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	---
	language:
	- multilingual
	- zh
	- ja
	- ar
	- ko
	- de
	- fr
	- es
	- pt
	- hi
	- id
	- it
	- tr
	- ru
	- bn
	- ur
	- mr
	- ta
	- vi
	- fa
	- pl
	- uk
	- nl
	- sv
	- he
	- sw
	- ps
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:10K<n<100K
	- loss:CoSENTLoss
	base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: Bottomless Mug
	sentences:
	- You are always safe.
	- That trend isn't very known yet
	- Eleanor Clift göreve koşuyor.
	- source_sentence: Tripp has a job.
	sentences:
	- They are having money problems.
	- Malignite aniden ortaya çıkar.
	- Mezarlar derin ormanlarda saklandı.
	- source_sentence: There are rules
	sentences:
	- There are more villians than heros.
	- The directions should be read.
	- Mezarlar derin ormanlarda saklandı.
	- source_sentence: K is a musician.
	sentences:
	- Klimt draws hotdogs.
	- Ed Wood hiç mahkemeye çıkmadı.
	- Çeçen Rusya yönetimi ele geçirdi.
	- source_sentence: We moved closer.
	sentences:
	- Clinton is unaware of the process.
	- Nesil deneyimleri anlamsızdır.
	- Hormonların etkileri vardır.
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: tr ling
	type: tr_ling
	metrics:
	- type: pearson_cosine
	value: 0.058743115070889876
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.059526247945378225
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.04582145815494953
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.04331287037397966
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.04709170917685587
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.04407504959649961
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.08477622619519222
	name: Pearson Dot
	- type: spearman_dot
	value: 0.08243745050110735
	name: Spearman Dot
	- type: pearson_max
	value: 0.08477622619519222
	name: Pearson Max
	- type: spearman_max
	value: 0.08243745050110735
	name: Spearman Max
	---

	# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) 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: [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
	- Maximum Sequence Length: 128 tokens
	- Output Dimensionality: 384 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7)
	- Languages: multilingual, zh, ja, ar, ko, de, fr, es, pt, hi, id, it, tr, ru, bn, ur, mr, ta, vi, fa, pl, uk, nl, sv, he, sw, ps
	<!-- - 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': 128, 'do_lower_case': False}) with Transformer model: BertModel
	(1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'We moved closer.',
	'Clinton is unaware of the process.',
	'Nesil deneyimleri anlamsızdır.',
	]
	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: `tr_ling`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:-------------------\|:-----------\|
	\| pearson_cosine \| 0.0587 \|
	\| spearman_cosine \| 0.0595 \|
	\| pearson_manhattan \| 0.0458 \|
	\| spearman_manhattan \| 0.0433 \|
	\| pearson_euclidean \| 0.0471 \|
	\| spearman_euclidean \| 0.0441 \|
	\| pearson_dot \| 0.0848 \|
	\| spearman_dot \| 0.0824 \|
	\| pearson_max \| 0.0848 \|
	\| spearman_max \| 0.0824 \|

