Add new SentenceTransformer model.

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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:1115700
	- loss:MatryoshkaLoss
	- loss:MultipleNegativesRankingLoss
	base_model: Geotrend/bert-base-sw-cased
	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 Geotrend/bert-base-sw-cased
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 768
	type: sts-test-768
	metrics:
	- type: pearson_cosine
	value: 0.6937245827269046
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6872564222432196
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6671541268726737
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6578428252987948
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6672292642346008
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6577692881532263
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5234944445417878
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5126395384896926
	name: Spearman Dot
	- type: pearson_max
	value: 0.6937245827269046
	name: Pearson Max
	- type: spearman_max
	value: 0.6872564222432196
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.689885399601221
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6847071916895495
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6678379220949281
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6579957115799916
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6673062843667007
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6573006123381013
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.49533316366864977
	name: Pearson Dot
	- type: spearman_dot
	value: 0.48723679408818543
	name: Spearman Dot
	- type: pearson_max
	value: 0.689885399601221
	name: Pearson Max
	- type: spearman_max
	value: 0.6847071916895495
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.6873377612773459
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6816874105466478
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.667357515297651
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6557727891191705
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6674937201647584
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6560441259953166
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.45660372834373963
	name: Pearson Dot
	- type: spearman_dot
	value: 0.4533070407260065
	name: Spearman Dot
	- type: pearson_max
	value: 0.6873377612773459
	name: Pearson Max
	- type: spearman_max
	value: 0.6816874105466478
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.6836009506667413
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6795423695973911
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6663652896396122
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6534731725514219
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6663726876345561
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6537216014002204
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.43102957451470686
	name: Pearson Dot
	- type: spearman_dot
	value: 0.431538008932168
	name: Spearman Dot
	- type: pearson_max
	value: 0.6836009506667413
	name: Pearson Max
	- type: spearman_max
	value: 0.6795423695973911
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6715253560367674
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.669070001537953
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6571390159051358
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6456119247619697
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6598587843081631
