Add new SentenceTransformer model.

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	---
	base_model: microsoft/mpnet-base
	datasets:
	- sentence-transformers/natural-questions
	language:
	- en
	library_name: sentence-transformers
	license: apache-2.0
	metrics:
	- cosine_accuracy@1
	- cosine_accuracy@3
	- cosine_accuracy@5
	- cosine_accuracy@10
	- cosine_precision@1
	- cosine_precision@3
	- cosine_precision@5
	- cosine_precision@10
	- cosine_recall@1
	- cosine_recall@3
	- cosine_recall@5
	- cosine_recall@10
	- cosine_ndcg@10
	- cosine_mrr@10
	- cosine_map@100
	- dot_accuracy@1
	- dot_accuracy@3
	- dot_accuracy@5
	- dot_accuracy@10
	- dot_precision@1
	- dot_precision@3
	- dot_precision@5
	- dot_precision@10
	- dot_recall@1
	- dot_recall@3
	- dot_recall@5
	- dot_recall@10
	- dot_ndcg@10
	- dot_mrr@10
	- dot_map@100
	pipeline_tag: sentence-similarity
	tags:
	- sentence-transformers
	- sentence-similarity
	- feature-extraction
	- generated_from_trainer
	- dataset_size:100231
	- loss:MultipleNegativesRankingLoss
	widget:
	- source_sentence: when did the british leave new york city
	sentences:
	- Golden State Warriors The Golden State Warriors are an American professional basketball
	team based in Oakland, California. The Warriors compete in the National Basketball
	Association (NBA) as a member of the league's Western Conference Pacific Division.
	The Warriors play their home games at the Oracle Arena in Oakland. The Warriors
	have reached nine NBA Finals, winning five NBA championships in 1947,[b] 1956,
	1975, 2015 and 2017. Golden State's five NBA championships are tied for fourth-most
	in NBA history with the San Antonio Spurs, and behind only the Boston Celtics
	(17), Los Angeles Lakers (16) and Chicago Bulls (6). As of 2017, the Warriors
	are the third most valuable NBA franchise according to Forbes, with an estimated
	value of $2.6Â billion.[6]
	- Evacuation Day (New York) Evacuation Day on November 25 marks the day in 1783
	when British troops departed from New York City on Manhattan Island, after the
	end of the American Revolutionary War. After this British Army evacuation, General
	George Washington triumphantly led the Continental Army from his former headquarters,
	north of the city, across the Harlem River south down Manhattan through the town
	to The Battery at the foot of Broadway.[1]
	- Biochemical oxygen demand BOD can be used as a gauge of the effectiveness of wastewater
	treatment plants. It is listed as a conventional pollutant in the U.S. Clean Water
	Act.[2]
	- source_sentence: what is the newest generation of the ipad
	sentences:
	- Alex Karev Alex is fired by Dr. Lebackes when Maggie Pierce accidentally reveals
	to him that Karev was thinking about leaving the job. Webber recommended Bailey
	to fill Yang's board seat after she left, so Bailey and Alex fight over the chair.
	They both make presentations to the board and eventually Bailey wins, with a unanimous
	vote in her favor. He is hired back as an attending Peds surgeon and takes over
	full-time as Arizona pursues a fellowship with Dr. Herman. Alex continues to date
	Jo and his friendship with Meredith grows stronger than ever, with him taking
	on the role of her new person. When Derek dies and Meredith runs away, Alex is
	upset by her leaving without telling him where she went and calls her everyday.
	Eventually she calls him, tells him she is okay, and to stop calling. When she
	goes into labor and gives birth to Ellis Shepherd, Alex goes to see her since
	he is her emergency contact. He brings Meredith and her kids back to her house.
	She asks to move back in with him in her old house. Alex sells Meredith back the
	house and he and Jo rent a loft.
	- List of presidents of the United States by age The median age upon accession to
	the presidency is 55 years and 3 months. This is how old Lyndon B. Johnson was
	at the time of his inauguration. The youngest person to assume the office was
	Theodore Roosevelt, who became president at the age of 42 years, 322 days, following
