Add new SentenceTransformer model.

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	---
	language: []
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- dataset_size:100K<n<1M
	- loss:CachedMultipleNegativesRankingLoss
	base_model: nomic-ai/nomic-embed-text-v1.5
	metrics:
	- cosine_accuracy
	- dot_accuracy
	- manhattan_accuracy
	- euclidean_accuracy
	- max_accuracy
	widget:
	- source_sentence: 'search_query: shark'
	sentences:
	- 'search_query: skull'
	- 'search_query: car picture frame'
	- 'search_query: cartera de guchi'
	- source_sentence: 'search_query: aolvo'
	sentences:
	- 'search_query: laço homem'
	- 'search_query: vdi to hdmi cable'
	- 'search_query: beads without holes'
	- source_sentence: 'search_query: 赤色のカバン'
	sentences:
	- 'search_query: 結婚式ガーランド'
	- 'search_query: remaches zapatero'
	- 'search_query: small feaux potted plants'
	- source_sentence: 'search_query: vipkid'
	sentences:
	- 'search_query: ceiling lamps for kids'
	- 'search_query: apple あいふぉんケース 12'
	- 'search_query: zapatos zaragoza mujer'
	- source_sentence: 'search_query: お布団バッグ'
	sentences:
	- 'search_query: 足なしソファー'
	- 'search_query: all color handbag'
	- 'search_query: tundra black out emblems'
	pipeline_tag: sentence-similarity
	model-index:
	- name: SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5
	results:
	- task:
	type: triplet
	name: Triplet
	dataset:
	name: triplet esci
	type: triplet-esci
	metrics:
	- type: cosine_accuracy
	value: 0.787
	name: Cosine Accuracy
	- type: dot_accuracy
	value: 0.22
	name: Dot Accuracy
	- type: manhattan_accuracy
	value: 0.762
	name: Manhattan Accuracy
	- type: euclidean_accuracy
	value: 0.768
	name: Euclidean Accuracy
	- type: max_accuracy
	value: 0.787
	name: Max Accuracy
	---

	# SentenceTransformer based on nomic-ai/nomic-embed-text-v1.5

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5). 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: [nomic-ai/nomic-embed-text-v1.5](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) <!-- at revision 91d2d6bfdddf0b0da840f901b533e99bae30d757 -->
	- Maximum Sequence Length: 8192 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': 8192, 'do_lower_case': False}) with Transformer model: NomicBertModel
	(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("sentence_transformers_model_id")
	# Run inference
	sentences = [
	'search_query: お布団バッグ',
	'search_query: 足なしソファー',
	'search_query: all color handbag',
	]
	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

	#### Triplet
	* Dataset: `triplet-esci`
	* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:----------\|
	\| cosine_accuracy \| 0.787 \|
	\| dot_accuracy \| 0.22 \|
	\| manhattan_accuracy \| 0.762 \|
	\| euclidean_accuracy \| 0.768 \|
	\| max_accuracy \| 0.787 \|

	<!--
	## 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

	#### Unnamed Dataset


	* Size: 100,000 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: 12.11 tokens</li><li>max: 47 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 49.91 tokens</li><li>max: 166 tokens</li></ul> \| <ul><li>min: 20 tokens</li><li>mean: 50.64 tokens</li><li>max: 152 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>search_query: blー5c</code> \| <code>search_document: [EnergyPower] TECSUN PL-368 電池2個セット SSB・同期検波・長波 [交換用バッテリーBL-5C付] デジタルDSPポケット短波ラジオ超小型長・中波用外付アンテナ 10キーポータブルBCL受信機 FMステレオ/LW/MW/SW ワールドバンドレシーバー 850局プリセットメモリーシグナルメーター USB充電スリープタイマーアラー, TECSUN, PL-368 電池＋セット [ブラック]</code> \| <code>search_document: RADIWOWで作る SIHUADON R108 ポータブル BCL短波ラジオAM FM LW SW 航空無線 DSPレシーバー LCD 良好屋内および屋外アクティビティの両親への贈り物, RADIWOW, グレー</code> \|
	\| <code>search_query: かわいいロングtシャツ</code> \| <code>search_document: レディースロンt 半袖 tシャツオーバーサイズコットンスリット大きいサイズ白シャツビッグシルエットワンピースシャツワンピロングｔシャツおおきいサイズ夏ピンクカジュアルカップ付きカーディガンキラキラキャミソールキャミサテンシンプルシニアシフォンシースルーシ, Sleeping Sheep(スリーピングシープ), ホワイト</code> \| <code>search_document: Perkisboby スポーツウェアレディースヨガウェア 4点セット上下セット 5点セットウェアフィットネス 2点セットジャージスポーツブラパンツパーカー半袖ハーフパンツ, Perkisboby, 2点セット-グレー</code> \|
	\| <code>search_query: iphone xr otterbox symmetry case</code> \| <code>search_document: Symmetry Clear Series Case for iPhone XR (ONLY) Symmetry Case for iPhone XR Symmetry Case - Clear, VTSOU, Clear</code> \| <code>search_document: OtterBox Symmetry Series Case for Apple iPhone XS Max - Tonic Violet / Purple, OtterBox, Tonic Violet / Purple</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### Unnamed Dataset


