Add new SentenceTransformer model.

df7dc43 verified 4 months ago

59 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1115700
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: UBC-NLP/serengeti-E250
	datasets: []
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Ndege mwenye mdomo mrefu katikati ya ndege.
	sentences:
	- Panya anayekimbia juu ya gurudumu.
	- Mtu anashindana katika mashindano ya mbio.
	- Ndege anayeruka.
	- source_sentence: Msichana mchanga mwenye nywele nyeusi anakabili kamera na kushikilia
	mfuko wa karatasi wakati amevaa shati la machungwa na mabawa ya kipepeo yenye
	rangi nyingi.
	sentences:
	- Mwanamke mzee anakataa kupigwa picha.
	- mtu akila na mvulana mdogo kwenye kijia cha jiji
	- Msichana mchanga anakabili kamera.
	- source_sentence: Wanawake na watoto wameketi nje katika kivuli wakati kikundi cha
	watoto wadogo wameketi ndani katika kivuli.
	sentences:
	- Mwanamke na watoto na kukaa chini.
	- Mwanamke huyo anakimbia.
	- Watu wanasafiri kwa baiskeli.
	- source_sentence: Mtoto mdogo anaruka mikononi mwa mwanamke aliyevalia suti nyeusi
	ya kuogelea akiwa kwenye dimbwi.
	sentences:
	- Mtoto akiruka mikononi mwa mwanamke aliyevalia suti ya kuogelea kwenye dimbwi.
	- Someone is holding oranges and walking
	- Mama na binti wakinunua viatu.
	- source_sentence: Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa
	kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi
	nyuma.
	sentences:
	- tai huruka
	- mwanamume na mwanamke wenye mikoba
	- Wanaume wawili wameketi karibu na mwanamke.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on UBC-NLP/serengeti-E250
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.7084016023985643
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7080643276583263
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7163851544290831
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7066259909380899
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.716171309296757
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7064427148038006
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.38463559218643695
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3566836293112297
	name: Spearman Dot
	- type: pearson_max
	value: 0.7163851544290831
	name: Pearson Max
	- type: spearman_max
	value: 0.7080643276583263
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.7059523092716506
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7046582726338858
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.714245009590492
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7048777976859945
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7150194670982656
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7055458365374757
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.3855295554891442
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3585966097040326
	name: Spearman Dot
	- type: pearson_max
	value: 0.7150194670982656
	name: Pearson Max
	- type: spearman_max
	value: 0.7055458365374757
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.7069259070512649
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7072103115498357
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7151518946293685
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7050845216566457
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7154956682724514
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.70486417475867
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.37291132473389677
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3480769113927452
	name: Spearman Dot
	- type: pearson_max
	value: 0.7154956682724514
	name: Pearson Max
	- type: spearman_max