	<!--
	## 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

	#### MoritzLaurer/multilingual-nli-26lang-2mil7

	* Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538)
	* Size: 25,000 training samples
	* Columns: <code>premise_original</code>, <code>hypothesis_original</code>, <code>score</code>, <code>sentence1</code>, and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| premise_original \| hypothesis_original \| score \| sentence1 \| sentence2 \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| int \| string \| string \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 29.3 tokens</li><li>max: 107 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 15.62 tokens</li><li>max: 40 tokens</li></ul> \| <ul><li>0: ~34.50%</li><li>1: ~33.30%</li><li>2: ~32.20%</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 28.28 tokens</li><li>max: 101 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 15.39 tokens</li><li>max: 38 tokens</li></ul> \|
	* Samples:
	\| premise_original \| hypothesis_original \| score \| sentence1 \| sentence2 \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------\|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>N, the total number of LC50 values used in calculating the CV(%) varied with organism and toxicant because some data were rejected due to water hardness, lack of concentration measurements, and/or because some of the LC50s were not calculable.</code> \| <code>Most discarded data was rejected due to water hardness.</code> \| <code>1</code> \| <code>N, CV'nin hesaplanmasında kullanılan LC50 değerlerinin toplam sayısı (%) organizma ve toksik madde ile çeşitlidir, çünkü bazı veriler su sertliği, konsantrasyon ölçümlerinin eksikliği ve / veya LC50'lerin bazıları hesaplanamaz olduğu için reddedilmiştir.</code> \| <code>Atılan verilerin çoğu su sertliği nedeniyle reddedildi.</code> \|
	\| <code>As the home of the Venus de Milo and Mona Lisa, the Louvre drew almost unmanageable crowds until President Mitterrand ordered its re-organization in the 1980s.</code> \| <code>The Louvre is home of the Venus de Milo and Mona Lisa.</code> \| <code>0</code> \| <code>Venus de Milo ve Mona Lisa'nın evi olarak Louvre, Başkan Mitterrand'ın 1980'lerde yeniden düzenlenmesini emredene kadar neredeyse yönetilemez kalabalıklar çekti.</code> \| <code>Louvre, Venus de Milo ve Mona Lisa'nın evidir.</code> \|
	\| <code>A year ago, the wife of the Oxford don noticed that the pattern on Kleenex quilted tissue uncannily resembled the Penrose Arrowed Rhombi tilings pattern, which Sir Roger had invented--and copyrighted--in 1974.</code> \| <code>It has been recently found out a similarity between the pattern on the recent Kleenex quilted tissue and the one of the Penrose Arrowed Rhombi tilings.</code> \| <code>0</code> \| <code>Bir yıl önce Oxford'un karısı, Kleenex kapitone dokudaki desenin 1974'te Sir Roger'ın icat ettiği -ve telif hakkı olan - Penrose Arrowed Rhombi tilings desenine benzediğini fark etti.</code> \| <code>Yakın zamanda, son Kleenex kapitone dokudaki desen ile Penrose Arrowed Rhombi döşemelerinden biri arasında bir benzerlik bulunmuştur.</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

