Cached Gist embedd loss

a86524d verified 2 months ago

105 kB

	---
	base_model: microsoft/deberta-v3-small
	datasets:
	- tals/vitaminc
	language:
	- en
	library_name: sentence-transformers
	metrics:
	- pearson_cosine
	- spearman_cosine
	- pearson_manhattan
	- spearman_manhattan
	- pearson_euclidean
	- spearman_euclidean
	- pearson_dot
	- spearman_dot
	- pearson_max
	- spearman_max
	- cosine_accuracy
	- cosine_accuracy_threshold
	- cosine_f1
	- cosine_f1_threshold
	- cosine_precision
	- cosine_recall
	- cosine_ap
	- dot_accuracy
	- dot_accuracy_threshold
	- dot_f1
	- dot_f1_threshold
	- dot_precision
	- dot_recall
	- dot_ap
	- manhattan_accuracy
	- manhattan_accuracy_threshold
	- manhattan_f1
	- manhattan_f1_threshold
	- manhattan_precision
	- manhattan_recall
	- manhattan_ap
	- euclidean_accuracy
	- euclidean_accuracy_threshold
	- euclidean_f1
	- euclidean_f1_threshold
	- euclidean_precision
	- euclidean_recall
	- euclidean_ap
	- max_accuracy
	- max_accuracy_threshold
	- max_f1
	- max_f1_threshold
	- max_precision
	- max_recall
	- max_ap
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:225247
	- loss:CachedGISTEmbedLoss
	widget:
	- source_sentence: what is exfo toolbox
	sentences:
	- Eye dilation from eye drops used for examination of the eye usually lasts from
	4 to 24 hours, depending upon the strength of the drop and upon the individual
	patient.
	- Garden Grove is a city in northern Orange County in the U.S. state of California,
	34 miles (55 km) south of Los Angeles. The population was 170,883 at the 2010
	United States Census. State Route 22, also known as the Garden Grove Freeway,
	passes through the city in an east-west direction.
	- EXFO ToolBox Office is a product that offers you a collection of viewers and analyzers.
	It enables you to manage and analyze results acquired from fiber optic test modules
	and instruments.
	- source_sentence: More than 273 people have died from the 2019-20 coronavirus outside
	mainland China .
	sentences:
	- 'More than 3,700 people have died : around 3,100 in mainland China and around
	550 in all other countries combined .'
	- 'More than 3,200 people have died : almost 3,000 in mainland China and around
	275 in other countries .'
	- more than 4,900 deaths have been attributed to COVID-19 .
	- source_sentence: Ultrasound, a diagnostic technology, uses high-frequency vibrations
	transmitted into any tissue in contact with the transducer.
	sentences:
	- What diagnostic technology uses high-frequency vibrations transmitted into any
	tissue in contact with the transducer?
	- The abnormal cells cannot carry oxygen properly and can get stuck where?
	- What type of organism is a bacteria?
	- source_sentence: When you add moles of gas to a baloon by blowing it up, the volume
	increases.
	sentences:
	- What shape is the lens of the eye?
	- What happens to the volume of a balloon when you add moles of gas to it by blowing
	up?
	- Most turtle bodies are covered by a special bony or cartilaginous shell developed
	from their what?
	- source_sentence: What was the name of eleven rulers of the 19th and 20th Egyptian
	dynasties?
	sentences:
	- 'Airlines Yugoslavia 1968 - 1968 Renamed ^ Comments : Aviogenex was formed on
	21May1968 as Genex Airlines. Restarted under current name on 30Apr1969 & liquidated
	in Feb2015 ^ Genealogy : Genex Airlines >Aviogenex 1968 - 1986 Renamed ^ Comments
	: Adria Airways was formed on 14Mar1961 & operations started on 30Jun1961 as Adria
	Airways, renamed to Inex in 1968 and back to Adria again in 1986. National airline
	of Slovenia ^ Genealogy : Adria Airways >Inex Adria Airways >Adria Airways JAT
	(Jugoslovenski Aerotransport) 1947 - 2003 Renamed ^ Comments : Air Serbia was
	founded as Aeroput on 17Jun1927, renamed to JAT on 01Apr1947. Started ops on 15Apr1947,
	Renamed again on 08Aug2003 to JAT Airways & reformed as Air Serbia on 26Oct2013
	^ Genealogy : Aeroput >JAT (Jugoslovenski Aerotransport) >JAT Airways >Air Serbia
	Jugoslovenski Aerotransport'
	- List of Rulers of Ancient Egypt and Nubia \| Lists of Rulers \| Heilbrunn Timeline
	of Art History \| The Metropolitan Museum of Art The Metropolitan Museum of Art
	List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our
	knowledge of the succession of Egyptian kings is based on kinglists kept by the
	ancient Egyptians themselves. The most famous are the Palermo Stone, which covers
	the period from the earliest dynasties to the middle of Dynasty 5; the Abydos
	Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon,
	a papyrus that covers the period from the earliest dynasties to the reign of Ramesses
	II. All are incomplete or fragmentary. We also rely on the History of Egypt written
	by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho
	had access to many original sources and it was he who divided the kings into the
	thirty dynasties we use today. It is to this structure of dynasties and listed
	kings that we now attempt to link an absolute chronology of dates in terms of
	our own calendrical system. The process is made difficult by the fragmentary condition
	of the kinglists and by differences in the calendrical years used at various times.
