Add new SentenceTransformer model.

46f3b2b verified 5 months ago

53.3 kB

	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:557850
	- 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: 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 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: 0.6868804546581948
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6801625382694466
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6719079171543956
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6653137984517007
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6734384393604611
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6665812962708187
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5540255947111082
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5399212934179993
	name: Spearman Dot
	- type: pearson_max
	value: 0.6868804546581948
	name: Pearson Max
	- type: spearman_max
	value: 0.6801625382694466
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 512
	type: sts-test-512
	metrics:
	- type: pearson_cosine
	value: 0.6827780698031986
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6770486364807735
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6729437410000495
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6664360018282044
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6738342605019458
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6666791464094138
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.5296210420398023
	name: Pearson Dot
	- type: spearman_dot
	value: 0.5173769714392553
	name: Spearman Dot
	- type: pearson_max
	value: 0.6827780698031986
	name: Pearson Max
	- type: spearman_max
	value: 0.6770486364807735
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 256
	type: sts-test-256
	metrics:
	- type: pearson_cosine
	value: 0.6758051721795716
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6701833115162764
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.671762500960023
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6643430423969034
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6730238156482042
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6649839339725255
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.48923961423508167
	name: Pearson Dot
	- type: spearman_dot
	value: 0.4783312389130331
	name: Spearman Dot
	- type: pearson_max
	value: 0.6758051721795716
	name: Pearson Max
	- type: spearman_max
	value: 0.6701833115162764
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 128
	type: sts-test-128
	metrics:
	- type: pearson_cosine
	value: 0.6700363607439113
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6637709194412489
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6692814840348797