	William McKinley's assassination; the oldest was Donald Trump, who was 70 years,
	220 days old at his inauguration. The youngest person to be elected president
	was John F. Kennedy, at 43 years, 163 days of age on election day; the oldest
	was Ronald Reagan, who was 73 years, 274 days old at the time of his election
	to a second term.
	- iPad (2018) The iPad (officially sixth-generation iPad) is a 9.7-inch (25cm) tablet
	computer designed, developed, and marketed by Apple Inc. It was announced on March
	27, 2018 during an education-focused event in Chicago and it is a revision of
	the 2017 model, upgraded with the Apple A10 Fusion SoC and support for styluses
	such as Apple Pencil.[2] The iPad is marketed towards educators and schools.
	- source_sentence: what is the average speed of passenger airplane
	sentences:
	- Fixed exchange-rate system In the 21st century, the currencies associated with
	large economies typically do not fix or peg exchange rates to other currencies.
	The last large economy to use a fixed exchange rate system was the People's Republic
	of China which, in July 2005, adopted a slightly more flexible exchange rate system
	called a managed exchange rate.[2] The European Exchange Rate Mechanism is also
	used on a temporary basis to establish a final conversion rate against the Euro
	(â‚¬) from the local currencies of countries joining the Eurozone.
	- Tenth Doctor The Tenth Doctor is an incarnation of the Doctor, the protagonist
	of the BBC science fiction television programme Doctor Who, who is played by David
	Tennant in three series as well as nine specials. As with previous incarnations
	of the Doctor, the character has also appeared in other Doctor Who spin-offs.
	In the programme's narrative, the Doctor is a centuries-old Time Lord alien from
	the planet Gallifrey who travels in time in his TARDIS, frequently with companions.
	When the Doctor is critically injured beyond medical repair, he can regenerate
	his body; in doing so, his physical appearance and personality change, and a new
	actor assumes the role. Tennant's portrayal of the Doctor is of an outwardly charismatic
	and charming adventurer whose likable and easygoing attitude can quickly turn
	to righteous fury when provoked.
	- Cruise (aeronautics) The typical cruising airspeed for a long-distance commercial
	passenger aircraft is approximately 475–500 knots (878–926 km/h; 546–575 mph).
	- source_sentence: when is cars three going to be released
	sentences:
	- Benedict's reagent The color of the obtained precipitate gives an idea about the
	quantity of sugar present in the solution, hence the test is semi-quantitative.
	A greenish precipitate indicates about 0.5 g% concentration; yellow precipitate
	indicates 1 g% concentration; orange indicates 1.5 g% and red indicates 2 g% or
	higher concentration.
	- Cars 3 The film was released on June 16, 2017, has grossed over $362 million worldwide
	and received generally positive reviews, with many critics considering it an improvement
	over its predecessor, as well as praising its emotional story and animation.[7]
	- Sleeping Beauty At the christening of a king and queen's long-wished-for child,
	seven good fairies are invited to be godmothers to the infant princess. The fairies
	attend the banquet at the palace. Each fairy is presented with a golden plate
	and drinking cups adorned with jewels. Soon after, an old fairy enters the palace
	and is seated with a plate of fine china and a crystal drinking glass. This old
	fairy is overlooked because she has been within a tower for many years and everyone
	had believed her to be deceased. Six of the other seven fairies then offer their
	gifts of beauty, wit, grace, dance, song, and goodness to the infant princess.
	The evil fairy is very angry about having been forgotten, and as her gift, enchants
	the infant princess so that she will one day prick her finger on a spindle of
	a spinning wheel and die. The seventh fairy, who hasn't yet given her gift, attempts