	value: 0.7072103115498357
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.7022542784280805
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7062378358777478
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.711575484251127
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.701312903814612
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7125043324593673
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7011154675785318
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.34394993785114003
	name: Pearson Dot
	- type: spearman_dot
	value: 0.31686351995727197
	name: Spearman Dot
	- type: pearson_max
	value: 0.7125043324593673
	name: Pearson Max
	- type: spearman_max
	value: 0.7062378358777478
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6950172826546709
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6993973161633343
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7059726901866531
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6938542774412633
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7066346687971139
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6949014564343952
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.30982738809482646
	name: Pearson Dot
	- type: spearman_dot
	value: 0.2855406388879541
	name: Spearman Dot
	- type: pearson_max
	value: 0.7066346687971139
	name: Pearson Max
	- type: spearman_max
	value: 0.6993973161633343
	name: Spearman Max
	---

	# SentenceTransformer based on UBC-NLP/serengeti-E250

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250) on the Mollel/swahili-n_li-triplet-swh-eng 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: [UBC-NLP/serengeti-E250](https://huggingface.co/UBC-NLP/serengeti-E250) <!-- at revision 41b5b8b6179c4af2859768cbf4f0f03e928d651d -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- Mollel/swahili-n_li-triplet-swh-eng
	<!-- - 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': 512, 'do_lower_case': False}) with Transformer model: ElectraModel
	(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("sartifyllc/MultiLinguSwahili-MultiLinguSwahili-serengeti-E250-nli-matryoshka-nli-matryoshka")
	# Run inference
	sentences = [
	'Mwanamume na mwanamke wachanga waliovaa mikoba wanaweka au kuondoa kitu kutoka kwenye mti mweupe wa zamani, huku watu wengine wamesimama au wameketi nyuma.',
	'mwanamume na mwanamke wenye mikoba',
	'tai huruka',
	]
	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]
	```

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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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	### Out-of-Scope Use

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	## Evaluation

	### Metrics

	#### Semantic Similarity
	* Dataset: `sts-test-768`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7084 \|
	\| spearman_cosine \| 0.7081 \|
	\| pearson_manhattan \| 0.7164 \|
	\| spearman_manhattan \| 0.7066 \|
	\| pearson_euclidean \| 0.7162 \|
	\| spearman_euclidean \| 0.7064 \|
	\| pearson_dot \| 0.3846 \|
	\| spearman_dot \| 0.3567 \|
	\| pearson_max \| 0.7164 \|
	\| spearman_max \| 0.7081 \|

	#### Semantic Similarity
	* Dataset: `sts-test-512`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.706 \|
	\| spearman_cosine \| 0.7047 \|
	\| pearson_manhattan \| 0.7142 \|
	\| spearman_manhattan \| 0.7049 \|
	\| pearson_euclidean \| 0.715 \|
	\| spearman_euclidean \| 0.7055 \|
	\| pearson_dot \| 0.3855 \|
	\| spearman_dot \| 0.3586 \|
	\| pearson_max \| 0.715 \|
	\| spearman_max \| 0.7055 \|

	#### Semantic Similarity
	* Dataset: `sts-test-256`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7069 \|
	\| spearman_cosine \| 0.7072 \|
