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:557850
	- 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: Mwanamume aliyepangwa vizuri anasimama kwa mguu mmoja karibu na
	pwani safi ya bahari.
	sentences:
	- mtu anacheka wakati wa kufua nguo
	- Mwanamume fulani yuko nje karibu na ufuo wa bahari.
	- Mwanamume fulani ameketi kwenye sofa yake.
	- source_sentence: Mwanamume mwenye ngozi nyeusi akivuta sigareti karibu na chombo
	cha taka cha kijani.
	sentences:
	- Karibu na chombo cha taka mwanamume huyo alisimama na kuvuta sigareti
	- Kitanda ni chafu.
	- Alipokuwa kwenye dimbwi la kuogelea mvulana huyo mwenye ugonjwa wa albino alijihadhari
	na jua kupita kiasi
	- source_sentence: Mwanamume kijana mwenye nywele nyekundu anaketi ukutani akisoma
	gazeti huku mwanamke na msichana mchanga wakipita.
	sentences:
	- Mwanamume aliyevalia shati la bluu amegonga ukuta kando ya barabara na gari la
	bluu na gari nyekundu lenye maji nyuma.
	- Mwanamume mchanga anatazama gazeti huku wanawake wawili wakipita karibu naye.
	- Mwanamume huyo mchanga analala huku Mama akimwongoza binti yake kwenye bustani.
	- source_sentence: Wasichana wako nje.
	sentences:
	- Wasichana wawili wakisafiri kwenye sehemu ya kusisimua.
	- Kuna watu watatu wakiongoza gari linaloweza kugeuzwa-geuzwa wakipita watu wengine.
	- Wasichana watatu wamesimama pamoja katika chumba, mmoja anasikiliza, mwingine
	anaandika ukutani na wa tatu anaongea nao.
	- source_sentence: Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso
	chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo
	ya miguu ya benchi.
	sentences:
	- Mwanamume amelala uso chini kwenye benchi ya bustani.
	- Mwanamke anaunganisha uzi katika mipira kando ya rundo la mipira
	- Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.
	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: 0.7113368462970326
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.706531149090894
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7134349154531519
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7023005843725415
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7137962920501839
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7020941994285994
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.3920803758314358
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3601086266312748
	name: Spearman Dot
	- type: pearson_max
	value: 0.7137962920501839
	name: Pearson Max
	- type: spearman_max
	value: 0.706531149090894
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.7090618585285485
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7045766195278508
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7129955390384859
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7021695501159393
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7138697740168334
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7032055408694606
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.39352767760073326
	name: Pearson Dot
	- type: spearman_dot
	value: 0.3628376619678567
	name: Spearman Dot
	- type: pearson_max
	value: 0.7138697740168334
	name: Pearson Max
	- type: spearman_max
	value: 0.7045766195278508
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.7067837420770313
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7044452613349608
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7137425083925593
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7032345257234871
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7146861583047366
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7039212190752775
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.37462153895392747
	name: Pearson Dot
	- type: spearman_dot
	value: 0.34441190254194326
	name: Spearman Dot