	* Size: 1,000 evaluation samples
	* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| anchor \| positive \| negative \|
	\|:--------\|:----------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|:------------------------------------------------------------------------------------\|
	\| type \| string \| string \| string \|
	\| details \| <ul><li>min: 7 tokens</li><li>mean: 12.13 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 50.76 tokens</li><li>max: 173 tokens</li></ul> \| <ul><li>min: 18 tokens</li><li>mean: 54.25 tokens</li><li>max: 161 tokens</li></ul> \|
	* Samples:
	\| anchor \| positive \| negative \|
	\|:---------------------------------------------------------------------\|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>search_query: snack vending machine</code> \| <code>search_document: Red All Metal Triple Compartment Commercial Vending Machine for 1 inch Gumballs, 1 inch Toy Capsules, Bouncy Balls, Candy, Nuts with Stand by American Gumball Company, American Gumball Company, CANDY RED</code> \| <code>search_document: Vending Machine Halloween Costume - Funny Snack Food Adult Men & Women Outfits, Hauntlook, Multicolored</code> \|
	\| <code>search_query: slim credit card holder without id window</code> \| <code>search_document: Banuce Top Grain Leather Card Holder for Women Men Unisex ID Credit Card Case Slim Card Wallet Black, Banuce, 1 ID + 5 Card Slots: Black</code> \| <code>search_document: Mens Wallet RFID Genuine Leather Bifold Wallets For Men, ID Window 16 Card Holders Gift Box, Swallowmall, Black Stripe</code> \|
	\| <code>search_query: gucci belts for women</code> \| <code>search_document: Gucci Women's Gg0027o 50Mm Optical Glasses, Gucci, Havana</code> \| <code>search_document: Gucci G-Gucci Gold PVD Women's Watch(Model:YA125511), Gucci, PVD/Brown</code> \|
	* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `per_device_train_batch_size`: 4
	- `per_device_eval_batch_size`: 4
	- `gradient_accumulation_steps`: 2
	- `learning_rate`: 1e-06
	- `lr_scheduler_type`: cosine
	- `warmup_ratio`: 0.1
	- `dataloader_drop_last`: True
	- `dataloader_num_workers`: 4
	- `dataloader_prefetch_factor`: 2
	- `load_best_model_at_end`: 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`: 4
	- `per_device_eval_batch_size`: 4
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 2
	- `eval_accumulation_steps`: None
	- `learning_rate`: 1e-06
	- `weight_decay`: 0.0
	- `adam_beta1`: 0.9
	- `adam_beta2`: 0.999
	- `adam_epsilon`: 1e-08