	to reverse the evil fairy's curse. However, she can only do so partially. Instead
	of dying, the Princess will fall into a deep sleep for 100 years and be awakened
	by a kiss from a king's son.
	- source_sentence: who was ancient china's main enemy that lived to the north
	sentences:
	- Betty Lynn Elizabeth Ann Theresa "Betty" Lynn[1] (born August 29, 1926) is a former
	American actress. She is best known for her role as Thelma Lou, Deputy Barney
	Fife's girlfriend, on The Andy Griffith Show.
	- Sampath Bank Sampath Bank PLC is a licensed commercial bank incorporated in Sri
	Lanka in 1986 with 229 branches and 373 ATMs island wide. It has won the "Bank
	of the Year" award by "The Banker" of Financial Times Limited â€“ London, for
	the second consecutive year and the "National Business Excellence Awards 2010".[citation
	needed] It has become the third largest private sector bank in Sri Lanka with
	Rs. 453 billion in deposits as of 30 June 2016.[1]
	- 'Sui dynasty The Sui Dynasty (Chinese: 隋朝; pinyin: Suí cháo) was a short-lived
	imperial dynasty of China of pivotal significance. The Sui unified the Northern
	and Southern dynasties and reinstalled the rule of ethnic Han Chinese in the entirety
	of China proper, along with sinicization of former nomadic ethnic minorities (the
	Five Barbarians) within its territory. It was succeeded by the Tang dynasty, which
	largely inherited its foundation.'
	co2_eq_emissions:
	emissions: 176.27316538970194
	energy_consumed: 0.45349178905614573
	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: 1.208
	hardware_used: 1 x NVIDIA GeForce RTX 3090
	model-index:
	- name: MPNet base trained on Natural Questions pairs
	results:
	- task:
	type: information-retrieval
	name: Information Retrieval
	dataset:
	name: natural questions dev
	type: natural-questions-dev
	metrics:
	- type: cosine_accuracy@1
	value: 0.5898739126185124
	name: Cosine Accuracy@1
	- type: cosine_accuracy@3
	value: 0.8181018473267521
	name: Cosine Accuracy@3
	- type: cosine_accuracy@5
	value: 0.8875965203792395
	name: Cosine Accuracy@5
	- type: cosine_accuracy@10
	value: 0.9434072915648519
	name: Cosine Accuracy@10
	- type: cosine_precision@1
	value: 0.5898739126185124
	name: Cosine Precision@1
	- type: cosine_precision@3
	value: 0.272700615775584
	name: Cosine Precision@3
	- type: cosine_precision@5
	value: 0.17751930407584793
	name: Cosine Precision@5
	- type: cosine_precision@10
	value: 0.0943407291564852
	name: Cosine Precision@10
	- type: cosine_recall@1
	value: 0.5898739126185124
	name: Cosine Recall@1
	- type: cosine_recall@3
	value: 0.8181018473267521
	name: Cosine Recall@3
	- type: cosine_recall@5
	value: 0.8875965203792395
	name: Cosine Recall@5
	- type: cosine_recall@10
	value: 0.9434072915648519
	name: Cosine Recall@10
	- type: cosine_ndcg@10
	value: 0.7718587241150588
	name: Cosine Ndcg@10
	- type: cosine_mrr@10
	value: 0.7161706640105541
	name: Cosine Mrr@10
	- type: cosine_map@100
	value: 0.7187822446150476
	name: Cosine Map@100
	- type: dot_accuracy@1
	value: 0.5610399765418825
	name: Dot Accuracy@1
	- type: dot_accuracy@3
	value: 0.7970872837454794
	name: Dot Accuracy@3
	- type: dot_accuracy@5
	value: 0.866386472485583
	name: Dot Accuracy@5
	- type: dot_accuracy@10
	value: 0.9327533965399277
	name: Dot Accuracy@10
	- type: dot_precision@1
	value: 0.5610399765418825
	name: Dot Precision@1
	- type: dot_precision@3
	value: 0.2656957612484931
	name: Dot Precision@3
	- type: dot_precision@5
	value: 0.17327729449711662
	name: Dot Precision@5
	- type: dot_precision@10
	value: 0.09327533965399278
	name: Dot Precision@10
	- type: dot_recall@1
	value: 0.5610399765418825
	name: Dot Recall@1
	- type: dot_recall@3
	value: 0.7970872837454794
	name: Dot Recall@3
	- type: dot_recall@5
	value: 0.866386472485583
	name: Dot Recall@5
	- type: dot_recall@10
	value: 0.9327533965399277
	name: Dot Recall@10
	- type: dot_ndcg@10
	value: 0.7508293948042275
	name: Dot Ndcg@10
	- type: dot_mrr@10
	value: 0.6920021317098743
	name: Dot Mrr@10
	- type: dot_map@100
	value: 0.6951493585515177
	name: Dot Map@100
	---