	Some astronomical observations from the ancient Egyptians have survived, allowing
	us to calculate absolute dates within a margin of error. Synchronisms with the
	other civilizations of the ancient world are also of limited use.
	- 'What is the "Jack Sprat" nursery rhyme? \| Reference.com What is the "Jack Sprat"
	nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme
	whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean.
	And so between them both, you see, they licked the platter clean." Though it was
	likely sung by children long before, "Jack Sprat" was first published around 1765
	in the compilation "Mother Goose''s Melody." Full Answer According to Rhymes.org,
	a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery
	rhyme has its origins in British history. In one interpretation, Jack Sprat was
	King Charles I, who ruled England in the early part of the 17th century, and his
	wife was Queen Henrietta Maria. Parliament refused to finance the king''s war
	with Spain, which made him lean. However, the queen fattened the coffers by levying
	an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme
	is linked to King Richard and his brother John of the Robin Hood legend. Jack
	Sprat was King John, the usurper who tried to take over the crown when King Richard
	went off to fight in the Crusades in the 12th century. When King Richard was captured,
	John had to raise a ransom to rescue him, leaving the country lean. The wife was
	Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However,
	after King Richard died and John became king, he had his marriage with Joan annulled.'
	model-index:
	- name: SentenceTransformer based on microsoft/deberta-v3-small
	results:
	- task:
	type: semantic-similarity
	name: Semantic Similarity
	dataset:
	name: sts test
	type: sts-test
	metrics:
	- type: pearson_cosine
	value: 0.7673854808079448
	name: Pearson Cosine
	- type: spearman_cosine
	value: 0.7776198286738142
	name: Spearman Cosine
	- type: pearson_manhattan
	value: 0.782368447545155
	name: Pearson Manhattan
	- type: spearman_manhattan
	value: 0.7720687033298573
	name: Spearman Manhattan
	- type: pearson_euclidean
	value: 0.7882638792170585
	name: Pearson Euclidean
	- type: spearman_euclidean
	value: 0.7775073687564514
	name: Spearman Euclidean
	- type: pearson_dot
	value: 0.7669147371310585
	name: Pearson Dot
	- type: spearman_dot
	value: 0.7762894632049069
	name: Spearman Dot
	- type: pearson_max
	value: 0.7882638792170585
	name: Pearson Max
	- type: spearman_max
	value: 0.7776198286738142
	name: Spearman Max
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: allNLI dev
	type: allNLI-dev
	metrics:
	- type: cosine_accuracy
	value: 0.708984375
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.8714957237243652
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.5913043478260869
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7768557071685791
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.4738675958188153
	name: Cosine Precision
	- type: cosine_recall
	value: 0.7861271676300579
	name: Cosine Recall
	- type: cosine_ap
	value: 0.5644305887001508
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.7109375
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 674.426025390625
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.5913043478260869
	name: Dot F1
	- type: dot_f1_threshold
	value: 603.435302734375
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.4738675958188153
	name: Dot Precision
	- type: dot_recall
	value: 0.7861271676300579
	name: Dot Recall
	- type: dot_ap
	value: 0.5664868031504724
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.7109375
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 294.4728088378906
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.5935483870967742
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 401.1482849121094
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.4726027397260274
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.7976878612716763
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.5642688421649988
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.7109375
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 14.565500259399414
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.5913043478260869
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 18.60409164428711
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.4738675958188153
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.7861271676300579
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.5645557227019772
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.7109375
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 674.426025390625
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.5935483870967742
	name: Max F1
	- type: max_f1_threshold
	value: 603.435302734375
	name: Max F1 Threshold
	- type: max_precision
	value: 0.4738675958188153
	name: Max Precision
	- type: max_recall
	value: 0.7976878612716763
	name: Max Recall
	- type: max_ap
	value: 0.5664868031504724
	name: Max Ap
	- task:
	type: binary-classification
	name: Binary Classification
	dataset:
	name: Qnli dev