	- `max_grad_norm`: 1.0
	- `num_train_epochs`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine
	- `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`: False
	- `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`: True
	- `dataloader_num_workers`: 4
	- `dataloader_prefetch_factor`: 2
	- `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}
	- `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
	- `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
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| loss \| triplet-esci_cosine_accuracy \|
	\|:------:\|:-----:\|:-------------:\|:------:\|:----------------------------:\|
	\| 0.008 \| 100 \| 0.7191 \| - \| - \|
	\| 0.016 \| 200 \| 0.6917 \| - \| - \|
	\| 0.024 \| 300 \| 0.7129 \| - \| - \|
	\| 0.032 \| 400 \| 0.6826 \| - \| - \|
	\| 0.04 \| 500 \| 0.7317 \| - \| - \|
	\| 0.048 \| 600 \| 0.7237 \| - \| - \|
	\| 0.056 \| 700 \| 0.6904 \| - \| - \|
	\| 0.064 \| 800 \| 0.6815 \| - \| - \|
	\| 0.072 \| 900 \| 0.6428 \| - \| - \|
	\| 0.08 \| 1000 \| 0.6561 \| 0.6741 \| 0.74 \|
	\| 0.088 \| 1100 \| 0.6097 \| - \| - \|
	\| 0.096 \| 1200 \| 0.6426 \| - \| - \|
	\| 0.104 \| 1300 \| 0.618 \| - \| - \|
	\| 0.112 \| 1400 \| 0.6346 \| - \| - \|
	\| 0.12 \| 1500 \| 0.611 \| - \| - \|
	\| 0.128 \| 1600 \| 0.6092 \| - \| - \|
	\| 0.136 \| 1700 \| 0.6512 \| - \| - \|
	\| 0.144 \| 1800 \| 0.646 \| - \| - \|
	\| 0.152 \| 1900 \| 0.6584 \| - \| - \|
	\| 0.16 \| 2000 \| 0.6403 \| 0.6411 \| 0.747 \|
	\| 0.168 \| 2100 \| 0.5882 \| - \| - \|
	\| 0.176 \| 2200 \| 0.6361 \| - \| - \|
	\| 0.184 \| 2300 \| 0.5641 \| - \| - \|
	\| 0.192 \| 2400 \| 0.5734 \| - \| - \|
	\| 0.2 \| 2500 \| 0.6156 \| - \| - \|
	\| 0.208 \| 2600 \| 0.6252 \| - \| - \|
	\| 0.216 \| 2700 \| 0.634 \| - \| - \|
	\| 0.224 \| 2800 \| 0.5743 \| - \| - \|
	\| 0.232 \| 2900 \| 0.5222 \| - \| - \|
	\| 0.24 \| 3000 \| 0.5604 \| 0.6180 \| 0.765 \|
	\| 0.248 \| 3100 \| 0.5864 \| - \| - \|
	\| 0.256 \| 3200 \| 0.5541 \| - \| - \|
	\| 0.264 \| 3300 \| 0.5661 \| - \| - \|
	\| 0.272 \| 3400 \| 0.5493 \| - \| - \|
	\| 0.28 \| 3500 \| 0.556 \| - \| - \|
	\| 0.288 \| 3600 \| 0.56 \| - \| - \|
	\| 0.296 \| 3700 \| 0.5552 \| - \| - \|
	\| 0.304 \| 3800 \| 0.5833 \| - \| - \|
	\| 0.312 \| 3900 \| 0.5578 \| - \| - \|
	\| 0.32 \| 4000 \| 0.5495 \| 0.6009 \| 0.769 \|
	\| 0.328 \| 4100 \| 0.5245 \| - \| - \|
	\| 0.336 \| 4200 \| 0.477 \| - \| - \|
	\| 0.344 \| 4300 \| 0.5536 \| - \| - \|
	\| 0.352 \| 4400 \| 0.5493 \| - \| - \|
	\| 0.36 \| 4500 \| 0.532 \| - \| - \|
