Update model name

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	---
	language:
	- en
	library_name: sentence-transformers
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated
	base_model: microsoft/mpnet-base
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	widget:
	- source_sentence: 'Really? No kidding! '
	sentences:
	- yeah really no kidding
	- At the end of the fourth century was when baked goods flourished.
	- The campaigns seem to reach a new pool of contributors.
	- source_sentence: A sleeping man.
	sentences:
	- Two men are sleeping.
	- Someone is selling oranges
	- the family is young
	- source_sentence: a guy on a bike
	sentences:
	- A tall person on a bike
	- A man is on a frozen lake.
	- The women throw food at the kids
	- source_sentence: yeah really no kidding
	sentences:
	- oh uh-huh well no they wouldn't would they no
	- yeah i mean just when uh the they military paid for her education
	- The campaigns seem to reach a new pool of contributors.
	- source_sentence: He ran like an athlete.
	sentences:
	- ' Then he ran.'
	- yeah i mean just when uh the they military paid for her education
	- Similarly, OIM revised the electronic Grant Renewal Application to accommodate
	new information sought by LSC and to ensure greater ease for users.
	pipeline_tag: sentence-similarity
	co2_eq_emissions:
	emissions: 17.515467907816664
	source: codecarbon
	training_type: fine-tuning
	on_cloud: false
	cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
	ram_total_size: 31.777088165283203
	hours_used: 0.13
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: SentenceTransformer based on microsoft/mpnet-base
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts dev
	type: sts-dev
	metrics:
	- type: pearson_cosine
	value: 0.7331234146933103
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7435439430716654
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.7389474504545281
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7473580293303098
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7356264396007131
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7436137284782617
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7093073700072118
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7150453113301433
	name: Spearman Dot
	- type: pearson_max
	value: 0.7389474504545281
	name: Pearson Max
	- type: spearman_max
	value: 0.7473580293303098
	name: Spearman Max
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.6750510843835755
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.6615639695746663
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.6718085205234632
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.6589482932175834
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.6693170762111229
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.6578210069410166
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.6490291380804283
	name: Pearson Dot
	- type: spearman_dot
	value: 0.6335192601696299
	name: Spearman Dot
	- type: pearson_max
	value: 0.6750510843835755
	name: Pearson Max
	- type: spearman_max
	value: 0.6615639695746663
	name: Spearman Max
	---

	# SentenceTransformer based on microsoft/mpnet-base

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co./microsoft/mpnet-base) on the [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli), [snli](https://huggingface.co./datasets/stanfordnlp/snli) and [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) datasets. 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: [microsoft/mpnet-base](https://huggingface.co./microsoft/mpnet-base)
	- Maximum Sequence Length: 384 tokens
	- Output Dimensionality: 768 tokens
	- Training Datasets:
	- [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli)
	- [snli](https://huggingface.co./datasets/stanfordnlp/snli)
	- [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts)
	- Language: en
	<!-- - 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': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
	(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("tomaarsen/st-v3-test-mpnet-base-allnli-stsb")
	# Run inference
	sentences = [
	"He ran like an athlete.",
	" Then he ran.",
	"yeah i mean just when uh the they military paid for her education",
	]
	embeddings = model.encode(sentences)
	print(embeddings.shape)
	# [3, 768]
	```

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

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7331 \|
	\| spearman_cosine \| 0.7435 \|
	\| pearson_manhattan \| 0.7389 \|
	\| spearman_manhattan \| 0.7474 \|
	\| pearson_euclidean \| 0.7356 \|
	\| spearman_euclidean \| 0.7436 \|
	\| pearson_dot \| 0.7093 \|
	\| spearman_dot \| 0.715 \|
	\| pearson_max \| 0.7389 \|
	\| spearman_max \| 0.7474 \|

	#### Semantic Similarity
	* Dataset: `sts-test`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.6751 \|
	\| spearman_cosine \| 0.6616 \|
	\| pearson_manhattan \| 0.6718 \|
	\| spearman_manhattan \| 0.6589 \|
	\| pearson_euclidean \| 0.6693 \|
	\| spearman_euclidean \| 0.6578 \|
	\| pearson_dot \| 0.649 \|
	\| spearman_dot \| 0.6335 \|
	\| pearson_max \| 0.6751 \|
	\| spearman_max \| 0.6616 \|

	<!--
	## 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 Datasets

	#### multi_nli

	* Dataset: [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co./datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
	* Size: 10,000 training samples
	* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| premise \| hypothesis \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 4 tokens</li><li>mean: 26.95 tokens</li><li>max: 189 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul> \| <ul><li>0: ~34.30%</li><li>1: ~28.20%</li><li>2: ~37.50%</li></ul> \|
	* Samples:
	\| premise \| hypothesis \| label \|
	\|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------\|
	\| <code>Conceptually cream skimming has two basic dimensions - product and geography.</code> \| <code>Product and geography are what make cream skimming work. </code> \| <code>1</code> \|
	\| <code>you know during the season and i guess at at your level uh you lose them to the next level if if they decide to recall the the parent team the Braves decide to call to recall a guy from triple A then a double A guy goes up to replace him and a single A guy goes up to replace him</code> \| <code>You lose the things to the following level if the people recall.</code> \| <code>0</code> \|
	\| <code>One of our number will carry out your instructions minutely.</code> \| <code>A member of my team will execute your orders with immense precision.</code> \| <code>0</code> \|
	* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)

	#### snli

	* Dataset: [snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
	* Size: 10,000 training samples
	* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| snli_premise \| hypothesis \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul> \| <ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul> \| <ul><li>0: ~33.40%</li><li>1: ~33.30%</li><li>2: ~33.30%</li></ul> \|
	* Samples:
	\| snli_premise \| hypothesis \| label \|
	\|:--------------------------------------------------------------------\|:---------------------------------------------------------------\|:---------------\|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is training his horse for a competition.</code> \| <code>1</code> \|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is at a diner, ordering an omelette.</code> \| <code>2</code> \|
	\| <code>A person on a horse jumps over a broken down airplane.</code> \| <code>A person is outdoors, on a horse.</code> \| <code>0</code> \|
	* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)

	#### stsb

	* Dataset: [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co./datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
	* Size: 5,749 training samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:-----------------------------------------------------------\|:----------------------------------------------------------------------\|:------------------\|
	\| <code>A plane is taking off.</code> \| <code>An air plane is taking off.</code> \| <code>1.0</code> \|
	\| <code>A man is playing a large flute.</code> \| <code>A man is playing a flute.</code> \| <code>0.76</code> \|
	\| <code>A man is spreading shreded cheese on a pizza.</code> \| <code>A man is spreading shredded cheese on an uncooked pizza.</code> \| <code>0.76</code> \|
	* Loss: [<code>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

	### Evaluation Datasets

	#### multi_nli

	* Dataset: [multi_nli](https://huggingface.co./datasets/nyu-mll/multi_nli) at [da70db2](https://huggingface.co./datasets/nyu-mll/multi_nli/tree/da70db2af9d09693783c3320c4249840212ee221)
	* Size: 100 evaluation samples
	* Columns: <code>premise</code>, <code>hypothesis</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| premise \| hypothesis \| label \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 27.67 tokens</li><li>max: 138 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 13.48 tokens</li><li>max: 27 tokens</li></ul> \| <ul><li>0: ~35.00%</li><li>1: ~31.00%</li><li>2: ~34.00%</li></ul> \|
	* Samples:
	\| premise \| hypothesis \| label \|
	\|:---------------------------------------------------------------------------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>The new rights are nice enough</code> \| <code>Everyone really likes the newest benefits </code> \| <code>1</code> \|
	\| <code>This site includes a list of all award winners and a searchable database of Government Executive articles.</code> \| <code>The Government Executive articles housed on the website are not able to be searched.</code> \| <code>2</code> \|
	\| <code>uh i don't know i i have mixed emotions about him uh sometimes i like him but at the same times i love to see somebody beat him</code> \| <code>I like him for the most part, but would still enjoy seeing someone beat him.</code> \| <code>0</code> \|
	* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)

	#### snli

	* Dataset: [snli](https://huggingface.co./datasets/stanfordnlp/snli) at [cdb5c3d](https://huggingface.co./datasets/stanfordnlp/snli/tree/cdb5c3d5eed6ead6e5a341c8e56e669bb666725b)
	* Size: 9,842 evaluation samples
	* Columns: <code>snli_premise</code>, <code>hypothesis</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| snli_premise \| hypothesis \| label \|
	\|:--------\|:----------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:-------------------------------------------------------------------\|
	\| type \| string \| string \| int \|
	\| details \| <ul><li>min: 6 tokens</li><li>mean: 18.44 tokens</li><li>max: 57 tokens</li></ul> \| <ul><li>min: 5 tokens</li><li>mean: 10.57 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>0: ~33.10%</li><li>1: ~33.30%</li><li>2: ~33.60%</li></ul> \|