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6594295578885248
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.671006713633375
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6600674238087292
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.45094972472157246
	name: Pearson Dot
	- type: spearman_dot
	value: 0.44023350072779777
	name: Spearman Dot
	- type: pearson_max
	value: 0.671006713633375
	name: Pearson Max
	- type: spearman_max
	value: 0.6637709194412489
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test 64
	type: sts-test-64
	metrics:
	- type: pearson_cosine
	value: 0.6614685875750459
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6556282400518681
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.665261323713716
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6533415018004937
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6671725346980402
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6540012112658994
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.38682442010639634
	name: Pearson Dot
	- type: spearman_dot
	value: 0.37712136401470375
	name: Spearman Dot
	- type: pearson_max
	value: 0.6671725346980402
	name: Pearson Max
	- type: spearman_max
	value: 0.6556282400518681
	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). 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: 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: 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("sartifyllc/swahili-bert-base-sw-cased-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 \| 0.6869 \|
	\| spearman_cosine \| 0.6802 \|
	\| pearson_manhattan \| 0.6719 \|
	\| spearman_manhattan \| 0.6653 \|
	\| pearson_euclidean \| 0.6734 \|
	\| spearman_euclidean \| 0.6666 \|
	\| pearson_dot \| 0.554 \|
	\| spearman_dot \| 0.5399 \|
	\| pearson_max \| 0.6869 \|
	\| spearman_max \| 0.6802 \|

	#### 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 \| 0.6828 \|
	\| spearman_cosine \| 0.677 \|
	\| pearson_manhattan \| 0.6729 \|
	\| spearman_manhattan \| 0.6664 \|
	\| pearson_euclidean \| 0.6738 \|
	\| spearman_euclidean \| 0.6667 \|
	\| pearson_dot \| 0.5296 \|
	\| spearman_dot \| 0.5174 \|
	\| pearson_max \| 0.6828 \|
	\| spearman_max \| 0.677 \|

	#### 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 \| 0.6758 \|
	\| spearman_cosine \| 0.6702 \|
	\| pearson_manhattan \| 0.6718 \|
	\| spearman_manhattan \| 0.6643 \|
	\| pearson_euclidean \| 0.673 \|
	\| spearman_euclidean \| 0.665 \|
	\| pearson_dot \| 0.4892 \|
	\| spearman_dot \| 0.4783 \|
	\| pearson_max \| 0.6758 \|
	\| spearman_max \| 0.6702 \|

	#### 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 \| 0.67 \|
	\| spearman_cosine \| 0.6638 \|
	\| pearson_manhattan \| 0.6693 \|