	\| 0.368 \| 4600 \| 0.5341 \| - \| - \|
	\| 0.376 \| 4700 \| 0.528 \| - \| - \|
	\| 0.384 \| 4800 \| 0.5574 \| - \| - \|
	\| 0.392 \| 4900 \| 0.4953 \| - \| - \|
	\| 0.4 \| 5000 \| 0.5365 \| 0.5969 \| 0.779 \|
	\| 0.408 \| 5100 \| 0.4835 \| - \| - \|
	\| 0.416 \| 5200 \| 0.4573 \| - \| - \|
	\| 0.424 \| 5300 \| 0.5554 \| - \| - \|
	\| 0.432 \| 5400 \| 0.5623 \| - \| - \|
	\| 0.44 \| 5500 \| 0.5955 \| - \| - \|
	\| 0.448 \| 5600 \| 0.5086 \| - \| - \|
	\| 0.456 \| 5700 \| 0.5081 \| - \| - \|
	\| 0.464 \| 5800 \| 0.4829 \| - \| - \|
	\| 0.472 \| 5900 \| 0.5066 \| - \| - \|
	\| 0.48 \| 6000 \| 0.4997 \| 0.5920 \| 0.776 \|
	\| 0.488 \| 6100 \| 0.5075 \| - \| - \|
	\| 0.496 \| 6200 \| 0.5051 \| - \| - \|
	\| 0.504 \| 6300 \| 0.5019 \| - \| - \|
	\| 0.512 \| 6400 \| 0.4774 \| - \| - \|
	\| 0.52 \| 6500 \| 0.4975 \| - \| - \|
	\| 0.528 \| 6600 \| 0.4756 \| - \| - \|
	\| 0.536 \| 6700 \| 0.4656 \| - \| - \|
	\| 0.544 \| 6800 \| 0.4671 \| - \| - \|
	\| 0.552 \| 6900 \| 0.4646 \| - \| - \|
	\| 0.56 \| 7000 \| 0.5595 \| 0.5853 \| 0.777 \|
	\| 0.568 \| 7100 \| 0.4812 \| - \| - \|
	\| 0.576 \| 7200 \| 0.506 \| - \| - \|
	\| 0.584 \| 7300 \| 0.49 \| - \| - \|
	\| 0.592 \| 7400 \| 0.464 \| - \| - \|
	\| 0.6 \| 7500 \| 0.441 \| - \| - \|
	\| 0.608 \| 7600 \| 0.4492 \| - \| - \|
	\| 0.616 \| 7700 \| 0.457 \| - \| - \|
	\| 0.624 \| 7800 \| 0.493 \| - \| - \|
	\| 0.632 \| 7900 \| 0.4174 \| - \| - \|
	\| 0.64 \| 8000 \| 0.4686 \| 0.5809 \| 0.785 \|
	\| 0.648 \| 8100 \| 0.4529 \| - \| - \|
	\| 0.656 \| 8200 \| 0.4784 \| - \| - \|
	\| 0.664 \| 8300 \| 0.4697 \| - \| - \|
	\| 0.672 \| 8400 \| 0.4489 \| - \| - \|
	\| 0.68 \| 8500 \| 0.4439 \| - \| - \|
	\| 0.688 \| 8600 \| 0.4063 \| - \| - \|
	\| 0.696 \| 8700 \| 0.4634 \| - \| - \|
	\| 0.704 \| 8800 \| 0.4446 \| - \| - \|
	\| 0.712 \| 8900 \| 0.4725 \| - \| - \|
	\| 0.72 \| 9000 \| 0.3954 \| 0.5769 \| 0.781 \|
	\| 0.728 \| 9100 \| 0.4536 \| - \| - \|
	\| 0.736 \| 9200 \| 0.4583 \| - \| - \|
	\| 0.744 \| 9300 \| 0.4415 \| - \| - \|
	\| 0.752 \| 9400 \| 0.4716 \| - \| - \|
	\| 0.76 \| 9500 \| 0.4393 \| - \| - \|
	\| 0.768 \| 9600 \| 0.4332 \| - \| - \|
	\| 0.776 \| 9700 \| 0.4236 \| - \| - \|
	\| 0.784 \| 9800 \| 0.4021 \| - \| - \|
	\| 0.792 \| 9900 \| 0.4324 \| - \| - \|
	\| 0.8 \| 10000 \| 0.4197 \| 0.5796 \| 0.78 \|
	\| 0.808 \| 10100 \| 0.4576 \| - \| - \|
	\| 0.816 \| 10200 \| 0.4238 \| - \| - \|
	\| 0.824 \| 10300 \| 0.4468 \| - \| - \|
	\| 0.832 \| 10400 \| 0.4301 \| - \| - \|
	\| 0.84 \| 10500 \| 0.414 \| - \| - \|