	type: Qnli-dev
	metrics:
	- type: cosine_accuracy
	value: 0.6796875
	name: Cosine Accuracy
	- type: cosine_accuracy_threshold
	value: 0.7726649045944214
	name: Cosine Accuracy Threshold
	- type: cosine_f1
	value: 0.6925675675675677
	name: Cosine F1
	- type: cosine_f1_threshold
	value: 0.7317887544631958
	name: Cosine F1 Threshold
	- type: cosine_precision
	value: 0.5758426966292135
	name: Cosine Precision
	- type: cosine_recall
	value: 0.8686440677966102
	name: Cosine Recall
	- type: cosine_ap
	value: 0.7302564198016936
	name: Cosine Ap
	- type: dot_accuracy
	value: 0.67578125
	name: Dot Accuracy
	- type: dot_accuracy_threshold
	value: 598.0419921875
	name: Dot Accuracy Threshold
	- type: dot_f1
	value: 0.6912751677852348
	name: Dot F1
	- type: dot_f1_threshold
	value: 565.4718017578125
	name: Dot F1 Threshold
	- type: dot_precision
	value: 0.5722222222222222
	name: Dot Precision
	- type: dot_recall
	value: 0.8728813559322034
	name: Dot Recall
	- type: dot_ap
	value: 0.7300462025003271
	name: Dot Ap
	- type: manhattan_accuracy
	value: 0.6796875
	name: Manhattan Accuracy
	- type: manhattan_accuracy_threshold
	value: 404.8309020996094
	name: Manhattan Accuracy Threshold
	- type: manhattan_f1
	value: 0.6933333333333332
	name: Manhattan F1
	- type: manhattan_f1_threshold
	value: 444.99224853515625
	name: Manhattan F1 Threshold
	- type: manhattan_precision
	value: 0.5714285714285714
	name: Manhattan Precision
	- type: manhattan_recall
	value: 0.8813559322033898
	name: Manhattan Recall
	- type: manhattan_ap
	value: 0.7369214156436785
	name: Manhattan Ap
	- type: euclidean_accuracy
	value: 0.6796875
	name: Euclidean Accuracy
	- type: euclidean_accuracy_threshold
	value: 18.790739059448242
	name: Euclidean Accuracy Threshold
	- type: euclidean_f1
	value: 0.6934306569343065
	name: Euclidean F1
	- type: euclidean_f1_threshold
	value: 19.35132598876953
	name: Euclidean F1 Threshold
	- type: euclidean_precision
	value: 0.6089743589743589
	name: Euclidean Precision
	- type: euclidean_recall
	value: 0.8050847457627118
	name: Euclidean Recall
	- type: euclidean_ap
	value: 0.7307381840067684
	name: Euclidean Ap
	- type: max_accuracy
	value: 0.6796875
	name: Max Accuracy
	- type: max_accuracy_threshold
	value: 598.0419921875
	name: Max Accuracy Threshold
	- type: max_f1
	value: 0.6934306569343065
	name: Max F1
	- type: max_f1_threshold
	value: 565.4718017578125
	name: Max F1 Threshold
	- type: max_precision
	value: 0.6089743589743589
	name: Max Precision
	- type: max_recall
	value: 0.8813559322033898
	name: Max Recall
	- type: max_ap
	value: 0.7369214156436785
	name: Max Ap
	---

	# SentenceTransformer based on microsoft/deberta-v3-small

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small). 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/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) <!-- at revision a36c739020e01763fe789b4b85e2df55d6180012 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	<!-- - Training Dataset: Unknown -->
	- 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': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
	(1): AdvancedWeightedPooling(
	(linear_cls): Linear(in_features=768, out_features=768, bias=True)
	(linear_mean): Linear(in_features=768, out_features=768, bias=True)
	(mha): MultiheadAttention(
	(out_proj): NonDynamicallyQuantizableLinear(in_features=768, out_features=768, bias=True)
	)
	(layernorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm2): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_cls): LayerNorm((768,), eps=1e-05, elementwise_affine=True)
	(layernorm_mean): LayerNorm((768,), eps=1e-05, elementwise_affine=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("bobox/DeBERTa3-s-CustomPoolin-v3-step1")
	# Run inference
	sentences = [
	'What was the name of eleven rulers of the 19th and 20th Egyptian dynasties?',
	'List of Rulers of Ancient Egypt and Nubia \| Lists of Rulers \| Heilbrunn Timeline of Art History \| The Metropolitan Museum of Art The Metropolitan Museum of Art List of Rulers of Ancient Egypt and Nubia See works of art 30.8.234 52.127.4 Our knowledge of the succession of Egyptian kings is based on kinglists kept by the ancient Egyptians themselves. The most famous are the Palermo Stone, which covers the period from the earliest dynasties to the middle of Dynasty 5; the Abydos Kinglist, which Seti I had carved on his temple at Abydos; and the Turin Canon, a papyrus that covers the period from the earliest dynasties to the reign of Ramesses II. All are incomplete or fragmentary. We also rely on the History of Egypt written by Manetho in the third century B.C. A priest in the temple at Heliopolis, Manetho had access to many original sources and it was he who divided the kings into the thirty dynasties we use today. It is to this structure of dynasties and listed kings that we now attempt to link an absolute chronology of dates in terms of our own calendrical system. The process is made difficult by the fragmentary condition of the kinglists and by differences in the calendrical years used at various times. Some astronomical observations from the ancient Egyptians have survived, allowing us to calculate absolute dates within a margin of error. Synchronisms with the other civilizations of the ancient world are also of limited use.',