	- type: pearson_max
	value: 0.7146861583047366
	name: Pearson Max
	- type: spearman_max
	value: 0.7044452613349608
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.7046839100746249
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7050559450173808
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7120431790616113
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7010054121016321
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7132280398983044
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.701626975970973
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.35455409787695585
	name: Pearson Dot
	- type: spearman_dot
	value: 0.32292034736383524
	name: Spearman Dot
	- type: pearson_max
	value: 0.7132280398983044
	name: Pearson Max
	- type: spearman_max
	value: 0.7050559450173808
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.7012310578605567
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7044132231714119
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7091211798265005
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6972792688781575
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7103033981031003
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6985716335223231
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.3379821887901175
	name: Pearson Dot
	- type: spearman_dot
	value: 0.30513652558145304
	name: Spearman Dot
	- type: pearson_max
	value: 0.7103033981031003
	name: Pearson Max
	- type: spearman_max
	value: 0.7044132231714119
	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). 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: 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': 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("Mollel/swahili-serengeti-E250-nli-matryoshka")
	# Run inference
	sentences = [
	'Mwanamume aliyevalia koti la bluu la kuzuia upepo, amelala uso chini kwenye benchi ya bustani, akiwa na chupa ya pombe iliyofungwa kwenye mojawapo ya miguu ya benchi.',
	'Mwanamume amelala uso chini kwenye benchi ya bustani.',
	'Mwanamume fulani anacheza dansi kwenye klabu hiyo akifungua chupa.',
	]
	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>
	-->

	<!--
	### 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-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 \| 0.7113 \|
	\| spearman_cosine \| 0.7065 \|
	\| pearson_manhattan \| 0.7134 \|
	\| spearman_manhattan \| 0.7023 \|
	\| pearson_euclidean \| 0.7138 \|
	\| spearman_euclidean \| 0.7021 \|
	\| pearson_dot \| 0.3921 \|
	\| spearman_dot \| 0.3601 \|
	\| pearson_max \| 0.7138 \|
	\| spearman_max \| 0.7065 \|

	#### 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 \| 0.7091 \|
	\| spearman_cosine \| 0.7046 \|
	\| pearson_manhattan \| 0.713 \|
	\| spearman_manhattan \| 0.7022 \|
	\| pearson_euclidean \| 0.7139 \|
	\| spearman_euclidean \| 0.7032 \|
	\| pearson_dot \| 0.3935 \|
	\| spearman_dot \| 0.3628 \|
	\| pearson_max \| 0.7139 \|
	\| spearman_max \| 0.7046 \|

	#### 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 \| 0.7068 \|
	\| spearman_cosine \| 0.7044 \|
	\| pearson_manhattan \| 0.7137 \|
	\| spearman_manhattan \| 0.7032 \|
	\| pearson_euclidean \| 0.7147 \|
	\| spearman_euclidean \| 0.7039 \|
	\| pearson_dot \| 0.3746 \|
	\| spearman_dot \| 0.3444 \|
	\| pearson_max \| 0.7147 \|
	\| spearman_max \| 0.7044 \|

	#### 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 \| 0.7047 \|
	\| spearman_cosine \| 0.7051 \|
	\| pearson_manhattan \| 0.712 \|
	\| spearman_manhattan \| 0.701 \|
	\| pearson_euclidean \| 0.7132 \|
	\| spearman_euclidean \| 0.7016 \|
	\| pearson_dot \| 0.3546 \|
	\| spearman_dot \| 0.3229 \|
	\| pearson_max \| 0.7132 \|
	\| spearman_max \| 0.7051 \|

	#### 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 \| 0.7012 \|