	### Evaluation Dataset

	#### MoritzLaurer/multilingual-nli-26lang-2mil7

	* Dataset: [MoritzLaurer/multilingual-nli-26lang-2mil7](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7) at [510a233](https://huggingface.co/datasets/MoritzLaurer/multilingual-nli-26lang-2mil7/tree/510a233972a0d7ff0f767d82f46e046832c10538)
	* Size: 5,000 evaluation samples
	* Columns: <code>premise_original</code>, <code>hypothesis_original</code>, <code>score</code>, <code>sentence1</code>, and <code>sentence2</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| premise_original \| hypothesis_original \| score \| sentence1 \| sentence2 \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|
	\| type \| string \| string \| int \| string \| string \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 30.3 tokens</li><li>max: 99 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 56 tokens</li></ul> \| <ul><li>0: ~34.50%</li><li>1: ~29.90%</li><li>2: ~35.60%</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 29.94 tokens</li><li>max: 106 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 15.29 tokens</li><li>max: 52 tokens</li></ul> \|
	* Samples:
	\| premise_original \| hypothesis_original \| score \| sentence1 \| sentence2 \|
	\|:----------------------------------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------\|:---------------\|:------------------------------------------------------------------------------\|:-----------------------------------------------------------------\|
	\| <code>But the racism charge isn't quirky or wacky--it's demagogy.</code> \| <code>The accusation of prejudice based on a pedestrian kind of hatred.</code> \| <code>0</code> \| <code>Ama ırkçılık suçlaması tuhaf ya da tuhaf değil, bu bir demagoji.</code> \| <code>Yaya nefretine dayanan önyargı suçlaması.</code> \|
	\| <code>Why would Gates allow the publication of such a book with his byline and photo on the dust jacket?</code> \| <code>Gates' byline and photo are on the dust jacket</code> \| <code>0</code> \| <code>Gates neden böyle bir kitabın basılmasına izin versin ki?</code> \| <code>Gates'in çizgisi ve fotoğrafı toz ceketin üzerinde.</code> \|
	\| <code>I am a nonsmoker and allergic to cigarette smoke.</code> \| <code>I do not smoke.</code> \| <code>0</code> \| <code>Sigara içmeyen biriyim ve sigara dumanına alerjim var.</code> \| <code>Sigara içmiyorum.</code> \|
	* Loss: [<code>CoSENTLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosentloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "pairwise_cos_sim"
	}
	```

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

	- `eval_strategy`: epoch
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 64
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 5
	- `warmup_ratio`: 0.1
	- `fp16`: True
	- `load_best_model_at_end`: True
	- `ddp_find_unused_parameters`: False

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: epoch
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `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`: 5
	- `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
	- `restore_callback_states_from_checkpoint`: False
	- `no_cuda`: False
	- `use_cpu`: False
	- `use_mps_device`: False