	'What is the "Jack Sprat" nursery rhyme? \| Reference.com What is the "Jack Sprat" nursery rhyme? A: Quick Answer "Jack Sprat" is a traditional English nursery rhyme whose main verse says, "Jack Sprat could eat no fat. His wife could eat no lean. And so between them both, you see, they licked the platter clean." Though it was likely sung by children long before, "Jack Sprat" was first published around 1765 in the compilation "Mother Goose\'s Melody." Full Answer According to Rhymes.org, a U.K. website devoted to nursery rhyme lyrics and origins, the "Jack Sprat" nursery rhyme has its origins in British history. In one interpretation, Jack Sprat was King Charles I, who ruled England in the early part of the 17th century, and his wife was Queen Henrietta Maria. Parliament refused to finance the king\'s war with Spain, which made him lean. However, the queen fattened the coffers by levying an illegal war tax. In an alternative version, the "Jack Sprat" nursery rhyme is linked to King Richard and his brother John of the Robin Hood legend. Jack Sprat was King John, the usurper who tried to take over the crown when King Richard went off to fight in the Crusades in the 12th century. When King Richard was captured, John had to raise a ransom to rescue him, leaving the country lean. The wife was Joan, daughter of the Earl of Gloucester, the greedy wife of King John. However, after King Richard died and John became king, he had his marriage with Joan annulled.',
	]
	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`
	* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| pearson_cosine \| 0.7674 \|
	\| spearman_cosine \| 0.7776 \|
	\| pearson_manhattan \| 0.7824 \|
	\| spearman_manhattan \| 0.7721 \|
	\| pearson_euclidean \| 0.7883 \|
	\| spearman_euclidean \| 0.7775 \|
	\| pearson_dot \| 0.7669 \|
	\| spearman_dot \| 0.7763 \|
	\| pearson_max \| 0.7883 \|
	\| spearman_max \| 0.7776 \|

	#### Binary Classification
	* Dataset: `allNLI-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.709 \|
	\| cosine_accuracy_threshold \| 0.8715 \|
	\| cosine_f1 \| 0.5913 \|
	\| cosine_f1_threshold \| 0.7769 \|
	\| cosine_precision \| 0.4739 \|
	\| cosine_recall \| 0.7861 \|
	\| cosine_ap \| 0.5644 \|
	\| dot_accuracy \| 0.7109 \|
	\| dot_accuracy_threshold \| 674.426 \|
	\| dot_f1 \| 0.5913 \|
	\| dot_f1_threshold \| 603.4353 \|
	\| dot_precision \| 0.4739 \|
	\| dot_recall \| 0.7861 \|
	\| dot_ap \| 0.5665 \|
	\| manhattan_accuracy \| 0.7109 \|
	\| manhattan_accuracy_threshold \| 294.4728 \|
	\| manhattan_f1 \| 0.5935 \|
	\| manhattan_f1_threshold \| 401.1483 \|
	\| manhattan_precision \| 0.4726 \|
	\| manhattan_recall \| 0.7977 \|
	\| manhattan_ap \| 0.5643 \|
	\| euclidean_accuracy \| 0.7109 \|
	\| euclidean_accuracy_threshold \| 14.5655 \|
	\| euclidean_f1 \| 0.5913 \|
	\| euclidean_f1_threshold \| 18.6041 \|
	\| euclidean_precision \| 0.4739 \|
	\| euclidean_recall \| 0.7861 \|
	\| euclidean_ap \| 0.5646 \|
	\| max_accuracy \| 0.7109 \|
	\| max_accuracy_threshold \| 674.426 \|
	\| max_f1 \| 0.5935 \|
	\| max_f1_threshold \| 603.4353 \|
	\| max_precision \| 0.4739 \|
	\| max_recall \| 0.7977 \|
	\| max_ap \| 0.5665 \|

	#### Binary Classification
	* Dataset: `Qnli-dev`
	* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)

	\| Metric \| Value \|
	\|:-----------------------------\|:-----------\|
	\| cosine_accuracy \| 0.6797 \|
	\| cosine_accuracy_threshold \| 0.7727 \|
	\| cosine_f1 \| 0.6926 \|
	\| cosine_f1_threshold \| 0.7318 \|
	\| cosine_precision \| 0.5758 \|
	\| cosine_recall \| 0.8686 \|
	\| cosine_ap \| 0.7303 \|
	\| dot_accuracy \| 0.6758 \|
	\| dot_accuracy_threshold \| 598.042 \|
	\| dot_f1 \| 0.6913 \|
	\| dot_f1_threshold \| 565.4718 \|
	\| dot_precision \| 0.5722 \|
	\| dot_recall \| 0.8729 \|
	\| dot_ap \| 0.73 \|
	\| manhattan_accuracy \| 0.6797 \|
	\| manhattan_accuracy_threshold \| 404.8309 \|
	\| manhattan_f1 \| 0.6933 \|
	\| manhattan_f1_threshold \| 444.9922 \|
	\| manhattan_precision \| 0.5714 \|
	\| manhattan_recall \| 0.8814 \|
	\| manhattan_ap \| 0.7369 \|
	\| euclidean_accuracy \| 0.6797 \|
	\| euclidean_accuracy_threshold \| 18.7907 \|
	\| euclidean_f1 \| 0.6934 \|
	\| euclidean_f1_threshold \| 19.3513 \|
	\| euclidean_precision \| 0.609 \|
	\| euclidean_recall \| 0.8051 \|
	\| euclidean_ap \| 0.7307 \|
	\| max_accuracy \| 0.6797 \|
	\| max_accuracy_threshold \| 598.042 \|
	\| max_f1 \| 0.6934 \|
	\| max_f1_threshold \| 565.4718 \|
	\| max_precision \| 0.609 \|
	\| max_recall \| 0.8814 \|
	\| max_ap \| 0.7369 \|

	<!--
	## 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

	### Evaluation Dataset

	#### vitaminc-pairs

	* Dataset: [vitaminc-pairs](https://huggingface.co/datasets/tals/vitaminc) at [be6febb](https://huggingface.co/datasets/tals/vitaminc/tree/be6febb761b0b2807687e61e0b5282e459df2fa0)
	* Size: 128 evaluation samples
	* Columns: <code>claim</code> and <code>evidence</code>
	* Approximate statistics based on the first 128 samples:
	\| \| claim \| evidence \|