	\| spearman_manhattan \| 0.6594 \|
	\| pearson_euclidean \| 0.671 \|
	\| spearman_euclidean \| 0.6601 \|
	\| pearson_dot \| 0.4509 \|
	\| spearman_dot \| 0.4402 \|
	\| pearson_max \| 0.671 \|
	\| spearman_max \| 0.6638 \|

	#### 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 \| 0.6615 \|
	\| spearman_cosine \| 0.6556 \|
	\| pearson_manhattan \| 0.6653 \|
	\| spearman_manhattan \| 0.6533 \|
	\| pearson_euclidean \| 0.6672 \|
	\| spearman_euclidean \| 0.654 \|
	\| pearson_dot \| 0.3868 \|
	\| spearman_dot \| 0.3771 \|
	\| pearson_max \| 0.6672 \|
	\| spearman_max \| 0.6556 \|

	<!--
	## 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 \| 20.0932 \| - \| - \| - \| - \| - \|
	\| 0.0115 \| 200 \| 16.2641 \| - \| - \| - \| - \| - \|
	\| 0.0172 \| 300 \| 12.797 \| - \| - \| - \| - \| - \|
	\| 0.0229 \| 400 \| 12.1927 \| - \| - \| - \| - \| - \|
	\| 0.0287 \| 500 \| 11.0423 \| - \| - \| - \| - \| - \|
	\| 0.0344 \| 600 \| 9.676 \| - \| - \| - \| - \| - \|
	\| 0.0402 \| 700 \| 8.1545 \| - \| - \| - \| - \| - \|
	\| 0.0459 \| 800 \| 7.7822 \| - \| - \| - \| - \| - \|
	\| 0.0516 \| 900 \| 7.9352 \| - \| - \| - \| - \| - \|
	\| 0.0574 \| 1000 \| 7.9534 \| - \| - \| - \| - \| - \|
	\| 0.0631 \| 1100 \| 8.1006 \| - \| - \| - \| - \| - \|
	\| 0.0688 \| 1200 \| 7.4767 \| - \| - \| - \| - \| - \|
	\| 0.0746 \| 1300 \| 8.3747 \| - \| - \| - \| - \| - \|
	\| 0.0803 \| 1400 \| 7.7686 \| - \| - \| - \| - \| - \|
	\| 0.0860 \| 1500 \| 6.8076 \| - \| - \| - \| - \| - \|
	\| 0.0918 \| 1600 \| 6.9238 \| - \| - \| - \| - \| - \|
	\| 0.0975 \| 1700 \| 6.5503 \| - \| - \| - \| - \| - \|
	\| 0.1033 \| 1800 \| 6.74 \| - \| - \| - \| - \| - \|
	\| 0.1090 \| 1900 \| 7.7802 \| - \| - \| - \| - \| - \|
	\| 0.1147 \| 2000 \| 7.2594 \| - \| - \| - \| - \| - \|
	\| 0.1205 \| 2100 \| 7.091 \| - \| - \| - \| - \| - \|
	\| 0.1262 \| 2200 \| 6.8677 \| - \| - \| - \| - \| - \|
	\| 0.1319 \| 2300 \| 6.4249 \| - \| - \| - \| - \| - \|
	\| 0.1377 \| 2400 \| 6.1512 \| - \| - \| - \| - \| - \|
	\| 0.1434 \| 2500 \| 5.9714 \| - \| - \| - \| - \| - \|
	\| 0.1491 \| 2600 \| 5.4914 \| - \| - \| - \| - \| - \|
	\| 0.1549 \| 2700 \| 5.5825 \| - \| - \| - \| - \| - \|
	\| 0.1606 \| 2800 \| 5.9456 \| - \| - \| - \| - \| - \|
	\| 0.1664 \| 2900 \| 6.4012 \| - \| - \| - \| - \| - \|
	\| 0.1721 \| 3000 \| 7.1999 \| - \| - \| - \| - \| - \|
	\| 0.1778 \| 3100 \| 6.8254 \| - \| - \| - \| - \| - \|
	\| 0.1836 \| 3200 \| 6.541 \| - \| - \| - \| - \| - \|
	\| 0.1893 \| 3300 \| 6.5411 \| - \| - \| - \| - \| - \|
	\| 0.1950 \| 3400 \| 5.56 \| - \| - \| - \| - \| - \|
	\| 0.2008 \| 3500 \| 6.4692 \| - \| - \| - \| - \| - \|
	\| 0.2065 \| 3600 \| 5.9266 \| - \| - \| - \| - \| - \|
	\| 0.2122 \| 3700 \| 6.2055 \| - \| - \| - \| - \| - \|
	\| 0.2180 \| 3800 \| 6.0835 \| - \| - \| - \| - \| - \|