	- `seed`: 42
	- `data_seed`: None
	- `jit_mode_eval`: False
	- `use_ipex`: False
	- `bf16`: False
	- `fp16`: True
	- `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`: True
	- `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, 'non_blocking': False, '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`: False
	- `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_eval_metrics`: False
	- `batch_sampler`: batch_sampler
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| loss \| tr_ling_spearman_max \|
	\|:------:\|:----:\|:-------------:\|:------:\|:--------------------:\|
	\| 0.0320 \| 25 \| 17.17 \| - \| - \|
	\| 0.0639 \| 50 \| 16.4932 \| - \| - \|
	\| 0.0959 \| 75 \| 16.5976 \| - \| - \|
	\| 0.1279 \| 100 \| 15.6991 \| - \| - \|
	\| 0.1598 \| 125 \| 14.876 \| - \| - \|
	\| 0.1918 \| 150 \| 14.4828 \| - \| - \|
	\| 0.2238 \| 175 \| 12.7061 \| - \| - \|
	\| 0.2558 \| 200 \| 10.8687 \| - \| - \|
	\| 0.2877 \| 225 \| 8.3797 \| - \| - \|
	\| 0.3197 \| 250 \| 6.2029 \| - \| - \|
	\| 0.3517 \| 275 \| 5.8228 \| - \| - \|
	\| 0.3836 \| 300 \| 5.811 \| - \| - \|
	\| 0.4156 \| 325 \| 5.8079 \| - \| - \|
	\| 0.4476 \| 350 \| 5.8077 \| - \| - \|
	\| 0.4795 \| 375 \| 5.8035 \| - \| - \|
	\| 0.5115 \| 400 \| 5.8072 \| - \| - \|
	\| 0.5435 \| 425 \| 5.8033 \| - \| - \|
	\| 0.5754 \| 450 \| 5.8086 \| - \| - \|
	\| 0.6074 \| 475 \| 5.81 \| - \| - \|
	\| 0.6394 \| 500 \| 5.7949 \| - \| - \|
	\| 0.6714 \| 525 \| 5.8079 \| - \| - \|
	\| 0.7033 \| 550 \| 5.8057 \| - \| - \|
	\| 0.7353 \| 575 \| 5.8097 \| - \| - \|
	\| 0.7673 \| 600 \| 5.7986 \| - \| - \|
	\| 0.7992 \| 625 \| 5.8051 \| - \| - \|
	\| 0.8312 \| 650 \| 5.8041 \| - \| - \|
	\| 0.8632 \| 675 \| 5.7907 \| - \| - \|
	\| 0.8951 \| 700 \| 5.7991 \| - \| - \|
	\| 0.9271 \| 725 \| 5.8035 \| - \| - \|
	\| 0.9591 \| 750 \| 5.7945 \| - \| - \|
	\| 0.9910 \| 775 \| 5.8077 \| - \| - \|
	\| 1.0 \| 782 \| - \| 5.8024 \| 0.0330 \|
	\| 1.0230 \| 800 \| 5.6703 \| - \| - \|
	\| 1.0550 \| 825 \| 5.8052 \| - \| - \|
	\| 1.0870 \| 850 \| 5.7936 \| - \| - \|
	\| 1.1189 \| 875 \| 5.7924 \| - \| - \|
	\| 1.1509 \| 900 \| 5.7806 \| - \| - \|
	\| 1.1829 \| 925 \| 5.7835 \| - \| - \|
	\| 1.2148 \| 950 \| 5.7619 \| - \| - \|
	\| 1.2468 \| 975 \| 5.8038 \| - \| - \|
	\| 1.2788 \| 1000 \| 5.779 \| - \| - \|
	\| 1.3107 \| 1025 \| 5.7904 \| - \| - \|
	\| 1.3427 \| 1050 \| 5.7696 \| - \| - \|
	\| 1.3747 \| 1075 \| 5.7919 \| - \| - \|
	\| 1.4066 \| 1100 \| 5.7785 \| - \| - \|
	\| 1.4386 \| 1125 \| 5.7862 \| - \| - \|
	\| 1.4706 \| 1150 \| 5.7703 \| - \| - \|
	\| 1.5026 \| 1175 \| 5.773 \| - \| - \|
	\| 1.5345 \| 1200 \| 5.7627 \| - \| - \|
	\| 1.5665 \| 1225 \| 5.7596 \| - \| - \|
	\| 1.5985 \| 1250 \| 5.7882 \| - \| - \|
	\| 1.6304 \| 1275 \| 5.7828 \| - \| - \|
	\| 1.6624 \| 1300 \| 5.771 \| - \| - \|
	\| 1.6944 \| 1325 \| 5.788 \| - \| - \|
	\| 1.7263 \| 1350 \| 5.7719 \| - \| - \|
	\| 1.7583 \| 1375 \| 5.7846 \| - \| - \|
	\| 1.7903 \| 1400 \| 5.7838 \| - \| - \|
	\| 1.8223 \| 1425 \| 5.7912 \| - \| - \|
	\| 1.8542 \| 1450 \| 5.7686 \| - \| - \|
	\| 1.8862 \| 1475 \| 5.7938 \| - \| - \|
	\| 1.9182 \| 1500 \| 5.7847 \| - \| - \|
	\| 1.9501 \| 1525 \| 5.7952 \| - \| - \|
	\| 1.9821 \| 1550 \| 5.7528 \| - \| - \|
	\| 2.0 \| 1564 \| - \| 5.7933 \| 0.0682 \|