	\|:--------\|:----------------------------------------------------------------------------------\|:-----------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 9 tokens</li><li>mean: 21.42 tokens</li><li>max: 41 tokens</li></ul> \| <ul><li>min: 11 tokens</li><li>mean: 35.55 tokens</li><li>max: 79 tokens</li></ul> \|
	* Samples:
	\| claim \| evidence \|
	\|:------------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>Dragon Con had over 5000 guests .</code> \| <code>Among the more than 6000 guests and musical performers at the 2009 convention were such notables as Patrick Stewart , William Shatner , Leonard Nimoy , Terry Gilliam , Bruce Boxleitner , James Marsters , and Mary McDonnell .</code> \|
	\| <code>COVID-19 has reached more than 185 countries .</code> \| <code>As of , more than cases of COVID-19 have been reported in more than 190 countries and 200 territories , resulting in more than deaths .</code> \|
	\| <code>In March , Italy had 3.6x times more cases of coronavirus than China .</code> \| <code>As of 12 March , among nations with at least one million citizens , Italy has the world 's highest per capita rate of positive coronavirus cases at 206.1 cases per million people ( 3.6x times the rate of China ) and is the country with the second-highest number of positive cases as well as of deaths in the world , after China .</code> \|
	* Loss: [<code>CachedGISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedgistembedloss) with these parameters:
	```json
	{'guide': 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': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
	(2): Normalize()
	), 'temperature': 0.025}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 100
	- `per_device_eval_batch_size`: 256
	- `gradient_accumulation_steps`: 2
	- `lr_scheduler_type`: cosine_with_min_lr
	- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05}
	- `warmup_ratio`: 0.33
	- `save_safetensors`: False
	- `fp16`: True
	- `push_to_hub`: True
	- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp
	- `hub_strategy`: all_checkpoints
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 100
	- `per_device_eval_batch_size`: 256
	- `per_gpu_train_batch_size`: None
	- `per_gpu_eval_batch_size`: None
	- `gradient_accumulation_steps`: 2
	- `eval_accumulation_steps`: None
	- `torch_empty_cache_steps`: None
	- `learning_rate`: 5e-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`: 3
	- `max_steps`: -1
	- `lr_scheduler_type`: cosine_with_min_lr
	- `lr_scheduler_kwargs`: {'num_cycles': 0.5, 'min_lr': 1.6666666666666667e-05}
	- `warmup_ratio`: 0.33
	- `warmup_steps`: 0
	- `log_level`: passive
	- `log_level_replica`: warning
	- `log_on_each_node`: True
	- `logging_nan_inf_filter`: True
	- `save_safetensors`: False
	- `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`: 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, '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`: 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`: True
	- `resume_from_checkpoint`: None
	- `hub_model_id`: bobox/DeBERTa3-s-CustomPoolin-v3-step1-checkpoints-tmp
	- `hub_strategy`: all_checkpoints
	- `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
	- `eval_on_start`: False
	- `eval_use_gather_object`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

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

	\| Epoch \| Step \| Training Loss \| vitaminc-pairs loss \| negation-triplets loss \| scitail-pairs-pos loss \| scitail-pairs-qa loss \| xsum-pairs loss \| sciq pairs loss \| qasc pairs loss \| openbookqa pairs loss \| msmarco pairs loss \| nq pairs loss \| trivia pairs loss \| gooaq pairs loss \| paws-pos loss \| global dataset loss \| sts-test_spearman_cosine \| allNLI-dev_max_ap \| Qnli-dev_max_ap \|
	\|:------:\|:----:\|:-------------:\|:-------------------:\|:----------------------:\|:----------------------:\|:---------------------:\|:---------------:\|:---------------:\|:---------------:\|:---------------------:\|:------------------:\|:-------------:\|:-----------------:\|:----------------:\|:-------------:\|:-------------------:\|:------------------------:\|:-----------------:\|:---------------:\|
	\| 0.0168 \| 8 \| 10.2928 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0336 \| 16 \| 9.2166 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0504 \| 24 \| 9.4858 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0672 \| 32 \| 10.6143 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.0840 \| 40 \| 8.7553 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1008 \| 48 \| 10.9939 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1176 \| 56 \| 7.6039 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1345 \| 64 \| 5.9498 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1513 \| 72 \| 7.3051 \| 3.2988 \| 3.9604 \| 1.9818 \| 2.1997 \| 6.0515 \| 0.6095 \| 6.3199 \| 4.8391 \| 6.4886 \| 6.6406 \| 6.4894 \| 6.1527 \| 2.0082 \| 4.9577 \| 0.3066 \| 0.3444 \| 0.5627 \|