	\| 0.2237 \| 3900 \| 6.6112 \| - \| - \| - \| - \| - \|
	\| 0.2294 \| 4000 \| 6.3391 \| - \| - \| - \| - \| - \|
	\| 0.2352 \| 4100 \| 5.8379 \| - \| - \| - \| - \| - \|
	\| 0.2409 \| 4200 \| 5.8107 \| - \| - \| - \| - \| - \|
	\| 0.2467 \| 4300 \| 6.1473 \| - \| - \| - \| - \| - \|
	\| 0.2524 \| 4400 \| 6.2827 \| - \| - \| - \| - \| - \|
	\| 0.2581 \| 4500 \| 6.2299 \| - \| - \| - \| - \| - \|
	\| 0.2639 \| 4600 \| 6.1013 \| - \| - \| - \| - \| - \|
	\| 0.2696 \| 4700 \| 5.6491 \| - \| - \| - \| - \| - \|
	\| 0.2753 \| 4800 \| 5.8641 \| - \| - \| - \| - \| - \|
	\| 0.2811 \| 4900 \| 5.4278 \| - \| - \| - \| - \| - \|
	\| 0.2868 \| 5000 \| 5.7304 \| - \| - \| - \| - \| - \|
	\| 0.2925 \| 5100 \| 5.4652 \| - \| - \| - \| - \| - \|
	\| 0.2983 \| 5200 \| 5.9031 \| - \| - \| - \| - \| - \|
	\| 0.3040 \| 5300 \| 6.1014 \| - \| - \| - \| - \| - \|
	\| 0.3098 \| 5400 \| 5.9282 \| - \| - \| - \| - \| - \|
	\| 0.3155 \| 5500 \| 5.6618 \| - \| - \| - \| - \| - \|
	\| 0.3212 \| 5600 \| 5.3803 \| - \| - \| - \| - \| - \|
	\| 0.3270 \| 5700 \| 5.5759 \| - \| - \| - \| - \| - \|
	\| 0.3327 \| 5800 \| 5.6936 \| - \| - \| - \| - \| - \|
	\| 0.3384 \| 5900 \| 5.7249 \| - \| - \| - \| - \| - \|
	\| 0.3442 \| 6000 \| 5.5926 \| - \| - \| - \| - \| - \|
	\| 0.3499 \| 6100 \| 5.6329 \| - \| - \| - \| - \| - \|
	\| 0.3556 \| 6200 \| 5.7456 \| - \| - \| - \| - \| - \|
	\| 0.3614 \| 6300 \| 5.1638 \| - \| - \| - \| - \| - \|
	\| 0.3671 \| 6400 \| 5.3258 \| - \| - \| - \| - \| - \|
	\| 0.3729 \| 6500 \| 5.1216 \| - \| - \| - \| - \| - \|
	\| 0.3786 \| 6600 \| 5.7453 \| - \| - \| - \| - \| - \|
	\| 0.3843 \| 6700 \| 4.9906 \| - \| - \| - \| - \| - \|
	\| 0.3901 \| 6800 \| 5.1126 \| - \| - \| - \| - \| - \|
	\| 0.3958 \| 6900 \| 5.2389 \| - \| - \| - \| - \| - \|
	\| 0.4015 \| 7000 \| 5.1483 \| - \| - \| - \| - \| - \|
	\| 0.4073 \| 7100 \| 5.6072 \| - \| - \| - \| - \| - \|
	\| 0.4130 \| 7200 \| 5.2018 \| - \| - \| - \| - \| - \|
	\| 0.4187 \| 7300 \| 5.4083 \| - \| - \| - \| - \| - \|
	\| 0.4245 \| 7400 \| 5.1995 \| - \| - \| - \| - \| - \|
	\| 0.4302 \| 7500 \| 5.5787 \| - \| - \| - \| - \| - \|
	\| 0.4360 \| 7600 \| 4.9942 \| - \| - \| - \| - \| - \|
	\| 0.4417 \| 7700 \| 4.9196 \| - \| - \| - \| - \| - \|
	\| 0.4474 \| 7800 \| 5.3938 \| - \| - \| - \| - \| - \|
	\| 0.4532 \| 7900 \| 5.381 \| - \| - \| - \| - \| - \|
	\| 0.4589 \| 8000 \| 4.908 \| - \| - \| - \| - \| - \|
	\| 0.4646 \| 8100 \| 4.8871 \| - \| - \| - \| - \| - \|
	\| 0.4704 \| 8200 \| 5.2298 \| - \| - \| - \| - \| - \|
	\| 0.4761 \| 8300 \| 4.6157 \| - \| - \| - \| - \| - \|
	\| 0.4818 \| 8400 \| 5.0344 \| - \| - \| - \| - \| - \|
	\| 0.4876 \| 8500 \| 5.0713 \| - \| - \| - \| - \| - \|
	\| 0.4933 \| 8600 \| 5.1952 \| - \| - \| - \| - \| - \|