	# MPNet base trained on Natural Questions pairs

	This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. 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) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
	- Maximum Sequence Length: 512 tokens
	- Output Dimensionality: 768 tokens
	- Similarity Function: Cosine Similarity
	- Training Dataset:
	- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
	- Language: en
	- License: apache-2.0

	### 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: 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/mpnet-base-natural-questions-mnrl")
	# Run inference
	sentences = [
	"who was ancient china's main enemy that lived to the north",
	'Sui dynasty The Sui Dynasty (Chinese: 隋朝; pinyin: Suí cháo) was a short-lived imperial dynasty of China of pivotal significance. The Sui unified the Northern and Southern dynasties and reinstalled the rule of ethnic Han Chinese in the entirety of China proper, along with sinicization of former nomadic ethnic minorities (the Five Barbarians) within its territory. It was succeeded by the Tang dynasty, which largely inherited its foundation.',
	'Sampath Bank Sampath Bank PLC is a licensed commercial bank incorporated in Sri Lanka in 1986 with 229 branches and 373 ATMs island wide. It has won the "Bank of the Year" award by "The Banker" of Financial Times Limited â€“ London, for the second consecutive year and the "National Business Excellence Awards 2010".[citation needed] It has become the third largest private sector bank in Sri Lanka with Rs. 453 billion in deposits as of 30 June 2016.[1]',
	]
	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

	#### Information Retrieval
	* Dataset: `natural-questions-dev`
	* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

	\| Metric \| Value \|
	\|:--------------------\|:-----------\|
	\| cosine_accuracy@1 \| 0.5899 \|
	\| cosine_accuracy@3 \| 0.8181 \|
	\| cosine_accuracy@5 \| 0.8876 \|
	\| cosine_accuracy@10 \| 0.9434 \|
	\| cosine_precision@1 \| 0.5899 \|
	\| cosine_precision@3 \| 0.2727 \|
	\| cosine_precision@5 \| 0.1775 \|
	\| cosine_precision@10 \| 0.0943 \|
	\| cosine_recall@1 \| 0.5899 \|
	\| cosine_recall@3 \| 0.8181 \|
	\| cosine_recall@5 \| 0.8876 \|
	\| cosine_recall@10 \| 0.9434 \|
	\| cosine_ndcg@10 \| 0.7719 \|
	\| cosine_mrr@10 \| 0.7162 \|
	\| cosine_map@100 \| 0.7188 \|
	\| dot_accuracy@1 \| 0.561 \|
	\| dot_accuracy@3 \| 0.7971 \|
	\| dot_accuracy@5 \| 0.8664 \|
	\| dot_accuracy@10 \| 0.9328 \|
	\| dot_precision@1 \| 0.561 \|
	\| dot_precision@3 \| 0.2657 \|
	\| dot_precision@5 \| 0.1733 \|
	\| dot_precision@10 \| 0.0933 \|
	\| dot_recall@1 \| 0.561 \|
	\| dot_recall@3 \| 0.7971 \|
	\| dot_recall@5 \| 0.8664 \|
	\| dot_recall@10 \| 0.9328 \|
	\| dot_ndcg@10 \| 0.7508 \|
	\| dot_mrr@10 \| 0.692 \|
	\| dot_map@100 \| 0.6951 \|

	<!--
	## 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

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 100,231 training samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.74 tokens</li><li>max: 21 tokens</li></ul> \| <ul><li>min: 17 tokens</li><li>mean: 135.66 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:----------------------------------------------------------------\|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>when did richmond last play in a preliminary final</code> \| <code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.</code> \|
	\| <code>who sang what in the world's come over you</code> \| <code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code> \|
	\| <code>who produces the most wool in the world</code> \| <code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