	\| 2.0141 \| 1575 \| 5.65 \| - \| - \|
	\| 2.0460 \| 1600 \| 5.7537 \| - \| - \|
	\| 2.0780 \| 1625 \| 5.7098 \| - \| - \|
	\| 2.1100 \| 1650 \| 5.7149 \| - \| - \|
	\| 2.1419 \| 1675 \| 5.7585 \| - \| - \|
	\| 2.1739 \| 1700 \| 5.7277 \| - \| - \|
	\| 2.2059 \| 1725 \| 5.7482 \| - \| - \|
	\| 2.2379 \| 1750 \| 5.7115 \| - \| - \|
	\| 2.2698 \| 1775 \| 5.6895 \| - \| - \|
	\| 2.3018 \| 1800 \| 5.7389 \| - \| - \|
	\| 2.3338 \| 1825 \| 5.7161 \| - \| - \|
	\| 2.3657 \| 1850 \| 5.7123 \| - \| - \|
	\| 2.3977 \| 1875 \| 5.7322 \| - \| - \|
	\| 2.4297 \| 1900 \| 5.7421 \| - \| - \|
	\| 2.4616 \| 1925 \| 5.7615 \| - \| - \|
	\| 2.4936 \| 1950 \| 5.7493 \| - \| - \|
	\| 2.5256 \| 1975 \| 5.7298 \| - \| - \|
	\| 2.5575 \| 2000 \| 5.7529 \| - \| - \|
	\| 2.5895 \| 2025 \| 5.7318 \| - \| - \|
	\| 2.6215 \| 2050 \| 5.7036 \| - \| - \|
	\| 2.6535 \| 2075 \| 5.7158 \| - \| - \|
	\| 2.6854 \| 2100 \| 5.7209 \| - \| - \|
	\| 2.7174 \| 2125 \| 5.738 \| - \| - \|
	\| 2.7494 \| 2150 \| 5.7337 \| - \| - \|
	\| 2.7813 \| 2175 \| 5.713 \| - \| - \|
	\| 2.8133 \| 2200 \| 5.7257 \| - \| - \|
	\| 2.8453 \| 2225 \| 5.6958 \| - \| - \|
	\| 2.8772 \| 2250 \| 5.7053 \| - \| - \|
	\| 2.9092 \| 2275 \| 5.7246 \| - \| - \|
	\| 2.9412 \| 2300 \| 5.7291 \| - \| - \|
	\| 2.9731 \| 2325 \| 5.7139 \| - \| - \|
	\| 3.0 \| 2346 \| - \| 5.8510 \| 0.0837 \|
	\| 3.0051 \| 2350 \| 5.5715 \| - \| - \|
	\| 3.0371 \| 2375 \| 5.6558 \| - \| - \|
	\| 3.0691 \| 2400 \| 5.6441 \| - \| - \|
	\| 3.1010 \| 2425 \| 5.6569 \| - \| - \|
	\| 3.1330 \| 2450 \| 5.669 \| - \| - \|
	\| 3.1650 \| 2475 \| 5.6361 \| - \| - \|
	\| 3.1969 \| 2500 \| 5.6524 \| - \| - \|
	\| 3.2289 \| 2525 \| 5.6773 \| - \| - \|
	\| 3.2609 \| 2550 \| 5.6552 \| - \| - \|
	\| 3.2928 \| 2575 \| 5.6807 \| - \| - \|
	\| 3.3248 \| 2600 \| 5.6638 \| - \| - \|
	\| 3.3568 \| 2625 \| 5.6582 \| - \| - \|
	\| 3.3887 \| 2650 \| 5.658 \| - \| - \|
	\| 3.4207 \| 2675 \| 5.6626 \| - \| - \|
	\| 3.4527 \| 2700 \| 5.6802 \| - \| - \|
	\| 3.4847 \| 2725 \| 5.6377 \| - \| - \|
	\| 3.5166 \| 2750 \| 5.6752 \| - \| - \|
	\| 3.5486 \| 2775 \| 5.6573 \| - \| - \|
	\| 3.5806 \| 2800 \| 5.6963 \| - \| - \|
	\| 3.6125 \| 2825 \| 5.7007 \| - \| - \|
	\| 3.6445 \| 2850 \| 5.6746 \| - \| - \|
	\| 3.6765 \| 2875 \| 5.6312 \| - \| - \|
	\| 3.7084 \| 2900 \| 5.5596 \| - \| - \|
	\| 3.7404 \| 2925 \| 5.7003 \| - \| - \|
	\| 3.7724 \| 2950 \| 5.6739 \| - \| - \|
	\| 3.8043 \| 2975 \| 5.655 \| - \| - \|
	\| 3.8363 \| 3000 \| 5.6787 \| - \| - \|
	\| 3.8683 \| 3025 \| 5.643 \| - \| - \|
	\| 3.9003 \| 3050 \| 5.6412 \| - \| - \|
	\| 3.9322 \| 3075 \| 5.758 \| - \| - \|
	\| 3.9642 \| 3100 \| 5.6769 \| - \| - \|
	\| 3.9962 \| 3125 \| 5.7206 \| - \| - \|
	\| 4.0 \| 3128 \| - \| 5.9125 \| 0.0824 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.0
	- Transformers: 4.41.0
	- PyTorch: 2.3.0+cu121
	- Accelerate: 0.30.1
	- Datasets: 2.19.1
	- 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",
	}
	```

	#### CoSENTLoss
	```bibtex
	@online{kexuefm-8847,
	title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
	author={Su Jianlin},
	year={2022},
	month={Jan},
	url={https://kexue.fm/archives/8847},
	}
	```

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