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6472279949159918
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.36757468941627225
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3678274698380672
	name: Spearman Dot
	- type: pearson_max
	value: 0.6715253560367674
	name: Pearson Max
	- type: spearman_max
	value: 0.669070001537953
	name: Spearman Max
	---

	# SentenceTransformer based on Geotrend/bert-base-sw-cased

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) 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: [Geotrend/bert-base-sw-cased](https://huggingface.co/Geotrend/bert-base-sw-cased) <!-- at revision 7d9ca957a81d2449cf1319af0b91f75f11642336 -->
	- 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: BertModel
	(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("Mollel/MultiLinguSwahili-bert-base-sw-cased-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]
	```

	<!--
	### 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>
	-->

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	### Out-of-Scope Use

	List how the model may foreseeably be misused and address what users ought not to do with the model.
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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 \| 0.6937 \|
	\| spearman_cosine \| 0.6873 \|
	\| pearson_manhattan \| 0.6672 \|
	\| spearman_manhattan \| 0.6578 \|
	\| pearson_euclidean \| 0.6672 \|
	\| spearman_euclidean \| 0.6578 \|
	\| pearson_dot \| 0.5235 \|
	\| spearman_dot \| 0.5126 \|
	\| pearson_max \| 0.6937 \|
	\| spearman_max \| 0.6873 \|

	#### 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 \| 0.6899 \|
	\| spearman_cosine \| 0.6847 \|
	\| pearson_manhattan \| 0.6678 \|
	\| spearman_manhattan \| 0.658 \|
	\| pearson_euclidean \| 0.6673 \|
	\| spearman_euclidean \| 0.6573 \|
	\| pearson_dot \| 0.4953 \|
	\| spearman_dot \| 0.4872 \|
	\| pearson_max \| 0.6899 \|
	\| spearman_max \| 0.6847 \|

	#### 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 \| 0.6873 \|
	\| spearman_cosine \| 0.6817 \|
	\| pearson_manhattan \| 0.6674 \|
	\| spearman_manhattan \| 0.6558 \|
	\| pearson_euclidean \| 0.6675 \|
	\| spearman_euclidean \| 0.656 \|
	\| pearson_dot \| 0.4566 \|
	\| spearman_dot \| 0.4533 \|
	\| pearson_max \| 0.6873 \|
	\| spearman_max \| 0.6817 \|

	#### 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 \| 0.6836 \|
	\| spearman_cosine \| 0.6795 \|
	\| pearson_manhattan \| 0.6664 \|
	\| spearman_manhattan \| 0.6535 \|
	\| pearson_euclidean \| 0.6664 \|
	\| spearman_euclidean \| 0.6537 \|
	\| pearson_dot \| 0.431 \|
	\| spearman_dot \| 0.4315 \|
	\| pearson_max \| 0.6836 \|
	\| spearman_max \| 0.6795 \|

	#### 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 \| 0.6715 \|
	\| spearman_cosine \| 0.6691 \|
	\| pearson_manhattan \| 0.6571 \|
	\| spearman_manhattan \| 0.6456 \|
	\| pearson_euclidean \| 0.6599 \|
	\| spearman_euclidean \| 0.6472 \|
	\| pearson_dot \| 0.3676 \|
	\| spearman_dot \| 0.3678 \|
	\| pearson_max \| 0.6715 \|
	\| spearman_max \| 0.6691 \|

	<!--
	## 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: 9 tokens</li><li>mean: 16.73 tokens</li><li>max: 71 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 19.74 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 19.0 tokens</li><li>max: 49 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: 7 tokens</li><li>mean: 28.25 tokens</li><li>max: 82 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.16 tokens</li><li>max: 55 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 15.55 tokens</li><li>max: 46 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 \| 19.9104 \| - \| - \| - \| - \| - \|
