sentence-transformers safetensors mpnet sentence-similarity feature-extraction generated_from_trainer dataset_size:100231 loss:MultipleNegativesSymmetricRankingLoss en dataset:sentence-transformers/natural-questions arxiv:1908.10084 base_model:microsoft/mpnet-base base_model:finetune:microsoft/mpnet-base license:apache-2.0 model-index co2_eq_emissions autotrain_compatible endpoints_compatible region:us
---
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:MultipleNegativesSymmetricRankingLoss
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: 165.0033810952838
  energy_consumed: 0.42449841033821234
  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: 1.092
  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: 0.5860619685270257
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.8133124816733457
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.8825139282572574
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.9408659955038609
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.5860619685270257
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.2711041605577819
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.1765027856514515
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.09408659955038609
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.5860619685270257
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.8133124816733457
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.8825139282572574
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.9408659955038609
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.768255166297218
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.7122767251102616
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.7149616399245906
      name: Cosine Map@100
    - type: dot_accuracy@1
      value: 0.5611377186980745
      name: Dot Accuracy@1
    - type: dot_accuracy@3
      value: 0.7913204965301535
      name: Dot Accuracy@3
    - type: dot_accuracy@5
      value: 0.8638451764245919
      name: Dot Accuracy@5
    - type: dot_accuracy@10
      value: 0.929039194604633
      name: Dot Accuracy@10
    - type: dot_precision@1
      value: 0.5611377186980745
      name: Dot Precision@1
    - type: dot_precision@3
      value: 0.26377349884338447
      name: Dot Precision@3
    - type: dot_precision@5
      value: 0.1727690352849184
      name: Dot Precision@5
    - type: dot_precision@10
      value: 0.09290391946046331
      name: Dot Precision@10
    - type: dot_recall@1
      value: 0.5611377186980745
      name: Dot Recall@1
    - type: dot_recall@3
      value: 0.7913204965301535
      name: Dot Recall@3
    - type: dot_recall@5
      value: 0.8638451764245919
      name: Dot Recall@5
    - type: dot_recall@10
      value: 0.929039194604633
      name: Dot Recall@10
    - type: dot_ndcg@10
      value: 0.748422205969158
      name: Dot Ndcg@10
    - type: dot_mrr@10
      value: 0.6900571403747948
      name: Dot Mrr@10
    - type: dot_map@100
      value: 0.6933908770247191
      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-mnsrl")
# 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>
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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## 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   | 0.5861    |
| cosine_accuracy@3   | 0.8133    |
| cosine_accuracy@5   | 0.8825    |
| cosine_accuracy@10  | 0.9409    |
| cosine_precision@1  | 0.5861    |
| cosine_precision@3  | 0.2711    |
| cosine_precision@5  | 0.1765    |
| cosine_precision@10 | 0.0941    |
| cosine_recall@1     | 0.5861    |
| cosine_recall@3     | 0.8133    |
| cosine_recall@5     | 0.8825    |
| cosine_recall@10    | 0.9409    |
| cosine_ndcg@10      | 0.7683    |
| cosine_mrr@10       | 0.7123    |
| **cosine_map@100**  | **0.715** |
| dot_accuracy@1      | 0.5611    |
| dot_accuracy@3      | 0.7913    |
| dot_accuracy@5      | 0.8638    |
| dot_accuracy@10     | 0.929     |
| dot_precision@1     | 0.5611    |
| dot_precision@3     | 0.2638    |
| dot_precision@5     | 0.1728    |
| dot_precision@10    | 0.0929    |
| dot_recall@1        | 0.5611    |
| dot_recall@3        | 0.7913    |
| dot_recall@5        | 0.8638    |
| dot_recall@10       | 0.929     |
| dot_ndcg@10         | 0.7484    |
| dot_mrr@10          | 0.6901    |
| dot_map@100         | 0.6934    |

<!--
## 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.*
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<!--
### 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>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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>MultipleNegativesSymmetricRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativessymmetricrankingloss) 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    | 3.2095        | -      | -                                    |
| 0.0355 | 100  | 1.4827        | 0.1693 | 0.5042                               |
| 0.0711 | 200  | 0.1299        | 0.0592 | 0.6293                               |
| 0.1066 | 300  | 0.0633        | 0.0456 | 0.6516                               |
| 0.1422 | 400  | 0.0516        | 0.0423 | 0.6512                               |
| 0.1777 | 500  | 0.0616        | 0.0352 | 0.6649                               |
| 0.2133 | 600  | 0.0424        | 0.0325 | 0.6758                               |
| 0.2488 | 700  | 0.0475        | 0.0301 | 0.6756                               |
| 0.2844 | 800  | 0.0394        | 0.0311 | 0.6861                               |
| 0.3199 | 900  | 0.0382        | 0.0278 | 0.6868                               |
| 0.3555 | 1000 | 0.0396        | 0.0250 | 0.6932                               |
| 0.3910 | 1100 | 0.0236        | 0.0266 | 0.6861                               |
| 0.4266 | 1200 | 0.0348        | 0.0241 | 0.6945                               |
| 0.4621 | 1300 | 0.0361        | 0.0240 | 0.6958                               |
| 0.4977 | 1400 | 0.0286        | 0.0224 | 0.6954                               |
| 0.5332 | 1500 | 0.024         | 0.0213 | 0.7016                               |
| 0.5688 | 1600 | 0.0325        | 0.0236 | 0.6923                               |
| 0.6043 | 1700 | 0.0385        | 0.0217 | 0.6988                               |
| 0.6399 | 1800 | 0.0258        | 0.0211 | 0.6974                               |
| 0.6754 | 1900 | 0.0282        | 0.0198 | 0.7075                               |
| 0.7110 | 2000 | 0.022         | 0.0201 | 0.7050                               |
| 0.7465 | 2100 | 0.025         | 0.0193 | 0.7042                               |
| 0.7821 | 2200 | 0.0248        | 0.0189 | 0.7069                               |
| 0.8176 | 2300 | 0.0206        | 0.0193 | 0.7079                               |
| 0.8532 | 2400 | 0.0228        | 0.0182 | 0.7122                               |
| 0.8887 | 2500 | 0.0316        | 0.0178 | 0.7117                               |
| 0.9243 | 2600 | 0.0296        | 0.0176 | 0.7130                               |
| 0.9598 | 2700 | 0.025         | 0.0175 | 0.7135                               |
| 0.9954 | 2800 | 0.0217        | 0.0174 | 0.7139                               |
| 1.0    | 2813 | -             | -      | 0.7150                               |


### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.424 kWh
- **Carbon Emitted**: 0.165 kg of CO2
- **Hours Used**: 1.092 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",
}
```

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