	### Evaluation Dataset

	#### natural-questions

	* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
	* Size: 100,231 evaluation samples
	* Columns: <code>query</code> and <code>answer</code>
	* Approximate statistics based on the first 1000 samples:
	\| \| query \| answer \|
	\|:--------\|:-----------------------------------------------------------------------------------\|:-------------------------------------------------------------------------------------\|
	\| type \| string \| string \|
	\| details \| <ul><li>min: 10 tokens</li><li>mean: 11.79 tokens</li><li>max: 25 tokens</li></ul> \| <ul><li>min: 15 tokens</li><li>mean: 142.78 tokens</li><li>max: 512 tokens</li></ul> \|
	* Samples:
	\| query \| answer \|
	\|:--------------------------------------------------------------------------\|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\|
	\| <code>who betrayed siraj ud daula in the battle of plassey in 1757</code> \| <code>Siraj ud-Daulah The Battle of Plassey (or Palashi) is widely considered the turning point in the history of the subcontinent, and opened the way to eventual British domination. After Siraj-ud-Daulah's conquest of Calcutta, the British sent fresh troops from Madras to recapture the fort and avenge the attack. A retreating Siraj-ud-Daulah met the British at Plassey. He had to make camp 27 miles away from Murshidabad. On 23 June 1757 Siraj-ud-Daulah called on Mir Jafar because he was saddened by the sudden fall of Mir Mardan who was a very dear companion of Siraj in battles. The Nawab asked for help from Mir Jafar. Mir Jafar advised Siraj to retreat for that day. The Nawab made the blunder in giving the order to stop the fight. Following his command, the soldiers of the Nawab were returning to their camps. At that time, Robert Clive attacked the soldiers with his army. At such a sudden attack, the army of Siraj became indisciplined and could think of no way to fight. So all fled away in such a situation. Betrayed by a conspiracy plotted by Jagat Seth, Mir Jafar, Krishna Chandra, Omichund etc., he lost the battle and had to escape. He went first to Murshidabad and then to Patna by boat, but was eventually arrested by Mir Jafar's soldiers.</code> \|
	\| <code>what is the meaning of single malt whisky</code> \| <code>Single malt whisky Single malt whisky is malt whisky from a single distillery, that is, whisky distilled from fermented mash made exclusively with malted grain (usually barley), as distinguished from unmalted grain.</code> \|
	\| <code>when is despicable me 3 going to release</code> \| <code>Despicable Me 3 Despicable Me 3 premiered on June 14, 2017, at the Annecy International Animated Film Festival, and was released in the United States on June 30, 2017, by Universal Pictures in 3D, RealD 3D, Dolby Cinema, and IMAX 3D. The film received mixed reviews from critics[7] and has grossed over $1 billion worldwide, making it the third highest-grossing film of 2017, the fifth highest-grossing animated film of all time and the 28th highest-grossing overall. It is Illumination's second film to gross over $1 billion, after Minions in 2015, becoming the first ever animated franchise to do so.</code> \|
	* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
	```json
	{
	"scale": 20.0,
	"similarity_fct": "cos_sim"
	}
	```

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

	- `eval_strategy`: steps
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `learning_rate`: 2e-05
	- `num_train_epochs`: 1
	- `warmup_ratio`: 0.1
	- `bf16`: True
	- `batch_sampler`: no_duplicates

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

	- `overwrite_output_dir`: False
	- `do_predict`: False
	- `eval_strategy`: steps
	- `prediction_loss_only`: True
	- `per_device_train_batch_size`: 32
	- `per_device_eval_batch_size`: 32
	- `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
	- `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`: 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, '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`: False
	- `resume_from_checkpoint`: None
	- `hub_model_id`: None
	- `hub_strategy`: every_save
	- `hub_private_repo`: False
	- `hub_always_push`: False
	- `gradient_checkpointing`: False
	- `gradient_checkpointing_kwargs`: None
	- `include_inputs_for_metrics`: False
	- `eval_do_concat_batches`: True
	- `fp16_backend`: auto
	- `push_to_hub_model_id`: None
	- `push_to_hub_organization`: None
	- `mp_parameters`:
	- `auto_find_batch_size`: False
	- `full_determinism`: False
	- `torchdynamo`: None
	- `ray_scope`: last
	- `ddp_timeout`: 1800
	- `torch_compile`: False
	- `torch_compile_backend`: None
	- `torch_compile_mode`: None
	- `dispatch_batches`: None
	- `split_batches`: None
	- `include_tokens_per_second`: False
	- `include_num_input_tokens_seen`: False
	- `neftune_noise_alpha`: None
	- `optim_target_modules`: None
	- `batch_eval_metrics`: False
	- `batch_sampler`: no_duplicates
	- `multi_dataset_batch_sampler`: proportional

