.8 tokens</li><li>max: 39 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------------\|:-------------------------------------------------\|:-----------------------------------------------------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>A person is at a diner, ordering an omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	\| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> \| <code>The boy does a skateboarding trick.</code> \| <code>The boy skates down the sidewalk.</code> \|
	* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1.0,
	"prior_layers_weight": 1.0,
	"kl_div_weight": 1.0,
	"kl_temperature": 0.3,
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": 1
	}
	```

	### Evaluation Dataset

	#### sentence-transformers/stsb

	* Dataset: [sentence-transformers/stsb](https://huggingface.co./datasets/sentence-transformers/stsb) at [ab7a5ac](https://huggingface.co./datasets/sentence-transformers/stsb/tree/ab7a5ac0e35aa22088bdcf23e7fd99b220e53308)
	* Size: 1,500 evaluation 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: 5 tokens</li><li>mean: 15.0 tokens</li><li>max: 44 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 14.99 tokens</li><li>max: 61 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| score \|
	\|:--------------------------------------------------\|:------------------------------------------------------\|:------------------\|
	\| <code>A man with a hard hat is dancing.</code> \| <code>A man wearing a hard hat is dancing.</code> \| <code>1.0</code> \|
	\| <code>A young child is riding a horse.</code> \| <code>A child is riding a horse.</code> \| <code>0.95</code> \|
	\| <code>A man is feeding a mouse to a snake.</code> \| <code>The man is feeding a mouse to the snake.</code> \| <code>1.0</code> \|
	* Loss: [<code>Matryoshka2dLoss</code>](https://sbert.net/docs/package_reference/losses.html#matryoshka2dloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"n_layers_per_step": 1,
	"last_layer_weight": 1.0,
	"prior_layers_weight": 1.0,
	"kl_div_weight": 1.0,
	"kl_temperature": 0.3,
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": 1
	}
	```