	\| spearman_cosine \| 0.7044 \|
	\| pearson_manhattan \| 0.7091 \|
	\| spearman_manhattan \| 0.6973 \|
	\| pearson_euclidean \| 0.7103 \|
	\| spearman_euclidean \| 0.6986 \|
	\| pearson_dot \| 0.338 \|
	\| spearman_dot \| 0.3051 \|
	\| pearson_max \| 0.7103 \|
	\| spearman_max \| 0.7044 \|

	<!--
	## 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 Hyperparameters
	#### Non-Default Hyperparameters

	- `per_device_train_batch_size`: 16
	- `per_device_eval_batch_size`: 16
	- `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`: 16
	- `per_device_eval_batch_size`: 16
	- `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 \| 25.7713 \| - \| - \| - \| - \| - \|
	\| 0.0115 \| 200 \| 20.7886 \| - \| - \| - \| - \| - \|
	\| 0.0172 \| 300 \| 17.0398 \| - \| - \| - \| - \| - \|
	\| 0.0229 \| 400 \| 15.3913 \| - \| - \| - \| - \| - \|
	\| 0.0287 \| 500 \| 14.0214 \| - \| - \| - \| - \| - \|
	\| 0.0344 \| 600 \| 12.2125 \| - \| - \| - \| - \| - \|
	\| 0.0402 \| 700 \| 10.3033 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 800 \| 9.3822 \| - \| - \| - \| - \| - \|
	\| 0.0516 \| 900 \| 8.9276 \| - \| - \| - \| - \| - \|
	\| 0.0574 \| 1000 \| 8.552 \| - \| - \| - \| - \| - \|
	\| 0.0631 \| 1100 \| 8.6293 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 1200 \| 8.5353 \| - \| - \| - \| - \| - \|
	\| 0.0746 \| 1300 \| 8.6431 \| - \| - \| - \| - \| - \|
	\| 0.0803 \| 1400 \| 8.3192 \| - \| - \| - \| - \| - \|
	\| 0.0860 \| 1500 \| 7.1834 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 1600 \| 6.7834 \| - \| - \| - \| - \| - \|
	\| 0.0975 \| 1700 \| 6.4758 \| - \| - \| - \| - \| - \|
	\| 0.1033 \| 1800 \| 6.756 \| - \| - \| - \| - \| - \|
	\| 0.1090 \| 1900 \| 7.807 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 2000 \| 6.8836 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2100 \| 6.9948 \| - \| - \| - \| - \| - \|
	\| 0.1262 \| 2200 \| 6.5031 \| - \| - \| - \| - \| - \|
	\| 0.1319 \| 2300 \| 6.3596 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 2400 \| 6.0257 \| - \| - \| - \| - \| - \|
	\| 0.1434 \| 2500 \| 5.9757 \| - \| - \| - \| - \| - \|
	\| 0.1491 \| 2600 \| 5.464 \| - \| - \| - \| - \| - \|
	\| 0.1549 \| 2700 \| 5.6518 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 2800 \| 6.2899 \| - \| - \| - \| - \| - \|
	\| 0.1664 \| 2900 \| 6.4876 \| - \| - \| - \| - \| - \|
	\| 0.1721 \| 3000 \| 6.9466 \| - \| - \| - \| - \| - \|
	\| 0.1778 \| 3100 \| 6.8439 \| - \| - \| - \| - \| - \|
	\| 0.1836 \| 3200 \| 6.2545 \| - \| - \| - \| - \| - \|
	\| 0.1893 \| 3300 \| 5.9795 \| - \| - \| - \| - \| - \|
	\| 0.1950 \| 3400 \| 5.3904 \| - \| - \| - \| - \| - \|
	\| 0.2008 \| 3500 \| 6.2798 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 3600 \| 5.6882 \| - \| - \| - \| - \| - \|
	\| 0.2122 \| 3700 \| 6.195 \| - \| - \| - \| - \| - \|
	\| 0.2180 \| 3800 \| 5.8728 \| - \| - \| - \| - \| - \|
	\| 0.2237 \| 3900 \| 6.2428 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 4000 \| 5.801 \| - \| - \| - \| - \| - \|
	\| 0.2352 \| 4100 \| 5.6918 \| - \| - \| - \| - \| - \|
	\| 0.2409 \| 4200 \| 5.3977 \| - \| - \| - \| - \| - \|
	\| 0.2467 \| 4300 \| 5.8792 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 4400 \| 5.9297 \| - \| - \| - \| - \| - \|
	\| 0.2581 \| 4500 \| 6.161 \| - \| - \| - \| - \| - \|
	\| 0.2639 \| 4600 \| 5.6571 \| - \| - \| - \| - \| - \|
	\| 0.2696 \| 4700 \| 5.5849 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 4800 \| 5.6382 \| - \| - \| - \| - \| - \|
	\| 0.2811 \| 4900 \| 5.2978 \| - \| - \| - \| - \| - \|
	\| 0.2868 \| 5000 \| 5.108 \| - \| - \| - \| - \| - \|
	\| 0.2925 \| 5100 \| 5.1158 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 5200 \| 5.6218 \| - \| - \| - \| - \| - \|