	\| 0.4991 \| 8700 \| 5.5352 \| - \| - \| - \| - \| - \|
	\| 0.5048 \| 8800 \| 5.1556 \| - \| - \| - \| - \| - \|
	\| 0.5105 \| 8900 \| 5.2318 \| - \| - \| - \| - \| - \|
	\| 0.5163 \| 9000 \| 4.7887 \| - \| - \| - \| - \| - \|
	\| 0.5220 \| 9100 \| 4.868 \| - \| - \| - \| - \| - \|
	\| 0.5277 \| 9200 \| 4.9544 \| - \| - \| - \| - \| - \|
	\| 0.5335 \| 9300 \| 4.816 \| - \| - \| - \| - \| - \|
	\| 0.5392 \| 9400 \| 4.8374 \| - \| - \| - \| - \| - \|
	\| 0.5449 \| 9500 \| 5.3242 \| - \| - \| - \| - \| - \|
	\| 0.5507 \| 9600 \| 4.9039 \| - \| - \| - \| - \| - \|
	\| 0.5564 \| 9700 \| 5.2907 \| - \| - \| - \| - \| - \|
	\| 0.5622 \| 9800 \| 5.4007 \| - \| - \| - \| - \| - \|
	\| 0.5679 \| 9900 \| 5.3016 \| - \| - \| - \| - \| - \|
	\| 0.5736 \| 10000 \| 5.3235 \| - \| - \| - \| - \| - \|
	\| 0.5794 \| 10100 \| 5.1566 \| - \| - \| - \| - \| - \|
	\| 0.5851 \| 10200 \| 5.1348 \| - \| - \| - \| - \| - \|
	\| 0.5908 \| 10300 \| 5.4583 \| - \| - \| - \| - \| - \|
	\| 0.5966 \| 10400 \| 4.9528 \| - \| - \| - \| - \| - \|
	\| 0.6023 \| 10500 \| 5.0073 \| - \| - \| - \| - \| - \|
	\| 0.6080 \| 10600 \| 5.0324 \| - \| - \| - \| - \| - \|
	\| 0.6138 \| 10700 \| 5.4107 \| - \| - \| - \| - \| - \|
	\| 0.6195 \| 10800 \| 5.3643 \| - \| - \| - \| - \| - \|
	\| 0.6253 \| 10900 \| 5.1267 \| - \| - \| - \| - \| - \|
	\| 0.6310 \| 11000 \| 5.0443 \| - \| - \| - \| - \| - \|
	\| 0.6367 \| 11100 \| 5.2001 \| - \| - \| - \| - \| - \|
	\| 0.6425 \| 11200 \| 4.8813 \| - \| - \| - \| - \| - \|
	\| 0.6482 \| 11300 \| 5.4734 \| - \| - \| - \| - \| - \|
	\| 0.6539 \| 11400 \| 5.0344 \| - \| - \| - \| - \| - \|
	\| 0.6597 \| 11500 \| 5.5043 \| - \| - \| - \| - \| - \|
	\| 0.6654 \| 11600 \| 4.6201 \| - \| - \| - \| - \| - \|
	\| 0.6711 \| 11700 \| 5.4626 \| - \| - \| - \| - \| - \|
	\| 0.6769 \| 11800 \| 5.3813 \| - \| - \| - \| - \| - \|
	\| 0.6826 \| 11900 \| 4.626 \| - \| - \| - \| - \| - \|
	\| 0.6883 \| 12000 \| 4.87 \| - \| - \| - \| - \| - \|
	\| 0.6941 \| 12100 \| 5.0015 \| - \| - \| - \| - \| - \|
	\| 0.6998 \| 12200 \| 4.962 \| - \| - \| - \| - \| - \|
	\| 0.7056 \| 12300 \| 5.1613 \| - \| - \| - \| - \| - \|
	\| 0.7113 \| 12400 \| 5.2074 \| - \| - \| - \| - \| - \|
	\| 0.7170 \| 12500 \| 4.958 \| - \| - \| - \| - \| - \|
	\| 0.7228 \| 12600 \| 4.4516 \| - \| - \| - \| - \| - \|
	\| 0.7285 \| 12700 \| 4.8421 \| - \| - \| - \| - \| - \|
	\| 0.7342 \| 12800 \| 4.9242 \| - \| - \| - \| - \| - \|
	\| 0.7400 \| 12900 \| 4.9256 \| - \| - \| - \| - \| - \|
	\| 0.7457 \| 13000 \| 4.8254 \| - \| - \| - \| - \| - \|
	\| 0.7514 \| 13100 \| 4.5114 \| - \| - \| - \| - \| - \|
	\| 0.7572 \| 13200 \| 7.7118 \| - \| - \| - \| - \| - \|
	\| 0.7629 \| 13300 \| 7.0822 \| - \| - \| - \| - \| - \|
	\| 0.7687 \| 13400 \| 6.8022 \| - \| - \| - \| - \| - \|
