Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "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
+}

README.md

@@ -0,0 +1,686 @@
+---
+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 $2.6 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
+    (€) 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 – 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 – 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   | 0.5861    |
+| 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.*
+-->
+
+<!--
+### 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",
+}
+```
+
+<!--
+## 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.*
+-->

config.json

@@ -0,0 +1,24 @@
+{
+  "_name_or_path": "microsoft/mpnet-base",
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.1.0.dev0",
+    "transformers": "4.41.2",
+    "pytorch": "2.3.1+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b91226771a76db743f93d13d6340b9558f94494922725708a08bbec4a10d904b
+size 437967672

modules.json

@@ -0,0 +1,14 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,51 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,65 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "special": true
+    },
+    "3": {
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+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "104": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    },
+    "30526": {
+      "content": "<mask>",
+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "mask_token": "<mask>",
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "unk_token": "[UNK]"
+}