	\| 0.3040 \| 5300 \| 5.643 \| - \| - \| - \| - \| - \|
	\| 0.3098 \| 5400 \| 5.6894 \| - \| - \| - \| - \| - \|
	\| 0.3155 \| 5500 \| 5.373 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 5600 \| 5.0673 \| - \| - \| - \| - \| - \|
	\| 0.3270 \| 5700 \| 5.1915 \| - \| - \| - \| - \| - \|
	\| 0.3327 \| 5800 \| 5.3705 \| - \| - \| - \| - \| - \|
	\| 0.3384 \| 5900 \| 5.6432 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 6000 \| 5.2567 \| - \| - \| - \| - \| - \|
	\| 0.3499 \| 6100 \| 5.4516 \| - \| - \| - \| - \| - \|
	\| 0.3556 \| 6200 \| 5.4844 \| - \| - \| - \| - \| - \|
	\| 0.3614 \| 6300 \| 4.8238 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 6400 \| 4.8271 \| - \| - \| - \| - \| - \|
	\| 0.3729 \| 6500 \| 4.9863 \| - \| - \| - \| - \| - \|
	\| 0.3786 \| 6600 \| 5.4894 \| - \| - \| - \| - \| - \|
	\| 0.3843 \| 6700 \| 4.95 \| - \| - \| - \| - \| - \|
	\| 0.3901 \| 6800 \| 5.0881 \| - \| - \| - \| - \| - \|
	\| 0.3958 \| 6900 \| 5.249 \| - \| - \| - \| - \| - \|
	\| 0.4015 \| 7000 \| 5.0082 \| - \| - \| - \| - \| - \|
	\| 0.4073 \| 7100 \| 5.5064 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 7200 \| 5.0885 \| - \| - \| - \| - \| - \|
	\| 0.4187 \| 7300 \| 5.0321 \| - \| - \| - \| - \| - \|
	\| 0.4245 \| 7400 \| 4.8212 \| - \| - \| - \| - \| - \|
	\| 0.4302 \| 7500 \| 5.4231 \| - \| - \| - \| - \| - \|
	\| 0.4360 \| 7600 \| 4.7687 \| - \| - \| - \| - \| - \|
	\| 0.4417 \| 7700 \| 4.5707 \| - \| - \| - \| - \| - \|
	\| 0.4474 \| 7800 \| 5.2229 \| - \| - \| - \| - \| - \|
	\| 0.4532 \| 7900 \| 5.2446 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 8000 \| 4.682 \| - \| - \| - \| - \| - \|
	\| 0.4646 \| 8100 \| 4.888 \| - \| - \| - \| - \| - \|
	\| 0.4704 \| 8200 \| 5.0496 \| - \| - \| - \| - \| - \|
	\| 0.4761 \| 8300 \| 4.7089 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 8400 \| 4.9567 \| - \| - \| - \| - \| - \|
	\| 0.4876 \| 8500 \| 4.7913 \| - \| - \| - \| - \| - \|
	\| 0.4933 \| 8600 \| 4.8904 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 8700 \| 5.247 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 8800 \| 4.8254 \| - \| - \| - \| - \| - \|
	\| 0.5105 \| 8900 \| 4.973 \| - \| - \| - \| - \| - \|
	\| 0.5163 \| 9000 \| 4.6657 \| - \| - \| - \| - \| - \|
	\| 0.5220 \| 9100 \| 4.9224 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 9200 \| 4.8163 \| - \| - \| - \| - \| - \|
	\| 0.5335 \| 9300 \| 4.3673 \| - \| - \| - \| - \| - \|
	\| 0.5392 \| 9400 \| 4.6509 \| - \| - \| - \| - \| - \|
	\| 0.5449 \| 9500 \| 5.0667 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 9600 \| 4.8771 \| - \| - \| - \| - \| - \|
	\| 0.5564 \| 9700 \| 5.1056 \| - \| - \| - \| - \| - \|
	\| 0.5622 \| 9800 \| 4.8297 \| - \| - \| - \| - \| - \|
	\| 0.5679 \| 9900 \| 5.0156 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 10000 \| 5.0758 \| - \| - \| - \| - \| - \|
	\| 0.5794 \| 10100 \| 4.9551 \| - \| - \| - \| - \| - \|
	\| 0.5851 \| 10200 \| 4.9594 \| - \| - \| - \| - \| - \|
	\| 0.5908 \| 10300 \| 5.136 \| - \| - \| - \| - \| - \|
	\| 0.5966 \| 10400 \| 4.7873 \| - \| - \| - \| - \| - \|
	\| 0.6023 \| 10500 \| 4.5154 \| - \| - \| - \| - \| - \|
	\| 0.6080 \| 10600 \| 4.928 \| - \| - \| - \| - \| - \|
	\| 0.6138 \| 10700 \| 5.1825 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 10800 \| 5.046 \| - \| - \| - \| - \| - \|
	\| 0.6253 \| 10900 \| 5.0111 \| - \| - \| - \| - \| - \|
	\| 0.6310 \| 11000 \| 4.9458 \| - \| - \| - \| - \| - \|
	\| 0.6367 \| 11100 \| 5.188 \| - \| - \| - \| - \| - \|
	\| 0.6425 \| 11200 \| 4.6219 \| - \| - \| - \| - \| - \|
	\| 0.6482 \| 11300 \| 5.3367 \| - \| - \| - \| - \| - \|
	\| 0.6539 \| 11400 \| 4.9851 \| - \| - \| - \| - \| - \|
	\| 0.6597 \| 11500 \| 5.2068 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 11600 \| 4.3789 \| - \| - \| - \| - \| - \|