	\| pearson_manhattan \| 0.7152 \|
	\| spearman_manhattan \| 0.7051 \|
	\| pearson_euclidean \| 0.7155 \|
	\| spearman_euclidean \| 0.7049 \|
	\| pearson_dot \| 0.3729 \|
	\| spearman_dot \| 0.3481 \|
	\| pearson_max \| 0.7155 \|
	\| spearman_max \| 0.7072 \|

	#### Semantic Similarity
	* Dataset: `sts-test-128`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7023 \|
	\| spearman_cosine \| 0.7062 \|
	\| pearson_manhattan \| 0.7116 \|
	\| spearman_manhattan \| 0.7013 \|
	\| pearson_euclidean \| 0.7125 \|
	\| spearman_euclidean \| 0.7011 \|
	\| pearson_dot \| 0.3439 \|
	\| spearman_dot \| 0.3169 \|
	\| pearson_max \| 0.7125 \|
	\| spearman_max \| 0.7062 \|

	#### Semantic Similarity
	* Dataset: `sts-test-64`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.695 \|
	\| spearman_cosine \| 0.6994 \|
	\| pearson_manhattan \| 0.706 \|
	\| spearman_manhattan \| 0.6939 \|
	\| pearson_euclidean \| 0.7066 \|
	\| spearman_euclidean \| 0.6949 \|
	\| pearson_dot \| 0.3098 \|
	\| spearman_dot \| 0.2855 \|
	\| pearson_max \| 0.7066 \|
	\| spearman_max \| 0.6994 \|

	<!--
	## 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

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	## Training Details

	### Training Dataset

	#### Mollel/swahili-n_li-triplet-swh-eng

	* Dataset: Mollel/swahili-n_li-triplet-swh-eng
	* Size: 1,115,700 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: 6 tokens</li><li>mean: 11.27 tokens</li><li>max: 48 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.0 tokens</li><li>max: 29 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 12.56 tokens</li><li>max: 29 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>Mtu aliyepanda farasi anaruka juu ya ndege iliyovunjika.</code> \| <code>Mtu yuko nje, juu ya farasi.</code> \| <code>Mtu yuko kwenye mkahawa, akiagiza omelette.</code> \|
	\| <code>Children smiling and waving at camera</code> \| <code>There are children present</code> \| <code>The kids are frowning</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

	### Evaluation Dataset

	#### Mollel/swahili-n_li-triplet-swh-eng

	* Dataset: Mollel/swahili-n_li-triplet-swh-eng
	* Size: 13,168 evaluation 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: 5 tokens</li><li>mean: 18.07 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 9.45 tokens</li><li>max: 33 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.27 tokens</li><li>max: 29 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>The men are fighting outside a deli.</code> \|
	\| <code>Wanawake wawili wanakumbatiana huku wakishikilia vifurushi vya kwenda.</code> \| <code>Wanawake wawili wanashikilia vifurushi.</code> \| <code>Wanaume hao wanapigana nje ya duka la vyakula vitamu.</code> \|
	\| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> \| <code>Two kids in numbered jerseys wash their hands.</code> \| <code>Two kids in jackets walk to school.</code> \|
	* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
	```json
	{
	"loss": "MultipleNegativesRankingLoss",
	"matryoshka_dims": [
	768,
	512,
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `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
	- `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`: 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`: True
	- `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`: 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, '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_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	<details><summary>Click to expand</summary>

	\| Epoch \| Step \| Training Loss \| sts-test-128_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-512_spearman_cosine \| sts-test-64_spearman_cosine \| sts-test-768_spearman_cosine \|