	\| 0.0115 \| 200 \| 15.4038 \| - \| - \| - \| - \| - \|
	\| 0.0172 \| 300 \| 12.4565 \| - \| - \| - \| - \| - \|
	\| 0.0229 \| 400 \| 11.8633 \| - \| - \| - \| - \| - \|
	\| 0.0287 \| 500 \| 11.0601 \| - \| - \| - \| - \| - \|
	\| 0.0344 \| 600 \| 9.7725 \| - \| - \| - \| - \| - \|
	\| 0.0402 \| 700 \| 8.8549 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 800 \| 8.0831 \| - \| - \| - \| - \| - \|
	\| 0.0516 \| 900 \| 7.9941 \| - \| - \| - \| - \| - \|
	\| 0.0574 \| 1000 \| 7.6537 \| - \| - \| - \| - \| - \|
	\| 0.0631 \| 1100 \| 7.9303 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 1200 \| 7.5246 \| - \| - \| - \| - \| - \|
	\| 0.0746 \| 1300 \| 7.7754 \| - \| - \| - \| - \| - \|
	\| 0.0803 \| 1400 \| 7.668 \| - \| - \| - \| - \| - \|
	\| 0.0860 \| 1500 \| 6.7171 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 1600 \| 6.347 \| - \| - \| - \| - \| - \|
	\| 0.0975 \| 1700 \| 6.0 \| - \| - \| - \| - \| - \|
	\| 0.1033 \| 1800 \| 6.4314 \| - \| - \| - \| - \| - \|
	\| 0.1090 \| 1900 \| 6.7947 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 2000 \| 6.9316 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2100 \| 6.6304 \| - \| - \| - \| - \| - \|
	\| 0.1262 \| 2200 \| 6.132 \| - \| - \| - \| - \| - \|
	\| 0.1319 \| 2300 \| 5.8953 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 2400 \| 5.6954 \| - \| - \| - \| - \| - \|
	\| 0.1434 \| 2500 \| 5.6832 \| - \| - \| - \| - \| - \|
	\| 0.1491 \| 2600 \| 5.2266 \| - \| - \| - \| - \| - \|
	\| 0.1549 \| 2700 \| 5.0678 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 2800 \| 5.4733 \| - \| - \| - \| - \| - \|
	\| 0.1664 \| 2900 \| 6.0899 \| - \| - \| - \| - \| - \|
	\| 0.1721 \| 3000 \| 6.332 \| - \| - \| - \| - \| - \|
	\| 0.1778 \| 3100 \| 6.4937 \| - \| - \| - \| - \| - \|
	\| 0.1836 \| 3200 \| 6.2242 \| - \| - \| - \| - \| - \|
	\| 0.1893 \| 3300 \| 5.8023 \| - \| - \| - \| - \| - \|
	\| 0.1950 \| 3400 \| 5.0745 \| - \| - \| - \| - \| - \|
	\| 0.2008 \| 3500 \| 5.5806 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 3600 \| 5.5191 \| - \| - \| - \| - \| - \|
	\| 0.2122 \| 3700 \| 5.3849 \| - \| - \| - \| - \| - \|
	\| 0.2180 \| 3800 \| 5.4828 \| - \| - \| - \| - \| - \|
	\| 0.2237 \| 3900 \| 5.9982 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 4000 \| 5.6842 \| - \| - \| - \| - \| - \|
	\| 0.2352 \| 4100 \| 5.1627 \| - \| - \| - \| - \| - \|
	\| 0.2409 \| 4200 \| 5.154 \| - \| - \| - \| - \| - \|
	\| 0.2467 \| 4300 \| 5.7932 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 4400 \| 5.5758 \| - \| - \| - \| - \| - \|
	\| 0.2581 \| 4500 \| 5.5212 \| - \| - \| - \| - \| - \|
	\| 0.2639 \| 4600 \| 5.5692 \| - \| - \| - \| - \| - \|
	\| 0.2696 \| 4700 \| 5.2699 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 4800 \| 5.4919 \| - \| - \| - \| - \| - \|
	\| 0.2811 \| 4900 \| 5.0754 \| - \| - \| - \| - \| - \|
	\| 0.2868 \| 5000 \| 5.1514 \| - \| - \| - \| - \| - \|
	\| 0.2925 \| 5100 \| 5.0241 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 5200 \| 5.2679 \| - \| - \| - \| - \| - \|
	\| 0.3040 \| 5300 \| 5.3576 \| - \| - \| - \| - \| - \|
	\| 0.3098 \| 5400 \| 5.3454 \| - \| - \| - \| - \| - \|
	\| 0.3155 \| 5500 \| 5.2142 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 5600 \| 4.8418 \| - \| - \| - \| - \| - \|
	\| 0.3270 \| 5700 \| 4.9597 \| - \| - \| - \| - \| - \|
	\| 0.3327 \| 5800 \| 5.1989 \| - \| - \| - \| - \| - \|
	\| 0.3384 \| 5900 \| 5.2624 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 6000 \| 5.0705 \| - \| - \| - \| - \| - \|
