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: BAAI/bge-small-en-v1.5
	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 BAAI/bge-small-en-v1.5
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.6831671531193453
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.677143022633225
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6891948944875336
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6892226446007472
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6916897298195501
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6916850273924392
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6418376172951465
	name: Pearson Dot
	- type: spearman_dot
	value: 0.628581703082033
	name: Spearman Dot
	- type: pearson_max
	value: 0.6916897298195501
	name: Pearson Max
	- type: spearman_max
	value: 0.6916850273924392
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.6753009254241098
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6731049071307844
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6906782473185179
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6927883369656496
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6933649652149252
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.694111832507592
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.600449101550258
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5857671058687308
	name: Spearman Dot
	- type: pearson_max
	value: 0.6933649652149252
	name: Pearson Max
	- type: spearman_max
	value: 0.694111832507592
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6546200020168988
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6523958945855459
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6837289470688535
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6796775815725002
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6861328219241016
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6815842202083926
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5120576666695955
	name: Pearson Dot
	- type: spearman_dot
	value: 0.49141347385563683
	name: Spearman Dot
	- type: pearson_max
	value: 0.6861328219241016
	name: Pearson Max
	- type: spearman_max
	value: 0.6815842202083926
	name: Spearman Max
	---

	# SentenceTransformer based on BAAI/bge-small-en-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) on the Mollel/swahili-n_li-triplet-swh-eng dataset. It maps sentences & paragraphs to a 384-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: [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) <!-- at revision 5c38ec7c405ec4b44b94cc5a9bb96e735b38267a -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 384 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': True}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	)
	```

	## Usage

	### Direct Usage (Sentence Transformers)

	First install the Sentence Transformers library:

	```bash
	pip install -U sentence-transformers
	```

	Then you can load this model and run inference.
	```python
	from sentence_transformers import SentenceTransformer

	# Download from the 🤗 Hub
	model = SentenceTransformer("Mollel/MultiLinguSwahili-bge-small-en-v1.5-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, 384]

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6832 \|
	\| spearman_cosine \| 0.6771 \|
	\| pearson_manhattan \| 0.6892 \|
	\| spearman_manhattan \| 0.6892 \|
	\| pearson_euclidean \| 0.6917 \|
	\| spearman_euclidean \| 0.6917 \|
	\| pearson_dot \| 0.6418 \|
	\| spearman_dot \| 0.6286 \|
	\| pearson_max \| 0.6917 \|
	\| spearman_max \| 0.6917 \|

	#### 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 \| 0.6753 \|
	\| spearman_cosine \| 0.6731 \|
	\| pearson_manhattan \| 0.6907 \|
	\| spearman_manhattan \| 0.6928 \|
	\| pearson_euclidean \| 0.6934 \|
	\| spearman_euclidean \| 0.6941 \|
	\| pearson_dot \| 0.6004 \|
	\| spearman_dot \| 0.5858 \|
	\| pearson_max \| 0.6934 \|
	\| spearman_max \| 0.6941 \|

	#### 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 \| 0.6546 \|
	\| spearman_cosine \| 0.6524 \|
	\| pearson_manhattan \| 0.6837 \|
	\| spearman_manhattan \| 0.6797 \|
	\| pearson_euclidean \| 0.6861 \|
	\| spearman_euclidean \| 0.6816 \|
	\| pearson_dot \| 0.5121 \|
	\| spearman_dot \| 0.4914 \|
	\| pearson_max \| 0.6861 \|
	\| spearman_max \| 0.6816 \|