	\| 0.7744 \| 13500 \| 6.7295 \| - \| - \| - \| - \| - \|
	\| 0.7801 \| 13600 \| 6.0547 \| - \| - \| - \| - \| - \|
	\| 0.7859 \| 13700 \| 6.5285 \| - \| - \| - \| - \| - \|
	\| 0.7916 \| 13800 \| 6.2666 \| - \| - \| - \| - \| - \|
	\| 0.7973 \| 13900 \| 6.1031 \| - \| - \| - \| - \| - \|
	\| 0.8031 \| 14000 \| 5.9138 \| - \| - \| - \| - \| - \|
	\| 0.8088 \| 14100 \| 5.6636 \| - \| - \| - \| - \| - \|
	\| 0.8145 \| 14200 \| 5.7073 \| - \| - \| - \| - \| - \|
	\| 0.8203 \| 14300 \| 5.7963 \| - \| - \| - \| - \| - \|
	\| 0.8260 \| 14400 \| 5.7336 \| - \| - \| - \| - \| - \|
	\| 0.8318 \| 14500 \| 5.8113 \| - \| - \| - \| - \| - \|
	\| 0.8375 \| 14600 \| 5.6708 \| - \| - \| - \| - \| - \|
	\| 0.8432 \| 14700 \| 5.4565 \| - \| - \| - \| - \| - \|
	\| 0.8490 \| 14800 \| 5.4293 \| - \| - \| - \| - \| - \|
	\| 0.8547 \| 14900 \| 5.4166 \| - \| - \| - \| - \| - \|
	\| 0.8604 \| 15000 \| 5.3616 \| - \| - \| - \| - \| - \|
	\| 0.8662 \| 15100 \| 5.1579 \| - \| - \| - \| - \| - \|
	\| 0.8719 \| 15200 \| 5.3887 \| - \| - \| - \| - \| - \|
	\| 0.8776 \| 15300 \| 5.346 \| - \| - \| - \| - \| - \|
	\| 0.8834 \| 15400 \| 5.2762 \| - \| - \| - \| - \| - \|
	\| 0.8891 \| 15500 \| 5.3417 \| - \| - \| - \| - \| - \|
	\| 0.8949 \| 15600 \| 5.1607 \| - \| - \| - \| - \| - \|
	\| 0.9006 \| 15700 \| 5.4493 \| - \| - \| - \| - \| - \|
	\| 0.9063 \| 15800 \| 5.0268 \| - \| - \| - \| - \| - \|
	\| 0.9121 \| 15900 \| 5.0612 \| - \| - \| - \| - \| - \|
	\| 0.9178 \| 16000 \| 5.1471 \| - \| - \| - \| - \| - \|
	\| 0.9235 \| 16100 \| 4.8275 \| - \| - \| - \| - \| - \|
	\| 0.9293 \| 16200 \| 5.1464 \| - \| - \| - \| - \| - \|
	\| 0.9350 \| 16300 \| 4.958 \| - \| - \| - \| - \| - \|
	\| 0.9407 \| 16400 \| 5.1968 \| - \| - \| - \| - \| - \|
	\| 0.9465 \| 16500 \| 4.7783 \| - \| - \| - \| - \| - \|
	\| 0.9522 \| 16600 \| 5.0834 \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 16700 \| 4.9839 \| - \| - \| - \| - \| - \|
	\| 0.9637 \| 16800 \| 5.0078 \| - \| - \| - \| - \| - \|
	\| 0.9694 \| 16900 \| 5.1624 \| - \| - \| - \| - \| - \|
	\| 0.9752 \| 17000 \| 5.2132 \| - \| - \| - \| - \| - \|
	\| 0.9809 \| 17100 \| 4.9741 \| - \| - \| - \| - \| - \|
	\| 0.9866 \| 17200 \| 4.96 \| - \| - \| - \| - \| - \|
	\| 0.9924 \| 17300 \| 5.1834 \| - \| - \| - \| - \| - \|
	\| 0.9981 \| 17400 \| 4.8955 \| - \| - \| - \| - \| - \|
	\| 1.0 \| 17433 \| - \| 0.6638 \| 0.6702 \| 0.6770 \| 0.6556 \| 0.6802 \|

	</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}
	}
	```

	<!--
	## Glossary

	Clearly define terms in order to be accessible across audiences.
	-->

	<!--
	## Model Card Authors

	Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.
	-->

	<!--
	## Model Card Contact

	Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.
	-->