	\|:------:\|:-----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|:----------------------------:\|
	\| 0.0057 \| 100 \| 26.7003 \| - \| - \| - \| - \| - \|
	\| 0.0115 \| 200 \| 20.7097 \| - \| - \| - \| - \| - \|
	\| 0.0172 \| 300 \| 17.2266 \| - \| - \| - \| - \| - \|
	\| 0.0229 \| 400 \| 15.7511 \| - \| - \| - \| - \| - \|
	\| 0.0287 \| 500 \| 14.5329 \| - \| - \| - \| - \| - \|
	\| 0.0344 \| 600 \| 12.6534 \| - \| - \| - \| - \| - \|
	\| 0.0402 \| 700 \| 10.6758 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 800 \| 9.421 \| - \| - \| - \| - \| - \|
	\| 0.0516 \| 900 \| 9.5664 \| - \| - \| - \| - \| - \|
	\| 0.0574 \| 1000 \| 8.5166 \| - \| - \| - \| - \| - \|
	\| 0.0631 \| 1100 \| 8.657 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 1200 \| 8.5473 \| - \| - \| - \| - \| - \|
	\| 0.0746 \| 1300 \| 8.3018 \| - \| - \| - \| - \| - \|
	\| 0.0803 \| 1400 \| 8.4488 \| - \| - \| - \| - \| - \|
	\| 0.0860 \| 1500 \| 7.1796 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 1600 \| 6.6136 \| - \| - \| - \| - \| - \|
	\| 0.0975 \| 1700 \| 6.2638 \| - \| - \| - \| - \| - \|
	\| 0.1033 \| 1800 \| 6.6955 \| - \| - \| - \| - \| - \|
	\| 0.1090 \| 1900 \| 7.3585 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 2000 \| 6.9043 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2100 \| 6.677 \| - \| - \| - \| - \| - \|
	\| 0.1262 \| 2200 \| 6.3914 \| - \| - \| - \| - \| - \|
	\| 0.1319 \| 2300 \| 6.0045 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 2400 \| 5.8048 \| - \| - \| - \| - \| - \|
	\| 0.1434 \| 2500 \| 5.6898 \| - \| - \| - \| - \| - \|
	\| 0.1491 \| 2600 \| 5.229 \| - \| - \| - \| - \| - \|
	\| 0.1549 \| 2700 \| 5.2407 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 2800 \| 5.7074 \| - \| - \| - \| - \| - \|
	\| 0.1664 \| 2900 \| 6.2917 \| - \| - \| - \| - \| - \|
	\| 0.1721 \| 3000 \| 6.5651 \| - \| - \| - \| - \| - \|
	\| 0.1778 \| 3100 \| 6.7751 \| - \| - \| - \| - \| - \|
	\| 0.1836 \| 3200 \| 6.195 \| - \| - \| - \| - \| - \|
	\| 0.1893 \| 3300 \| 5.4697 \| - \| - \| - \| - \| - \|
	\| 0.1950 \| 3400 \| 5.1362 \| - \| - \| - \| - \| - \|
	\| 0.2008 \| 3500 \| 5.581 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 3600 \| 5.4309 \| - \| - \| - \| - \| - \|
	\| 0.2122 \| 3700 \| 5.6688 \| - \| - \| - \| - \| - \|
	\| 0.2180 \| 3800 \| 5.6923 \| - \| - \| - \| - \| - \|
	\| 0.2237 \| 3900 \| 5.8598 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 4000 \| 5.3498 \| - \| - \| - \| - \| - \|
	\| 0.2352 \| 4100 \| 5.3797 \| - \| - \| - \| - \| - \|
	\| 0.2409 \| 4200 \| 5.0389 \| - \| - \| - \| - \| - \|
	\| 0.2467 \| 4300 \| 5.6622 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 4400 \| 5.6249 \| - \| - \| - \| - \| - \|
	\| 0.2581 \| 4500 \| 5.6927 \| - \| - \| - \| - \| - \|
	\| 0.2639 \| 4600 \| 5.3612 \| - \| - \| - \| - \| - \|
	\| 0.2696 \| 4700 \| 5.2751 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 4800 \| 5.4224 \| - \| - \| - \| - \| - \|
	\| 0.2811 \| 4900 \| 5.0338 \| - \| - \| - \| - \| - \|
	\| 0.2868 \| 5000 \| 4.9813 \| - \| - \| - \| - \| - \|
	\| 0.2925 \| 5100 \| 4.8533 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 5200 \| 5.4137 \| - \| - \| - \| - \| - \|
	\| 0.3040 \| 5300 \| 5.4063 \| - \| - \| - \| - \| - \|
	\| 0.3098 \| 5400 \| 5.3107 \| - \| - \| - \| - \| - \|
	\| 0.3155 \| 5500 \| 5.0907 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 5600 \| 4.8644 \| - \| - \| - \| - \| - \|
	\| 0.3270 \| 5700 \| 4.7926 \| - \| - \| - \| - \| - \|
	\| 0.3327 \| 5800 \| 5.0268 \| - \| - \| - \| - \| - \|
	\| 0.3384 \| 5900 \| 5.3029 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 6000 \| 5.1246 \| - \| - \| - \| - \| - \|