	\| 0.3499 \| 6100 \| 5.232 \| - \| - \| - \| - \| - \|
	\| 0.3556 \| 6200 \| 5.2428 \| - \| - \| - \| - \| - \|
	\| 0.3614 \| 6300 \| 4.755 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 6400 \| 4.7266 \| - \| - \| - \| - \| - \|
	\| 0.3729 \| 6500 \| 4.6452 \| - \| - \| - \| - \| - \|
	\| 0.3786 \| 6600 \| 5.1431 \| - \| - \| - \| - \| - \|
	\| 0.3843 \| 6700 \| 4.5343 \| - \| - \| - \| - \| - \|
	\| 0.3901 \| 6800 \| 4.698 \| - \| - \| - \| - \| - \|
	\| 0.3958 \| 6900 \| 4.6944 \| - \| - \| - \| - \| - \|
	\| 0.4015 \| 7000 \| 4.6255 \| - \| - \| - \| - \| - \|
	\| 0.4073 \| 7100 \| 5.0211 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 7200 \| 4.6974 \| - \| - \| - \| - \| - \|
	\| 0.4187 \| 7300 \| 4.9182 \| - \| - \| - \| - \| - \|
	\| 0.4245 \| 7400 \| 4.652 \| - \| - \| - \| - \| - \|
	\| 0.4302 \| 7500 \| 5.1015 \| - \| - \| - \| - \| - \|
	\| 0.4360 \| 7600 \| 4.5249 \| - \| - \| - \| - \| - \|
	\| 0.4417 \| 7700 \| 4.455 \| - \| - \| - \| - \| - \|
	\| 0.4474 \| 7800 \| 4.8153 \| - \| - \| - \| - \| - \|
	\| 0.4532 \| 7900 \| 4.7665 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 8000 \| 4.3413 \| - \| - \| - \| - \| - \|
	\| 0.4646 \| 8100 \| 4.4697 \| - \| - \| - \| - \| - \|
	\| 0.4704 \| 8200 \| 4.6776 \| - \| - \| - \| - \| - \|
	\| 0.4761 \| 8300 \| 4.2868 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 8400 \| 4.7052 \| - \| - \| - \| - \| - \|
	\| 0.4876 \| 8500 \| 4.4721 \| - \| - \| - \| - \| - \|
	\| 0.4933 \| 8600 \| 4.6926 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 8700 \| 4.9891 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 8800 \| 4.4837 \| - \| - \| - \| - \| - \|
	\| 0.5105 \| 8900 \| 4.8127 \| - \| - \| - \| - \| - \|
	\| 0.5163 \| 9000 \| 4.3438 \| - \| - \| - \| - \| - \|
	\| 0.5220 \| 9100 \| 4.4743 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 9200 \| 4.6879 \| - \| - \| - \| - \| - \|
	\| 0.5335 \| 9300 \| 4.3593 \| - \| - \| - \| - \| - \|
	\| 0.5392 \| 9400 \| 4.3023 \| - \| - \| - \| - \| - \|
	\| 0.5449 \| 9500 \| 4.8188 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 9600 \| 4.6142 \| - \| - \| - \| - \| - \|
	\| 0.5564 \| 9700 \| 4.7679 \| - \| - \| - \| - \| - \|
	\| 0.5622 \| 9800 \| 4.6224 \| - \| - \| - \| - \| - \|
	\| 0.5679 \| 9900 \| 4.9154 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 10000 \| 4.7557 \| - \| - \| - \| - \| - \|
	\| 0.5794 \| 10100 \| 4.6395 \| - \| - \| - \| - \| - \|
	\| 0.5851 \| 10200 \| 4.7977 \| - \| - \| - \| - \| - \|
	\| 0.5908 \| 10300 \| 4.915 \| - \| - \| - \| - \| - \|
	\| 0.5966 \| 10400 \| 4.4854 \| - \| - \| - \| - \| - \|
	\| 0.6023 \| 10500 \| 4.3973 \| - \| - \| - \| - \| - \|
	\| 0.6080 \| 10600 \| 4.6964 \| - \| - \| - \| - \| - \|
	\| 0.6138 \| 10700 \| 4.8853 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 10800 \| 4.786 \| - \| - \| - \| - \| - \|
	\| 0.6253 \| 10900 \| 4.5482 \| - \| - \| - \| - \| - \|
	\| 0.6310 \| 11000 \| 4.4857 \| - \| - \| - \| - \| - \|
	\| 0.6367 \| 11100 \| 4.7415 \| - \| - \| - \| - \| - \|
	\| 0.6425 \| 11200 \| 4.2596 \| - \| - \| - \| - \| - \|
	\| 0.6482 \| 11300 \| 4.8578 \| - \| - \| - \| - \| - \|
	\| 0.6539 \| 11400 \| 4.5471 \| - \| - \| - \| - \| - \|
	\| 0.6597 \| 11500 \| 4.8337 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 11600 \| 4.2244 \| - \| - \| - \| - \| - \|
	\| 0.6711 \| 11700 \| 4.9619 \| - \| - \| - \| - \| - \|
	\| 0.6769 \| 11800 \| 4.9369 \| - \| - \| - \| - \| - \|