	\| 0.6711 \| 11700 \| 5.3533 \| - \| - \| - \| - \| - \|
	\| 0.6769 \| 11800 \| 5.3983 \| - \| - \| - \| - \| - \|
	\| 0.6826 \| 11900 \| 4.6 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 12000 \| 4.6668 \| - \| - \| - \| - \| - \|
	\| 0.6941 \| 12100 \| 5.0814 \| - \| - \| - \| - \| - \|
	\| 0.6998 \| 12200 \| 5.0787 \| - \| - \| - \| - \| - \|
	\| 0.7056 \| 12300 \| 4.6325 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 12400 \| 4.9415 \| - \| - \| - \| - \| - \|
	\| 0.7170 \| 12500 \| 4.7053 \| - \| - \| - \| - \| - \|
	\| 0.7228 \| 12600 \| 4.3212 \| - \| - \| - \| - \| - \|
	\| 0.7285 \| 12700 \| 4.8205 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 12800 \| 4.8602 \| - \| - \| - \| - \| - \|
	\| 0.7400 \| 12900 \| 4.6944 \| - \| - \| - \| - \| - \|
	\| 0.7457 \| 13000 \| 4.7785 \| - \| - \| - \| - \| - \|
	\| 0.7514 \| 13100 \| 4.3515 \| - \| - \| - \| - \| - \|
	\| 0.7572 \| 13200 \| 5.7561 \| - \| - \| - \| - \| - \|
	\| 0.7629 \| 13300 \| 5.3526 \| - \| - \| - \| - \| - \|
	\| 0.7687 \| 13400 \| 5.187 \| - \| - \| - \| - \| - \|
	\| 0.7744 \| 13500 \| 5.0143 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 13600 \| 4.515 \| - \| - \| - \| - \| - \|
	\| 0.7859 \| 13700 \| 4.639 \| - \| - \| - \| - \| - \|
	\| 0.7916 \| 13800 \| 4.5556 \| - \| - \| - \| - \| - \|
	\| 0.7973 \| 13900 \| 4.3526 \| - \| - \| - \| - \| - \|
	\| 0.8031 \| 14000 \| 4.3091 \| - \| - \| - \| - \| - \|
	\| 0.8088 \| 14100 \| 4.1761 \| - \| - \| - \| - \| - \|
	\| 0.8145 \| 14200 \| 4.0484 \| - \| - \| - \| - \| - \|
	\| 0.8203 \| 14300 \| 4.1886 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 14400 \| 4.237 \| - \| - \| - \| - \| - \|
	\| 0.8318 \| 14500 \| 4.2167 \| - \| - \| - \| - \| - \|
	\| 0.8375 \| 14600 \| 4.0329 \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 14700 \| 3.9902 \| - \| - \| - \| - \| - \|
	\| 0.8490 \| 14800 \| 3.8211 \| - \| - \| - \| - \| - \|
	\| 0.8547 \| 14900 \| 4.0048 \| - \| - \| - \| - \| - \|
	\| 0.8604 \| 15000 \| 3.7979 \| - \| - \| - \| - \| - \|
	\| 0.8662 \| 15100 \| 3.8117 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 15200 \| 3.909 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 15300 \| 3.8526 \| - \| - \| - \| - \| - \|
	\| 0.8834 \| 15400 \| 3.79 \| - \| - \| - \| - \| - \|
	\| 0.8891 \| 15500 \| 3.7792 \| - \| - \| - \| - \| - \|
	\| 0.8949 \| 15600 \| 3.7469 \| - \| - \| - \| - \| - \|
	\| 0.9006 \| 15700 \| 3.8387 \| - \| - \| - \| - \| - \|
	\| 0.9063 \| 15800 \| 3.6418 \| - \| - \| - \| - \| - \|
	\| 0.9121 \| 15900 \| 3.645 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 16000 \| 3.4861 \| - \| - \| - \| - \| - \|
	\| 0.9235 \| 16100 \| 3.6416 \| - \| - \| - \| - \| - \|
	\| 0.9293 \| 16200 \| 3.6665 \| - \| - \| - \| - \| - \|
	\| 0.9350 \| 16300 \| 3.6809 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 16400 \| 3.7944 \| - \| - \| - \| - \| - \|
	\| 0.9465 \| 16500 \| 3.6585 \| - \| - \| - \| - \| - \|
	\| 0.9522 \| 16600 \| 3.5398 \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 16700 \| 3.7036 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 16800 \| 3.6386 \| - \| - \| - \| - \| - \|
	\| 0.9694 \| 16900 \| 3.5501 \| - \| - \| - \| - \| - \|
	\| 0.9752 \| 17000 \| 3.7957 \| - \| - \| - \| - \| - \|
	\| 0.9809 \| 17100 \| 3.6076 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 17200 \| 3.4653 \| - \| - \| - \| - \| - \|
	\| 0.9924 \| 17300 \| 3.6768 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 17400 \| 3.49 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 17433 \| - \| 0.7051 \| 0.7044 \| 0.7046 \| 0.7044 \| 0.7065 \|

	</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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