	<!--
	## Bias, Risks and Limitations

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

	<!--
	### Recommendations

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

	## Training Details

	### Training Dataset

	#### 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: 7 tokens</li><li>mean: 15.18 tokens</li><li>max: 80 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 18.53 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 17.8 tokens</li><li>max: 53 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": [
	256,
	128,
	64
	],
	"matryoshka_weights": [
	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: 6 tokens</li><li>mean: 26.43 tokens</li><li>max: 94 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 13.37 tokens</li><li>max: 65 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.7 tokens</li><li>max: 54 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": [
	256,
	128,
	64
	],
	"matryoshka_weights": [
	1,
	1,
	1
	],
	"n_dims_per_step": -1
	}
	```

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

	- `per_device_train_batch_size`: 64
	- `per_device_eval_batch_size`: 64
	- `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`: 64
	- `per_device_eval_batch_size`: 64
	- `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
	\| Epoch \| Step \| Training Loss \| sts-test-128_spearman_cosine \| sts-test-256_spearman_cosine \| sts-test-64_spearman_cosine \|
	\|:------:\|:----:\|:-------------:\|:----------------------------:\|:----------------------------:\|:---------------------------:\|
	\| 0.0115 \| 100 \| 9.6847 \| - \| - \| - \|
	\| 0.0229 \| 200 \| 8.5336 \| - \| - \| - \|
	\| 0.0344 \| 300 \| 7.768 \| - \| - \| - \|
	\| 0.0459 \| 400 \| 7.2049 \| - \| - \| - \|
	\| 0.0574 \| 500 \| 6.9425 \| - \| - \| - \|
	\| 0.0688 \| 600 \| 7.029 \| - \| - \| - \|
	\| 0.0803 \| 700 \| 6.259 \| - \| - \| - \|
	\| 0.0918 \| 800 \| 6.0939 \| - \| - \| - \|
	\| 0.1032 \| 900 \| 5.991 \| - \| - \| - \|
	\| 0.1147 \| 1000 \| 5.39 \| - \| - \| - \|
	\| 0.1262 \| 1100 \| 5.3214 \| - \| - \| - \|
	\| 0.1377 \| 1200 \| 5.1469 \| - \| - \| - \|
	\| 0.1491 \| 1300 \| 4.901 \| - \| - \| - \|
	\| 0.1606 \| 1400 \| 5.2725 \| - \| - \| - \|
	\| 0.1721 \| 1500 \| 5.077 \| - \| - \| - \|
	\| 0.1835 \| 1600 \| 4.8006 \| - \| - \| - \|
	\| 0.1950 \| 1700 \| 4.5318 \| - \| - \| - \|
	\| 0.2065 \| 1800 \| 4.48 \| - \| - \| - \|
	\| 0.2180 \| 1900 \| 4.5752 \| - \| - \| - \|
	\| 0.2294 \| 2000 \| 4.427 \| - \| - \| - \|
	\| 0.2409 \| 2100 \| 4.4021 \| - \| - \| - \|
	\| 0.2524 \| 2200 \| 4.5903 \| - \| - \| - \|
	\| 0.2639 \| 2300 \| 4.4561 \| - \| - \| - \|
	\| 0.2753 \| 2400 \| 4.372 \| - \| - \| - \|
	\| 0.2868 \| 2500 \| 4.2698 \| - \| - \| - \|
	\| 0.2983 \| 2600 \| 4.3954 \| - \| - \| - \|
	\| 0.3097 \| 2700 \| 4.2697 \| - \| - \| - \|
	\| 0.3212 \| 2800 \| 4.125 \| - \| - \| - \|
	\| 0.3327 \| 2900 \| 4.3611 \| - \| - \| - \|