	\| 0.6826 \| 11900 \| 4.2697 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 12000 \| 4.2711 \| - \| - \| - \| - \| - \|
	\| 0.6941 \| 12100 \| 4.6396 \| - \| - \| - \| - \| - \|
	\| 0.6998 \| 12200 \| 4.5626 \| - \| - \| - \| - \| - \|
	\| 0.7056 \| 12300 \| 4.5767 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 12400 \| 4.6449 \| - \| - \| - \| - \| - \|
	\| 0.7170 \| 12500 \| 4.4217 \| - \| - \| - \| - \| - \|
	\| 0.7228 \| 12600 \| 4.0203 \| - \| - \| - \| - \| - \|
	\| 0.7285 \| 12700 \| 4.5381 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 12800 \| 4.5865 \| - \| - \| - \| - \| - \|
	\| 0.7400 \| 12900 \| 4.4203 \| - \| - \| - \| - \| - \|
	\| 0.7457 \| 13000 \| 4.3761 \| - \| - \| - \| - \| - \|
	\| 0.7514 \| 13100 \| 4.093 \| - \| - \| - \| - \| - \|
	\| 0.7572 \| 13200 \| 5.9235 \| - \| - \| - \| - \| - \|
	\| 0.7629 \| 13300 \| 5.4098 \| - \| - \| - \| - \| - \|
	\| 0.7687 \| 13400 \| 5.3079 \| - \| - \| - \| - \| - \|
	\| 0.7744 \| 13500 \| 5.0946 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 13600 \| 4.7098 \| - \| - \| - \| - \| - \|
	\| 0.7859 \| 13700 \| 4.9471 \| - \| - \| - \| - \| - \|
	\| 0.7916 \| 13800 \| 4.5742 \| - \| - \| - \| - \| - \|
	\| 0.7973 \| 13900 \| 4.6178 \| - \| - \| - \| - \| - \|
	\| 0.8031 \| 14000 \| 4.4516 \| - \| - \| - \| - \| - \|
	\| 0.8088 \| 14100 \| 4.429 \| - \| - \| - \| - \| - \|
	\| 0.8145 \| 14200 \| 4.3812 \| - \| - \| - \| - \| - \|
	\| 0.8203 \| 14300 \| 4.3739 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 14400 \| 4.3821 \| - \| - \| - \| - \| - \|
	\| 0.8318 \| 14500 \| 4.4396 \| - \| - \| - \| - \| - \|
	\| 0.8375 \| 14600 \| 4.2667 \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 14700 \| 4.1963 \| - \| - \| - \| - \| - \|
	\| 0.8490 \| 14800 \| 4.1298 \| - \| - \| - \| - \| - \|
	\| 0.8547 \| 14900 \| 4.1843 \| - \| - \| - \| - \| - \|
	\| 0.8604 \| 15000 \| 4.0735 \| - \| - \| - \| - \| - \|
	\| 0.8662 \| 15100 \| 3.9319 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 15200 \| 4.1544 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 15300 \| 4.105 \| - \| - \| - \| - \| - \|
	\| 0.8834 \| 15400 \| 4.014 \| - \| - \| - \| - \| - \|
	\| 0.8891 \| 15500 \| 4.0345 \| - \| - \| - \| - \| - \|
	\| 0.8949 \| 15600 \| 3.9127 \| - \| - \| - \| - \| - \|
	\| 0.9006 \| 15700 \| 4.1002 \| - \| - \| - \| - \| - \|
	\| 0.9063 \| 15800 \| 3.8564 \| - \| - \| - \| - \| - \|
	\| 0.9121 \| 15900 \| 3.9297 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 16000 \| 3.8487 \| - \| - \| - \| - \| - \|
	\| 0.9235 \| 16100 \| 3.7099 \| - \| - \| - \| - \| - \|
	\| 0.9293 \| 16200 \| 3.8545 \| - \| - \| - \| - \| - \|
	\| 0.9350 \| 16300 \| 3.8122 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 16400 \| 3.8951 \| - \| - \| - \| - \| - \|
	\| 0.9465 \| 16500 \| 3.6996 \| - \| - \| - \| - \| - \|
	\| 0.9522 \| 16600 \| 3.9081 \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 16700 \| 3.8603 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 16800 \| 3.8534 \| - \| - \| - \| - \| - \|
	\| 0.9694 \| 16900 \| 3.8145 \| - \| - \| - \| - \| - \|
	\| 0.9752 \| 17000 \| 3.9858 \| - \| - \| - \| - \| - \|
	\| 0.9809 \| 17100 \| 3.8224 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 17200 \| 3.7469 \| - \| - \| - \| - \| - \|
	\| 0.9924 \| 17300 \| 3.9066 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 17400 \| 3.6754 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 17433 \| - \| 0.6795 \| 0.6817 \| 0.6847 \| 0.6691 \| 0.6873 \|

	</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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