	\| 0.3499 \| 6100 \| 5.1152 \| - \| - \| - \| - \| - \|
	\| 0.3556 \| 6200 \| 5.4265 \| - \| - \| - \| - \| - \|
	\| 0.3614 \| 6300 \| 4.7079 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 6400 \| 4.6368 \| - \| - \| - \| - \| - \|
	\| 0.3729 \| 6500 \| 4.662 \| - \| - \| - \| - \| - \|
	\| 0.3786 \| 6600 \| 5.3695 \| - \| - \| - \| - \| - \|
	\| 0.3843 \| 6700 \| 4.6974 \| - \| - \| - \| - \| - \|
	\| 0.3901 \| 6800 \| 4.6584 \| - \| - \| - \| - \| - \|
	\| 0.3958 \| 6900 \| 4.7413 \| - \| - \| - \| - \| - \|
	\| 0.4015 \| 7000 \| 4.6604 \| - \| - \| - \| - \| - \|
	\| 0.4073 \| 7100 \| 5.2476 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 7200 \| 4.9966 \| - \| - \| - \| - \| - \|
	\| 0.4187 \| 7300 \| 4.656 \| - \| - \| - \| - \| - \|
	\| 0.4245 \| 7400 \| 4.5711 \| - \| - \| - \| - \| - \|
	\| 0.4302 \| 7500 \| 5.0256 \| - \| - \| - \| - \| - \|
	\| 0.4360 \| 7600 \| 4.3856 \| - \| - \| - \| - \| - \|
	\| 0.4417 \| 7700 \| 4.2548 \| - \| - \| - \| - \| - \|
	\| 0.4474 \| 7800 \| 4.8584 \| - \| - \| - \| - \| - \|
	\| 0.4532 \| 7900 \| 4.8563 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 8000 \| 4.5101 \| - \| - \| - \| - \| - \|
	\| 0.4646 \| 8100 \| 4.4688 \| - \| - \| - \| - \| - \|
	\| 0.4704 \| 8200 \| 4.7076 \| - \| - \| - \| - \| - \|
	\| 0.4761 \| 8300 \| 4.3268 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 8400 \| 4.6622 \| - \| - \| - \| - \| - \|
	\| 0.4876 \| 8500 \| 4.4808 \| - \| - \| - \| - \| - \|
	\| 0.4933 \| 8600 \| 4.676 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 8700 \| 5.0348 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 8800 \| 4.5497 \| - \| - \| - \| - \| - \|
	\| 0.5105 \| 8900 \| 4.7428 \| - \| - \| - \| - \| - \|
	\| 0.5163 \| 9000 \| 4.4418 \| - \| - \| - \| - \| - \|
	\| 0.5220 \| 9100 \| 4.4946 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 9200 \| 4.5249 \| - \| - \| - \| - \| - \|
	\| 0.5335 \| 9300 \| 4.2413 \| - \| - \| - \| - \| - \|
	\| 0.5392 \| 9400 \| 4.4799 \| - \| - \| - \| - \| - \|
	\| 0.5449 \| 9500 \| 4.6807 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 9600 \| 4.5901 \| - \| - \| - \| - \| - \|
	\| 0.5564 \| 9700 \| 4.7266 \| - \| - \| - \| - \| - \|
	\| 0.5622 \| 9800 \| 4.692 \| - \| - \| - \| - \| - \|
	\| 0.5679 \| 9900 \| 4.8651 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 10000 \| 4.7746 \| - \| - \| - \| - \| - \|
	\| 0.5794 \| 10100 \| 4.68 \| - \| - \| - \| - \| - \|
	\| 0.5851 \| 10200 \| 4.7697 \| - \| - \| - \| - \| - \|
	\| 0.5908 \| 10300 \| 4.8848 \| - \| - \| - \| - \| - \|
	\| 0.5966 \| 10400 \| 4.4004 \| - \| - \| - \| - \| - \|
	\| 0.6023 \| 10500 \| 4.2979 \| - \| - \| - \| - \| - \|
	\| 0.6080 \| 10600 \| 4.7266 \| - \| - \| - \| - \| - \|
	\| 0.6138 \| 10700 \| 4.8605 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 10800 \| 4.7436 \| - \| - \| - \| - \| - \|
	\| 0.6253 \| 10900 \| 4.6239 \| - \| - \| - \| - \| - \|
	\| 0.6310 \| 11000 \| 4.394 \| - \| - \| - \| - \| - \|
	\| 0.6367 \| 11100 \| 4.8081 \| - \| - \| - \| - \| - \|
	\| 0.6425 \| 11200 \| 4.2329 \| - \| - \| - \| - \| - \|
	\| 0.6482 \| 11300 \| 4.873 \| - \| - \| - \| - \| - \|
	\| 0.6539 \| 11400 \| 4.5557 \| - \| - \| - \| - \| - \|
	\| 0.6597 \| 11500 \| 4.7918 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 11600 \| 4.1607 \| - \| - \| - \| - \| - \|
	\| 0.6711 \| 11700 \| 4.8744 \| - \| - \| - \| - \| - \|
	\| 0.6769 \| 11800 \| 5.0072 \| - \| - \| - \| - \| - \|
	\| 0.6826 \| 11900 \| 4.3532 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 12000 \| 4.3319 \| - \| - \| - \| - \| - \|
	\| 0.6941 \| 12100 \| 4.6885 \| - \| - \| - \| - \| - \|