	\| 0.848 \| 10600 \| 0.4563 \| - \| - \|
	\| 0.856 \| 10700 \| 0.4212 \| - \| - \|
	\| 0.864 \| 10800 \| 0.3905 \| - \| - \|
	\| 0.872 \| 10900 \| 0.4384 \| - \| - \|
	\| 0.88 \| 11000 \| 0.3474 \| 0.5709 \| 0.788 \|
	\| 0.888 \| 11100 \| 0.4396 \| - \| - \|
	\| 0.896 \| 11200 \| 0.3819 \| - \| - \|
	\| 0.904 \| 11300 \| 0.3748 \| - \| - \|
	\| 0.912 \| 11400 \| 0.4217 \| - \| - \|
	\| 0.92 \| 11500 \| 0.3893 \| - \| - \|
	\| 0.928 \| 11600 \| 0.3835 \| - \| - \|
	\| 0.936 \| 11700 \| 0.4303 \| - \| - \|
	\| 0.944 \| 11800 \| 0.4274 \| - \| - \|
	\| 0.952 \| 11900 \| 0.4089 \| - \| - \|
	\| 0.96 \| 12000 \| 0.4009 \| 0.5710 \| 0.786 \|
	\| 0.968 \| 12100 \| 0.3832 \| - \| - \|
	\| 0.976 \| 12200 \| 0.3543 \| - \| - \|
	\| 0.984 \| 12300 \| 0.4866 \| - \| - \|
	\| 0.992 \| 12400 \| 0.4531 \| - \| - \|
	\| 1.0 \| 12500 \| 0.3728 \| - \| - \|
	\| 1.008 \| 12600 \| 0.386 \| - \| - \|
	\| 1.016 \| 12700 \| 0.3622 \| - \| - \|
	\| 1.024 \| 12800 \| 0.4013 \| - \| - \|
	\| 1.032 \| 12900 \| 0.3543 \| - \| - \|
	\| 1.04 \| 13000 \| 0.3918 \| 0.5712 \| 0.792 \|
	\| 1.048 \| 13100 \| 0.3961 \| - \| - \|
	\| 1.056 \| 13200 \| 0.3804 \| - \| - \|
	\| 1.064 \| 13300 \| 0.4049 \| - \| - \|
	\| 1.072 \| 13400 \| 0.3374 \| - \| - \|
	\| 1.08 \| 13500 \| 0.3746 \| - \| - \|
	\| 1.088 \| 13600 \| 0.3162 \| - \| - \|
	\| 1.096 \| 13700 \| 0.3536 \| - \| - \|
	\| 1.104 \| 13800 \| 0.3101 \| - \| - \|
	\| 1.112 \| 13900 \| 0.3704 \| - \| - \|
	\| 1.12 \| 14000 \| 0.3412 \| 0.5758 \| 0.788 \|
	\| 1.1280 \| 14100 \| 0.342 \| - \| - \|
	\| 1.1360 \| 14200 \| 0.383 \| - \| - \|
	\| 1.144 \| 14300 \| 0.3554 \| - \| - \|
	\| 1.152 \| 14400 \| 0.4013 \| - \| - \|
	\| 1.16 \| 14500 \| 0.3486 \| - \| - \|
	\| 1.168 \| 14600 \| 0.3367 \| - \| - \|
	\| 1.176 \| 14700 \| 0.3737 \| - \| - \|
	\| 1.184 \| 14800 \| 0.319 \| - \| - \|
	\| 1.192 \| 14900 \| 0.3211 \| - \| - \|
	\| 1.2 \| 15000 \| 0.3284 \| 0.5804 \| 0.787 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.0.0
	- Transformers: 4.38.2
	- PyTorch: 2.1.2+cu121
	- Accelerate: 0.27.2
	- Datasets: 2.19.1
	- Tokenizers: 0.15.2

	## 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",
	}
	```

	#### CachedMultipleNegativesRankingLoss
	```bibtex
	@misc{gao2021scaling,
	title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
	author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
	year={2021},
	eprint={2101.06983},
	archivePrefix={arXiv},
	primaryClass={cs.LG}
	}
	```

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