	* Samples:
	\| snli_premise \| hypothesis \| label \|
	\|:-------------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------\|:---------------\|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>The sisters are hugging goodbye while holding to go packages after just eating lunch.</code> \| <code>1</code> \|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>Two woman are holding packages.</code> \| <code>0</code> \|
	\| <code>Two women are embracing while holding to go packages.</code> \| <code>The men are fighting outside a deli.</code> \| <code>2</code> \|
	* Loss: [<code>sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss</code>](https://sbert.net/docs/package_reference/losses.html#softmaxloss)

	#### stsb

	* Dataset: [stsb](https://huggingface.co./datasets/mteb/stsbenchmark-sts) at [8913289](https://huggingface.co./datasets/mteb/stsbenchmark-sts/tree/8913289635987208e6e7c72789e4be2fe94b6abd)
	* Size: 1,500 evaluation samples
	* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| sentence1 \| sentence2 \| label \|
	\|:--------\|:---------------------------------------------------------------------------------\|:----------------------------------------------------------------------------------\|:---------------------------------------------------------------\|
	\| type \| string \| string \| float \|
	\| details \| <ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul> \| <ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul> \| <ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul> \|
	* Samples:
	\| sentence1 \| sentence2 \| label \|
	\|:--------------------------------------------------\|:------------------------------------------------------\|:------------------\|
	\| <code>A man with a hard hat is dancing.</code> \| <code>A man wearing a hard hat is dancing.</code> \| <code>1.0</code> \|
	\| <code>A young child is riding a horse.</code> \| <code>A child is riding a horse.</code> \| <code>0.95</code> \|
	\| <code>A man is feeding a mouse to a snake.</code> \| <code>The man is feeding a mouse to the snake.</code> \| <code>1.0</code> \|
	* Loss: [<code>sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/losses.html#cosinesimilarityloss) with these parameters:
	```json
	{
	"loss_fct": "torch.nn.modules.loss.MSELoss"
	}
	```

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

	- per_device_train_batch_size: 128
	- per_device_eval_batch_size: 128
	- learning_rate: 2e-05
	- num_train_epochs: 1
	- warmup_ratio: 0.1
	- seed: 33
	- bf16: True

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

	- overwrite_output_dir: False
	- do_predict: False
	- prediction_loss_only: False
	- per_device_train_batch_size: 128
	- per_device_eval_batch_size: 128
	- 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: 33
	- 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}
	- 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: None
	- 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
	- optim_target_modules: None
	- round_robin_sampler: False

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| multi nli loss \| snli loss \| stsb loss \| sts-dev spearman cosine \|
	\|:------:\|:----:\|:-------------:\|:--------------:\|:---------:\|:---------:\|:-----------------------:\|
	\| 0.0493 \| 10 \| 0.9199 \| 1.1019 \| 1.1017 \| 0.3016 \| 0.6324 \|
	\| 0.0985 \| 20 \| 1.0063 \| 1.1000 \| 1.0966 \| 0.2635 \| 0.6093 \|
	\| 0.1478 \| 30 \| 1.002 \| 1.0995 \| 1.0908 \| 0.1766 \| 0.5328 \|
	\| 0.1970 \| 40 \| 0.7946 \| 1.0980 \| 1.0913 \| 0.0923 \| 0.5991 \|
	\| 0.2463 \| 50 \| 0.9891 \| 1.0967 \| 1.0781 \| 0.0912 \| 0.6457 \|
	\| 0.2956 \| 60 \| 0.784 \| 1.0938 \| 1.0699 \| 0.0934 \| 0.6629 \|
	\| 0.3448 \| 70 \| 0.6735 \| 1.0940 \| 1.0728 \| 0.0640 \| 0.7538 \|
	\| 0.3941 \| 80 \| 0.7713 \| 1.0893 \| 1.0676 \| 0.0612 \| 0.7653 \|
	\| 0.4433 \| 90 \| 0.9772 \| 1.0870 \| 1.0573 \| 0.0636 \| 0.7621 \|
	\| 0.4926 \| 100 \| 0.8613 \| 1.0862 \| 1.0515 \| 0.0632 \| 0.7583 \|
	\| 0.5419 \| 110 \| 0.7528 \| 1.0814 \| 1.0397 \| 0.0617 \| 0.7536 \|
	\| 0.5911 \| 120 \| 0.6541 \| 1.0854 \| 1.0329 \| 0.0657 \| 0.7512 \|
	\| 0.6404 \| 130 \| 1.051 \| 1.0658 \| 1.0211 \| 0.0607 \| 0.7340 \|
	\| 0.6897 \| 140 \| 0.8516 \| 1.0631 \| 1.0171 \| 0.0587 \| 0.7467 \|
	\| 0.7389 \| 150 \| 0.7484 \| 1.0563 \| 1.0122 \| 0.0556 \| 0.7537 \|
	\| 0.7882 \| 160 \| 0.7368 \| 1.0534 \| 1.0100 \| 0.0588 \| 0.7526 \|
	\| 0.8374 \| 170 \| 0.8373 \| 1.0498 \| 1.0030 \| 0.0565 \| 0.7491 \|
	\| 0.8867 \| 180 \| 0.9311 \| 1.0387 \| 0.9981 \| 0.0588 \| 0.7302 \|
	\| 0.9360 \| 190 \| 0.5445 \| 1.0357 \| 0.9967 \| 0.0565 \| 0.7382 \|
	\| 0.9852 \| 200 \| 0.9154 \| 1.0359 \| 0.9964 \| 0.0556 \| 0.7435 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Carbon Emitted: 0.018 kg of CO2
	- Hours Used: 0.13 hours

	### Training Hardware
	- On Cloud: No
	- GPU Model: 1 x NVIDIA GeForce RTX 3090
	- CPU Model: 13th Gen Intel(R) Core(TM) i7-13700K
	- RAM Size: 31.78 GB

	### Framework Versions
	- Python: 3.11.6
	- Sentence Transformers: 2.7.0.dev0
	- Transformers: 4.39.3
	- PyTorch: 2.1.0+cu121
	- Accelerate: 0.26.1
	- Datasets: 2.18.0
	- Tokenizers: 0.15.2

	## Citation

	### BibTeX

	#### Sentence Transformers and SoftmaxLoss
	```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",
	}
	```

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