	\| 0.6998 \| 12200 \| 4.6682 \| - \| - \| - \| - \| - \|
	\| 0.7056 \| 12300 \| 4.4258 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 12400 \| 4.6136 \| - \| - \| - \| - \| - \|
	\| 0.7170 \| 12500 \| 4.3594 \| - \| - \| - \| - \| - \|
	\| 0.7228 \| 12600 \| 4.0627 \| - \| - \| - \| - \| - \|
	\| 0.7285 \| 12700 \| 4.5244 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 12800 \| 4.504 \| - \| - \| - \| - \| - \|
	\| 0.7400 \| 12900 \| 4.4694 \| - \| - \| - \| - \| - \|
	\| 0.7457 \| 13000 \| 4.4804 \| - \| - \| - \| - \| - \|
	\| 0.7514 \| 13100 \| 4.0588 \| - \| - \| - \| - \| - \|
	\| 0.7572 \| 13200 \| 4.8016 \| - \| - \| - \| - \| - \|
	\| 0.7629 \| 13300 \| 4.2971 \| - \| - \| - \| - \| - \|
	\| 0.7687 \| 13400 \| 4.1326 \| - \| - \| - \| - \| - \|
	\| 0.7744 \| 13500 \| 3.9763 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 13600 \| 3.7716 \| - \| - \| - \| - \| - \|
	\| 0.7859 \| 13700 \| 3.8448 \| - \| - \| - \| - \| - \|
	\| 0.7916 \| 13800 \| 3.6779 \| - \| - \| - \| - \| - \|
	\| 0.7973 \| 13900 \| 3.5938 \| - \| - \| - \| - \| - \|
	\| 0.8031 \| 14000 \| 3.3981 \| - \| - \| - \| - \| - \|
	\| 0.8088 \| 14100 \| 3.4151 \| - \| - \| - \| - \| - \|
	\| 0.8145 \| 14200 \| 3.2498 \| - \| - \| - \| - \| - \|
	\| 0.8203 \| 14300 \| 3.4909 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 14400 \| 3.4098 \| - \| - \| - \| - \| - \|
	\| 0.8318 \| 14500 \| 3.4448 \| - \| - \| - \| - \| - \|
	\| 0.8375 \| 14600 \| 3.2868 \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 14700 \| 3.2196 \| - \| - \| - \| - \| - \|
	\| 0.8490 \| 14800 \| 3.0852 \| - \| - \| - \| - \| - \|
	\| 0.8547 \| 14900 \| 3.2341 \| - \| - \| - \| - \| - \|
	\| 0.8604 \| 15000 \| 3.164 \| - \| - \| - \| - \| - \|
	\| 0.8662 \| 15100 \| 3.0919 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 15200 \| 3.176 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 15300 \| 3.1361 \| - \| - \| - \| - \| - \|
	\| 0.8834 \| 15400 \| 3.0683 \| - \| - \| - \| - \| - \|
	\| 0.8891 \| 15500 \| 3.0275 \| - \| - \| - \| - \| - \|
	\| 0.8949 \| 15600 \| 3.0763 \| - \| - \| - \| - \| - \|
	\| 0.9006 \| 15700 \| 3.1828 \| - \| - \| - \| - \| - \|
	\| 0.9063 \| 15800 \| 3.0053 \| - \| - \| - \| - \| - \|
	\| 0.9121 \| 15900 \| 2.9696 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 16000 \| 2.8919 \| - \| - \| - \| - \| - \|
	\| 0.9235 \| 16100 \| 2.9922 \| - \| - \| - \| - \| - \|
	\| 0.9293 \| 16200 \| 2.9063 \| - \| - \| - \| - \| - \|
	\| 0.9350 \| 16300 \| 3.0633 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 16400 \| 3.1782 \| - \| - \| - \| - \| - \|
	\| 0.9465 \| 16500 \| 2.9206 \| - \| - \| - \| - \| - \|
	\| 0.9522 \| 16600 \| 2.8785 \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 16700 \| 2.9934 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 16800 \| 3.0125 \| - \| - \| - \| - \| - \|
	\| 0.9694 \| 16900 \| 2.9338 \| - \| - \| - \| - \| - \|
	\| 0.9752 \| 17000 \| 2.9931 \| - \| - \| - \| - \| - \|
	\| 0.9809 \| 17100 \| 2.956 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 17200 \| 2.8415 \| - \| - \| - \| - \| - \|
	\| 0.9924 \| 17300 \| 3.0072 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 17400 \| 2.9046 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 17433 \| - \| 0.7062 \| 0.7072 \| 0.7047 \| 0.6994 \| 0.7081 \|

	</details>

	### Framework Versions
	- Python: 3.11.9
	- Sentence Transformers: 3.0.1
	- Transformers: 4.40.1
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.29.3
	- Datasets: 2.19.0
	- 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",
	}
	```

	#### 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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