	\| 0.1681 \| 80 \| 8.3034 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.1849 \| 88 \| 7.6669 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2017 \| 96 \| 6.6415 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2185 \| 104 \| 5.7797 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2353 \| 112 \| 5.8361 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2521 \| 120 \| 5.3339 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2689 \| 128 \| 5.5908 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.2857 \| 136 \| 5.3209 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3025 \| 144 \| 5.5359 \| 3.3310 \| 3.8580 \| 1.4769 \| 1.6994 \| 5.4819 \| 0.5385 \| 5.2021 \| 4.4410 \| 5.3419 \| 5.5506 \| 5.6972 \| 5.3376 \| 1.4170 \| 3.9169 \| 0.2954 \| 0.3795 \| 0.6317 \|
	\| 0.3193 \| 152 \| 5.4713 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3361 \| 160 \| 4.9368 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3529 \| 168 \| 4.6594 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3697 \| 176 \| 4.8392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.3866 \| 184 \| 4.414 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4034 \| 192 \| 4.891 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4202 \| 200 \| 4.4553 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4370 \| 208 \| 3.9729 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4538 \| 216 \| 3.7705 \| 3.2468 \| 3.6435 \| 0.7890 \| 0.7356 \| 3.9327 \| 0.4082 \| 3.7175 \| 3.5404 \| 3.5351 \| 4.0506 \| 3.9953 \| 3.6074 \| 0.4195 \| 2.4726 \| 0.3791 \| 0.4133 \| 0.6779 \|
	\| 0.4706 \| 224 \| 3.8409 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.4874 \| 232 \| 3.7894 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5042 \| 240 \| 3.3523 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5210 \| 248 \| 3.2407 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5378 \| 256 \| 3.3203 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5546 \| 264 \| 2.8457 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5714 \| 272 \| 2.4181 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.5882 \| 280 \| 3.4589 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6050 \| 288 \| 2.8203 \| 3.1119 \| 3.1485 \| 0.4531 \| 0.2652 \| 2.6895 \| 0.2656 \| 2.5542 \| 2.7523 \| 2.6600 \| 3.1773 \| 3.2099 \| 2.7316 \| 0.2006 \| 1.6342 \| 0.5257 \| 0.4717 \| 0.7078 \|
	\| 0.6218 \| 296 \| 2.4697 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6387 \| 304 \| 2.4654 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6555 \| 312 \| 2.4236 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6723 \| 320 \| 2.2879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.6891 \| 328 \| 2.2145 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7059 \| 336 \| 1.8464 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7227 \| 344 \| 2.0086 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7395 \| 352 \| 2.0635 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7563 \| 360 \| 1.8584 \| 3.3202 \| 2.5793 \| 0.3434 \| 0.1618 \| 1.6759 \| 0.1834 \| 1.6454 \| 2.1257 \| 2.1938 \| 2.5316 \| 2.4558 \| 2.0596 \| 0.0984 \| 1.2206 \| 0.6610 \| 0.5199 \| 0.7119 \|
	\| 0.7731 \| 368 \| 2.0286 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.7899 \| 376 \| 1.9389 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8067 \| 384 \| 1.7453 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8235 \| 392 \| 1.6629 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8403 \| 400 \| 1.2724 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8571 \| 408 \| 1.7824 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8739 \| 416 \| 1.5826 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.8908 \| 424 \| 1.1971 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9076 \| 432 \| 1.5228 \| 3.3624 \| 2.1952 \| 0.3006 \| 0.1223 \| 1.1091 \| 0.1582 \| 1.2383 \| 1.8664 \| 1.7434 \| 2.3959 \| 2.0697 \| 1.7563 \| 0.0766 \| 1.0193 \| 0.7292 \| 0.5194 \| 0.7126 \|
	\| 0.9244 \| 440 \| 1.3323 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9412 \| 448 \| 1.5124 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9580 \| 456 \| 1.5565 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9748 \| 464 \| 1.3672 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 0.9916 \| 472 \| 1.0382 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0084 \| 480 \| 1.0626 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0252 \| 488 \| 1.3539 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0420 \| 496 \| 1.1723 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0588 \| 504 \| 1.4235 \| 3.4031 \| 1.9759 \| 0.2554 \| 0.0814 \| 0.9034 \| 0.1378 \| 1.1603 \| 1.7589 \| 1.5608 \| 2.1230 \| 1.7719 \| 1.6633 \| 0.0720 \| 0.9380 \| 0.7523 \| 0.5297 \| 0.7129 \|
	\| 1.0756 \| 512 \| 1.2283 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.0924 \| 520 \| 1.2455 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1092 \| 528 \| 1.4265 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1261 \| 536 \| 1.296 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1429 \| 544 \| 0.8763 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1597 \| 552 \| 1.5678 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1765 \| 560 \| 1.2548 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.1933 \| 568 \| 1.3731 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.2101 \| 576 \| 1.3023 \| 3.3815 \| 1.8740 \| 0.2373 \| 0.0769 \| 0.7711 \| 0.1237 \| 0.9432 \| 1.6871 \| 1.5070 \| 1.9947 \| 1.6041 \| 1.5579 \| 0.0721 \| 0.8661 \| 0.7642 \| 0.5412 \| 0.7159 \|