	</details>

	### Training Logs
	\| Epoch \| Step \| Training Loss \| loss \| natural-questions-dev_cosine_map@100 \|
	\|:------:\|:----:\|:-------------:\|:------:\|:------------------------------------:\|
	\| 0 \| 0 \| - \| - \| 0.1228 \|
	\| 0.0004 \| 1 \| 3.0833 \| - \| - \|
	\| 0.0355 \| 100 \| 1.3516 \| 0.1545 \| 0.5151 \|
	\| 0.0711 \| 200 \| 0.1189 \| 0.0607 \| 0.6299 \|
	\| 0.1066 \| 300 \| 0.0641 \| 0.0450 \| 0.6535 \|
	\| 0.1422 \| 400 \| 0.0529 \| 0.0436 \| 0.6532 \|
	\| 0.1777 \| 500 \| 0.0601 \| 0.0349 \| 0.6716 \|
	\| 0.2133 \| 600 \| 0.0453 \| 0.0308 \| 0.6771 \|
	\| 0.2488 \| 700 \| 0.0478 \| 0.0298 \| 0.6769 \|
	\| 0.2844 \| 800 \| 0.0404 \| 0.0309 \| 0.6834 \|
	\| 0.3199 \| 900 \| 0.0377 \| 0.0275 \| 0.6855 \|
	\| 0.3555 \| 1000 \| 0.0391 \| 0.0248 \| 0.6929 \|
	\| 0.3910 \| 1100 \| 0.026 \| 0.0265 \| 0.6919 \|
	\| 0.4266 \| 1200 \| 0.0343 \| 0.0247 \| 0.6985 \|
	\| 0.4621 \| 1300 \| 0.0359 \| 0.0245 \| 0.6951 \|
	\| 0.4977 \| 1400 \| 0.0283 \| 0.0213 \| 0.6993 \|
	\| 0.5332 \| 1500 \| 0.027 \| 0.0207 \| 0.7072 \|
	\| 0.5688 \| 1600 \| 0.0313 \| 0.0223 \| 0.6980 \|
	\| 0.6043 \| 1700 \| 0.0373 \| 0.0203 \| 0.7042 \|
	\| 0.6399 \| 1800 \| 0.0245 \| 0.0199 \| 0.7049 \|
	\| 0.6754 \| 1900 \| 0.0294 \| 0.0186 \| 0.7143 \|
	\| 0.7110 \| 2000 \| 0.0185 \| 0.0185 \| 0.7116 \|
	\| 0.7465 \| 2100 \| 0.0247 \| 0.0181 \| 0.7118 \|
	\| 0.7821 \| 2200 \| 0.0221 \| 0.0183 \| 0.7142 \|
	\| 0.8176 \| 2300 \| 0.0178 \| 0.0182 \| 0.7141 \|
	\| 0.8532 \| 2400 \| 0.0235 \| 0.0170 \| 0.7172 \|
	\| 0.8887 \| 2500 \| 0.0279 \| 0.0168 \| 0.7190 \|
	\| 0.9243 \| 2600 \| 0.0278 \| 0.0167 \| 0.7188 \|
	\| 0.9598 \| 2700 \| 0.022 \| 0.0166 \| 0.7179 \|
	\| 0.9954 \| 2800 \| 0.0191 \| 0.0166 \| 0.7173 \|
	\| 1.0 \| 2813 \| - \| - \| 0.7188 \|


	### Environmental Impact
	Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
	- Energy Consumed: 0.453 kWh
	- Carbon Emitted: 0.176 kg of CO2
	- Hours Used: 1.208 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: 3.1.0.dev0
	- Transformers: 4.41.2
	- PyTorch: 2.3.1+cu121
	- Accelerate: 0.31.0
	- Datasets: 2.20.0
	- Tokenizers: 0.19.1

	## Citation

	### BibTeX

	#### Sentence Transformers
	```bibtex
	@inproceedings{reimers-2019-sentence-bert,
	title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
	author = "Reimers, Nils and Gurevych, Iryna",
	booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
	month = "11",
	year = "2019",
	publisher = "Association for Computational Linguistics",
	url = "https://arxiv.org/abs/1908.10084",
	}
	```

	#### MultipleNegativesRankingLoss
	```bibtex
	@misc{henderson2017efficient,
	title={Efficient Natural Language Response Suggestion for Smart Reply},
	author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
	year={2017},
	eprint={1705.00652},
	archivePrefix={arXiv},
	primaryClass={cs.CL}
	}
	```

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