	### Training Hyperparameters
	#### Non-Default Hyperparameters

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: False
	- `per_device_train_batch_size`: 128
	- `per_device_eval_batch_size`: 128
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 1
	- `eval_accumulation_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`: 1
	- `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
	- `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`: None
	- `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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| sts-dev_spearman_cosine \| sts-test_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:------:\|:-----------------------:\|:------------------------:\|
	\| 0.0229 \| 100 \| 6.2779 \| 3.9959 \| 0.8008 \| - \|
	\| 0.0459 \| 200 \| 4.3212 \| 3.5818 \| 0.7956 \| - \|
	\| 0.0688 \| 300 \| 3.7135 \| 3.4422 \| 0.7940 \| - \|
	\| 0.0918 \| 400 \| 3.5567 \| 3.5458 \| 0.7951 \| - \|
	\| 0.1147 \| 500 \| 3.1297 \| 3.1253 \| 0.8050 \| - \|
	\| 0.1376 \| 600 \| 2.7001 \| 3.4366 \| 0.7996 \| - \|
	\| 0.1606 \| 700 \| 2.8664 \| 3.6609 \| 0.8033 \| - \|
	\| 0.1835 \| 800 \| 2.6656 \| 3.3736 \| 0.7975 \| - \|
	\| 0.2065 \| 900 \| 2.633 \| 3.3735 \| 0.8076 \| - \|
	\| 0.2294 \| 1000 \| 2.4335 \| 3.6499 \| 0.7996 \| - \|
	\| 0.2524 \| 1100 \| 2.4165 \| 3.6301 \| 0.8015 \| - \|
	\| 0.2753 \| 1200 \| 2.2942 \| 3.1541 \| 0.7994 \| - \|
	\| 0.2982 \| 1300 \| 2.2402 \| 3.4284 \| 0.7977 \| - \|
	\| 0.3212 \| 1400 \| 2.2148 \| 3.3775 \| 0.7988 \| - \|
	\| 0.3441 \| 1500 \| 2.2285 \| 3.6097 \| 0.8016 \| - \|
	\| 0.3671 \| 1600 \| 2.0591 \| 3.3839 \| 0.7926 \| - \|
	\| 0.3900 \| 1700 \| 2.0253 \| 3.1113 \| 0.7981 \| - \|
	\| 0.4129 \| 1800 \| 2.0244 \| 3.8289 \| 0.7954 \| - \|
	\| 0.4359 \| 1900 \| 1.8582 \| 3.3515 \| 0.8000 \| - \|
	\| 0.4588 \| 2000 \| 1.977 \| 3.3054 \| 0.7917 \| - \|
	\| 0.4818 \| 2100 \| 1.9028 \| 3.2166 \| 0.7927 \| - \|
	\| 0.5047 \| 2200 \| 1.8316 \| 3.6504 \| 0.7955 \| - \|
	\| 0.5276 \| 2300 \| 1.8404 \| 3.2822 \| 0.7843 \| - \|
	\| 0.5506 \| 2400 \| 1.8455 \| 3.2583 \| 0.7941 \| - \|
	\| 0.5735 \| 2500 \| 1.9488 \| 3.3970 \| 0.7971 \| - \|
	\| 0.5965 \| 2600 \| 1.9403 \| 2.8948 \| 0.7959 \| - \|
	\| 0.6194 \| 2700 \| 1.8884 \| 3.2227 \| 0.8008 \| - \|
	\| 0.6423 \| 2800 \| 1.8655 \| 3.1948 \| 0.7920 \| - \|
	\| 0.6653 \| 2900 \| 1.8567 \| 3.4374 \| 0.7913 \| - \|
	\| 0.6882 \| 3000 \| 1.8423 \| 3.1118 \| 0.7949 \| - \|
	\| 0.7112 \| 3100 \| 1.7475 \| 3.1359 \| 0.8062 \| - \|
	\| 0.7341 \| 3200 \| 1.8166 \| 2.9927 \| 0.7984 \| - \|
	\| 0.7571 \| 3300 \| 1.5626 \| 3.5143 \| 0.8405 \| - \|
	\| 0.7800 \| 3400 \| 1.2038 \| 3.3909 \| 0.8411 \| - \|
	\| 0.8029 \| 3500 \| 1.1579 \| 3.2458 \| 0.8413 \| - \|
	\| 0.8259 \| 3600 \| 1.0978 \| 3.1592 \| 0.8404 \| - \|
	\| 0.8488 \| 3700 \| 1.0283 \| 2.9557 \| 0.8408 \| - \|
	\| 0.8718 \| 3800 \| 0.9993 \| 3.4073 \| 0.8430 \| - \|
	\| 0.8947 \| 3900 \| 0.9727 \| 3.0570 \| 0.8434 \| - \|
	\| 0.9176 \| 4000 \| 0.9692 \| 2.9357 \| 0.8439 \| - \|
	\| 0.9406 \| 4100 \| 0.9412 \| 2.9494 \| 0.8428 \| - \|
	\| 0.9635 \| 4200 \| 1.0063 \| 3.4047 \| 0.8422 \| - \|
	\| 0.9865 \| 4300 \| 0.9678 \| 3.4299 \| 0.8425 \| - \|
	\| 1.0 \| 4359 \| - \| - \| - \| 0.8171 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.178 kWh
	- Carbon Emitted: 0.069 kg of CO2
	- Hours Used: 0.626 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 3.0.0.dev0
	- Transformers: 4.41.0.dev0
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.18.0
	- 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",
	}
	```

	#### Matryoshka2dLoss
	```bibtex
	@misc{li20242d,
	title={2D Matryoshka Sentence Embeddings},
	author={Xianming Li and Zongxi Li and Jing Li and Haoran Xie and Qing Li},
	year={2024},
	eprint={2402.14776},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

	#### MatryoshkaLoss
	```bibtex
	@misc{kusupati2024matryoshka,
	title={Matryoshka Representation Learning},
	author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
	year={2024},
	eprint={2205.13147},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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