	\| 1.2269 \| 584 \| 0.8135 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.2437 \| 592 \| 1.0259 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.2605 \| 600 \| 1.1896 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.2773 \| 608 \| 1.0532 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.2941 \| 616 \| 1.3221 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.3109 \| 624 \| 1.3136 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.3277 \| 632 \| 1.2238 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.3445 \| 640 \| 1.2407 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.3613 \| 648 \| 1.2245 \| 3.4717 \| 1.7962 \| 0.2242 \| 0.0488 \| 0.7472 \| 0.1108 \| 0.9272 \| 1.6692 \| 1.3845 \| 1.9117 \| 1.3410 \| 1.4387 \| 0.0701 \| 0.8505 \| 0.7680 \| 0.5471 \| 0.7227 \|
	\| 1.3782 \| 656 \| 1.0428 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.3950 \| 664 \| 1.1391 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4118 \| 672 \| 1.2632 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4286 \| 680 \| 0.9403 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4454 \| 688 \| 0.7571 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4622 \| 696 \| 0.9436 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4790 \| 704 \| 1.1239 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.4958 \| 712 \| 0.9499 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.5126 \| 720 \| 1.0945 \| 3.6495 \| 1.6693 \| 0.2157 \| 0.0492 \| 0.6830 \| 0.1049 \| 0.9140 \| 1.5967 \| 1.4397 \| 1.7394 \| 1.3303 \| 1.4334 \| 0.0603 \| 0.8185 \| 0.7815 \| 0.5606 \| 0.7098 \|
	\| 1.5294 \| 728 \| 1.1161 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.5462 \| 736 \| 1.0056 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.5630 \| 744 \| 1.1743 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.5798 \| 752 \| 0.9153 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.5966 \| 760 \| 1.1589 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.6134 \| 768 \| 0.9187 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.6303 \| 776 \| 0.6937 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.6471 \| 784 \| 0.9704 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.6639 \| 792 \| 0.7343 \| 3.5442 \| 1.6493 \| 0.2208 \| 0.0249 \| 0.6152 \| 0.0969 \| 0.7111 \| 1.5369 \| 1.4058 \| 1.7066 \| 1.2784 \| 1.3419 \| 0.0585 \| 0.7827 \| 0.7749 \| 0.5627 \| 0.7284 \|
	\| 1.6807 \| 800 \| 1.2878 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.6975 \| 808 \| 0.9898 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7143 \| 816 \| 0.7613 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7311 \| 824 \| 0.9612 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7479 \| 832 \| 1.1524 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7647 \| 840 \| 0.827 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7815 \| 848 \| 1.1898 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.7983 \| 856 \| 1.0117 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.8151 \| 864 \| 0.7019 \| 3.4544 \| 1.6149 \| 0.2035 \| 0.0181 \| 0.5525 \| 0.0999 \| 0.6641 \| 1.5456 \| 1.3911 \| 1.7188 \| 1.2547 \| 1.3517 \| 0.0562 \| 0.7473 \| 0.7684 \| 0.5697 \| 0.7329 \|
	\| 1.8319 \| 872 \| 0.8352 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.8487 \| 880 \| 0.7836 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.8655 \| 888 \| 1.0187 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.8824 \| 896 \| 0.74 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.8992 \| 904 \| 0.7263 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.9160 \| 912 \| 0.8073 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.9328 \| 920 \| 0.8185 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.9496 \| 928 \| 1.0992 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 1.9664 \| 936 \| 0.9973 \| 3.5110 \| 1.5776 \| 0.2035 \| 0.0250 \| 0.5881 \| 0.0934 \| 0.6719 \| 1.5059 \| 1.2970 \| 1.6186 \| 1.1815 \| 1.2714 \| 0.0564 \| 0.7213 \| 0.7799 \| 0.5544 \| 0.7341 \|
	\| 1.9832 \| 944 \| 0.6662 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0 \| 952 \| 0.533 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0168 \| 960 \| 0.7712 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0336 \| 968 \| 0.6879 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0504 \| 976 \| 0.7975 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0672 \| 984 \| 0.873 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.0840 \| 992 \| 0.7995 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.1008 \| 1000 \| 1.0119 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.1176 \| 1008 \| 0.6317 \| 3.6778 \| 1.5845 \| 0.2102 \| 0.0228 \| 0.5851 \| 0.0977 \| 0.6411 \| 1.4752 \| 1.2992 \| 1.6314 \| 1.1260 \| 1.2683 \| 0.0556 \| 0.7329 \| 0.7693 \| 0.5614 \| 0.7274 \|