	\| 0.3442 \| 3000 \| 4.2527 \| - \| - \| - \|
	\| 0.3556 \| 3100 \| 4.1892 \| - \| - \| - \|
	\| 0.3671 \| 3200 \| 4.0417 \| - \| - \| - \|
	\| 0.3786 \| 3300 \| 3.9434 \| - \| - \| - \|
	\| 0.3900 \| 3400 \| 3.9797 \| - \| - \| - \|
	\| 0.4015 \| 3500 \| 3.9611 \| - \| - \| - \|
	\| 0.4130 \| 3600 \| 4.04 \| - \| - \| - \|
	\| 0.4245 \| 3700 \| 3.965 \| - \| - \| - \|
	\| 0.4359 \| 3800 \| 3.778 \| - \| - \| - \|
	\| 0.4474 \| 3900 \| 4.0624 \| - \| - \| - \|
	\| 0.4589 \| 4000 \| 3.8972 \| - \| - \| - \|
	\| 0.4703 \| 4100 \| 3.7882 \| - \| - \| - \|
	\| 0.4818 \| 4200 \| 3.8048 \| - \| - \| - \|
	\| 0.4933 \| 4300 \| 3.9253 \| - \| - \| - \|
	\| 0.5048 \| 4400 \| 3.9832 \| - \| - \| - \|
	\| 0.5162 \| 4500 \| 3.6644 \| - \| - \| - \|
	\| 0.5277 \| 4600 \| 3.7353 \| - \| - \| - \|
	\| 0.5392 \| 4700 \| 3.7768 \| - \| - \| - \|
	\| 0.5506 \| 4800 \| 3.796 \| - \| - \| - \|
	\| 0.5621 \| 4900 \| 3.875 \| - \| - \| - \|
	\| 0.5736 \| 5000 \| 3.7856 \| - \| - \| - \|
	\| 0.5851 \| 5100 \| 3.8898 \| - \| - \| - \|
	\| 0.5965 \| 5200 \| 3.6327 \| - \| - \| - \|
	\| 0.6080 \| 5300 \| 3.7727 \| - \| - \| - \|
	\| 0.6195 \| 5400 \| 3.8582 \| - \| - \| - \|
	\| 0.6310 \| 5500 \| 3.729 \| - \| - \| - \|
	\| 0.6424 \| 5600 \| 3.7088 \| - \| - \| - \|
	\| 0.6539 \| 5700 \| 3.8414 \| - \| - \| - \|
	\| 0.6654 \| 5800 \| 3.7624 \| - \| - \| - \|
	\| 0.6768 \| 5900 \| 3.8816 \| - \| - \| - \|
	\| 0.6883 \| 6000 \| 3.7483 \| - \| - \| - \|
	\| 0.6998 \| 6100 \| 3.7759 \| - \| - \| - \|
	\| 0.7113 \| 6200 \| 3.6674 \| - \| - \| - \|
	\| 0.7227 \| 6300 \| 3.6441 \| - \| - \| - \|
	\| 0.7342 \| 6400 \| 3.7779 \| - \| - \| - \|
	\| 0.7457 \| 6500 \| 3.6691 \| - \| - \| - \|
	\| 0.7571 \| 6600 \| 3.7636 \| - \| - \| - \|
	\| 0.7686 \| 6700 \| 3.7424 \| - \| - \| - \|
	\| 0.7801 \| 6800 \| 3.4943 \| - \| - \| - \|
	\| 0.7916 \| 6900 \| 3.5399 \| - \| - \| - \|
	\| 0.8030 \| 7000 \| 3.3658 \| - \| - \| - \|
	\| 0.8145 \| 7100 \| 3.2856 \| - \| - \| - \|
	\| 0.8260 \| 7200 \| 3.3702 \| - \| - \| - \|
	\| 0.8374 \| 7300 \| 3.3121 \| - \| - \| - \|
	\| 0.8489 \| 7400 \| 3.2322 \| - \| - \| - \|
	\| 0.8604 \| 7500 \| 3.1577 \| - \| - \| - \|
	\| 0.8719 \| 7600 \| 3.1873 \| - \| - \| - \|
	\| 0.8833 \| 7700 \| 3.1492 \| - \| - \| - \|
	\| 0.8948 \| 7800 \| 3.2035 \| - \| - \| - \|
	\| 0.9063 \| 7900 \| 3.1607 \| - \| - \| - \|
	\| 0.9177 \| 8000 \| 3.1557 \| - \| - \| - \|
	\| 0.9292 \| 8100 \| 3.0915 \| - \| - \| - \|
	\| 0.9407 \| 8200 \| 3.1335 \| - \| - \| - \|
	\| 0.9522 \| 8300 \| 3.14 \| - \| - \| - \|
	\| 0.9636 \| 8400 \| 3.1422 \| - \| - \| - \|
	\| 0.9751 \| 8500 \| 3.1923 \| - \| - \| - \|
	\| 0.9866 \| 8600 \| 3.1085 \| - \| - \| - \|
	\| 0.9980 \| 8700 \| 3.089 \| - \| - \| - \|
	\| 1.0 \| 8717 \| - \| 0.6731 \| 0.6771 \| 0.6524 \|


	### 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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