	\| 2.1345 \| 1016 \| 0.72 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.1513 \| 1024 \| 0.9418 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.1681 \| 1032 \| 0.7848 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.1849 \| 1040 \| 0.6965 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.2017 \| 1048 \| 1.0447 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.2185 \| 1056 \| 0.6361 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.2353 \| 1064 \| 0.6837 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.2521 \| 1072 \| 0.5713 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.2689 \| 1080 \| 0.8193 \| 3.6399 \| 1.5565 \| 0.2069 \| 0.0213 \| 0.5440 \| 0.0904 \| 0.6057 \| 1.4815 \| 1.2856 \| 1.6441 \| 1.1469 \| 1.2540 \| 0.0543 \| 0.7216 \| 0.7765 \| 0.5599 \| 0.7322 \|
	\| 2.2857 \| 1088 \| 0.9754 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3025 \| 1096 \| 0.8932 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3193 \| 1104 \| 0.8716 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3361 \| 1112 \| 0.8787 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3529 \| 1120 \| 0.9529 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3697 \| 1128 \| 0.775 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.3866 \| 1136 \| 0.6178 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.4034 \| 1144 \| 0.8384 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.4202 \| 1152 \| 0.9425 \| 3.5672 \| 1.5244 \| 0.2111 \| 0.0162 \| 0.5593 \| 0.0893 \| 0.5759 \| 1.4933 \| 1.2703 \| 1.5815 \| 1.1202 \| 1.2132 \| 0.0531 \| 0.7058 \| 0.7730 \| 0.5635 \| 0.7350 \|
	\| 2.4370 \| 1160 \| 0.4551 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.4538 \| 1168 \| 0.6392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.4706 \| 1176 \| 0.8341 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.4874 \| 1184 \| 0.7392 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.5042 \| 1192 \| 0.7646 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.5210 \| 1200 \| 0.8613 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.5378 \| 1208 \| 0.7585 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.5546 \| 1216 \| 1.0611 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.5714 \| 1224 \| 0.6506 \| 3.6439 \| 1.5040 \| 0.2125 \| 0.0162 \| 0.5282 \| 0.0863 \| 0.5858 \| 1.5073 \| 1.2444 \| 1.5493 \| 1.1014 \| 1.2073 \| 0.0532 \| 0.7022 \| 0.7774 \| 0.5647 \| 0.7328 \|
	\| 2.5882 \| 1232 \| 0.8525 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6050 \| 1240 \| 0.6304 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6218 \| 1248 \| 0.6354 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6387 \| 1256 \| 0.6583 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6555 \| 1264 \| 0.5964 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6723 \| 1272 \| 0.818 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.6891 \| 1280 \| 0.8635 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.7059 \| 1288 \| 0.6389 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.7227 \| 1296 \| 0.6819 \| 3.6131 \| 1.5104 \| 0.2084 \| 0.0148 \| 0.5229 \| 0.0854 \| 0.5588 \| 1.4963 \| 1.2766 \| 1.5679 \| 1.0982 \| 1.2203 \| 0.0529 \| 0.7059 \| 0.7762 \| 0.5659 \| 0.7355 \|
	\| 2.7395 \| 1304 \| 0.7878 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.7563 \| 1312 \| 0.7638 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.7731 \| 1320 \| 0.8885 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.7899 \| 1328 \| 0.8184 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.8067 \| 1336 \| 0.7472 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.8235 \| 1344 \| 0.7012 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.8403 \| 1352 \| 0.4622 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.8571 \| 1360 \| 0.846 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.8739 \| 1368 \| 0.8308 \| 3.6224 \| 1.5088 \| 0.2084 \| 0.0148 \| 0.5118 \| 0.0858 \| 0.5523 \| 1.4941 \| 1.2756 \| 1.5808 \| 1.0925 \| 1.2114 \| 0.0521 \| 0.7022 \| 0.7765 \| 0.5662 \| 0.7366 \|
	\| 2.8908 \| 1376 \| 0.5334 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9076 \| 1384 \| 0.7893 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9244 \| 1392 \| 0.6897 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9412 \| 1400 \| 0.7803 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9580 \| 1408 \| 0.841 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9748 \| 1416 \| 0.787 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 2.9916 \| 1424 \| 0.5861 \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \| - \|
	\| 3.0 \| 1428 \| - \| 3.6139 \| 1.5071 \| 0.2084 \| 0.0150 \| 0.5124 \| 0.0862 \| 0.5532 \| 1.4924 \| 1.2700 \| 1.5806 \| 1.0905 \| 1.2081 \| 0.0519 \| 0.6997 \| 0.7776 \| 0.5665 \| 0.7369 \|

	</details>

	### Framework Versions
	- Python: 3.10.12
	- Sentence Transformers: 3.2.0
	- Transformers: 4.44.2
	- PyTorch: 2.4.1+cu121
	- Accelerate: 0.34.2
	- Datasets: 3.0.1
	- 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",
	}
	```

	<!--
	## 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.
	-->