Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": true,
+    "pooling_mode_mean_tokens": false,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

@@ -0,0 +1,712 @@
+---
+language:
+- en
+license: mit
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- dense
+- generated_from_trainer
+- dataset_size:64147
+- loss:CachedMultipleNegativesRankingLoss
+base_model: BAAI/bge-large-en-v1.5
+widget:
+- source_sentence: who is the second prime minister of india
+  sentences:
+  - List of Prime Ministers of India Since 1947, India has had fourteen Prime Ministers,
+    fifteen including Gulzarilal Nanda who twice acted in the role. The first was
+    Jawaharlal Nehru of the Indian National Congress party, who was sworn-in on 15
+    August 1947, when India gained independence from the British. Serving until his
+    death in May 1964, Nehru remains India's longest-serving prime minister. He was
+    succeeded by fellow Congressman Lal Bahadur Shastri, whose 19-month term also
+    ended in death. Indira Gandhi, Nehru's daughter, succeeded Shastri in 1966 to
+    become the country's first woman premier. Eleven years later, she was voted out
+    of power in favour of the Janata Party, whose leader Morarji Desai became the
+    first non-Congress prime minister. After he resigned in 1979, his former deputy
+    Charan Singh briefly held office until Indira Gandhi was voted back six months
+    later. Indira Gandhi's second stint as Prime Minister ended five years later on
+    the morning of 31 October 1984, when she was gunned down by her own bodyguards.
+    That evening, her son Rajiv Gandhi was sworn-in as India's youngest premier, and
+    the third from his family. Thus far, members of Nehru–Gandhi dynasty have been
+    Prime Minister for a total of 37 years and 303 days.[1]
+  - Can You Feel the Love Tonight The song was performed in the film by Kristle Edwards,
+    Joseph Williams, Sally Dworsky, Nathan Lane, and Ernie Sabella, while the end
+    title version was performed by Elton John. It won the 1994 Academy Award for Best
+    Original Song,[1] and the Golden Globe Award for Best Original Song. It also earned
+    Elton John the Grammy Award for Best Male Pop Vocal Performance.
+  - 'Sam Worthington Samuel Henry John Worthington[1] (born 2 August 1976) is an English
+    born, Australian actor and writer. He portrayed Jake Sully in the 2009 film Avatar,
+    Marcus Wright in Terminator Salvation, and Perseus in Clash of the Titans as well
+    as its sequel Wrath of the Titans before transitioning to more dramatic roles
+    in Everest (2015), Hacksaw Ridge (2016), The Shack, and Manhunt: Unabomber (both
+    in 2017). He also played the main protagonist, Captain Alex Mason, in Call of
+    Duty: Black Ops.'
+- source_sentence: who drafted most of the declaration of independence
+  sentences:
+  - 'United States Declaration of Independence John Adams persuaded the committee
+    to select Thomas Jefferson to compose the original draft of the document,[3] which
+    Congress would edit to produce the final version. The Declaration was ultimately
+    a formal explanation of why Congress had voted on July 2 to declare independence
+    from Great Britain, more than a year after the outbreak of the American Revolutionary
+    War. The next day, Adams wrote to his wife Abigail: "The Second Day of July 1776,
+    will be the most memorable Epocha, in the History of America."[4] But Independence
+    Day is actually celebrated on July 4, the date that the Declaration of Independence
+    was approved.'
+  - Luke Cage (season 2) The season is set to premiere in 2018.
+  - Politics of the European Union The competencies of the European Union stem from
+    the original Coal and Steel Community, which had as its goal an integrated market.
+    The original competencies were regulatory in nature, restricted to matters of
+    maintaining a healthy business environment. Rulings were confined to laws covering
+    trade, currency, and competition. Increases in the number of EU competencies result
+    from a process known as functional spillover. Functional spillover resulted in,
+    first, the integration of banking and insurance industries to manage finance and
+    investment. The size of the bureaucracies increased, requiring modifications to
+    the treaty system as the scope of competencies integrated more and more functions.
+    While member states hold their sovereignty inviolate, they remain within a system
+    to which they have delegated the tasks of managing the marketplace. These tasks
+    have expanded to include the competencies of free movement of persons, employment,
+    transportation, and environmental regulation.
+- source_sentence: is there a difference between 300 blackout and 300 aac blackout
+  sentences:
+  - 'Call of Duty: World at War Call of Duty: World at War is a 2008 first-person
+    shooter video game developed by Treyarch and published by Activision for Microsoft
+    Windows, PlayStation 3, Wii, and Xbox 360. The game is the fifth mainstream game
+    of the Call of Duty series and returns the setting to World War II for the last
+    time until Call of Duty: WWII almost nine years later. The game is also the first
+    title in the Black Ops story line. The game was released in North America on November
+    11, 2008, and in Europe on November 14, 2008. A Windows Mobile version was also
+    made available by Glu Mobile and different storyline versions for the Nintendo
+    DS and PlayStation 2 were also produced, but remain in the World War II setting.
+    The game is based on an enhanced version of the Call of Duty 4: Modern Warfare
+    game engine developed by Infinity Ward with increased development on audio and
+    visual effects.'
+  - Vincent van Gogh Van Gogh suffered from psychotic episodes and delusions and though
+    he worried about his mental stability, he often neglected his physical health,
+    did not eat properly and drank heavily. His friendship with Gauguin ended after
+    a confrontation with a razor, when in a rage, he severed part of his own left
+    ear. He spent time in psychiatric hospitals, including a period at Saint-Rémy.
+    After he discharged himself and moved to the Auberge Ravoux in Auvers-sur-Oise
+    near Paris, he came under the care of the homoeopathic doctor Paul Gachet. His
+    depression continued and on 27 July 1890, Van Gogh shot himself in the chest with
+    a revolver. He died from his injuries two days later.
+  - .300 AAC Blackout The .300 AAC Blackout (designated as the 300 BLK by the SAAMI[1]
+    and 300 AAC Blackout by the C.I.P.[2]), also known as 7.62×35mm is a carbine cartridge
+    developed in the United States by Advanced Armament Corporation (AAC) for use
+    in the M4 carbine. Its purpose is to achieve ballistics similar to the 7.62×39mm
+    Soviet cartridge in an AR-15 while using standard AR-15 magazines at their normal
+    capacity. It can be seen as a SAAMI-certified copy of J. D. Jones' wildcat .300
+    Whisper. Care should be taken not to use 300 BLK ammunition in a rifle chambered
+    for 7.62×40mm Wilson Tactical.[3]
+- source_sentence: when does the new army uniform come out
+  sentences:
+  - United States v. Paramount Pictures, Inc. The case reached the U.S. Supreme Court
+    in 1948; their verdict went against the movie studios, forcing all of them to
+    divest themselves of their movie theater chains.[8] This, coupled with the advent
+    of television and the attendant drop in movie ticket sales, brought about a severe
+    slump in the movie business, a slump that would not be reversed until 1972, with
+    the release of The Godfather, the first modern blockbuster.
+  - 'E. L. James James says the idea for the Fifty Shades trilogy began as a response
+    to the vampire novel series Twilight. In late 2008 James saw the movie Twilight,
+    and then became intensely absorbed with the novels that the movie was based on.
+    She read the novels several times over in a period of a few days, and then, for
+    the first time in her life, sat down to write a book: basically a sequel to the
+    Twilight novels. Between January and August 2009 she wrote two such books in quick
+    succession. She says she then discovered the phenomenon of fan fiction, and this
+    inspired her to publish her novels as Kindle books under the pen name "Snowqueens
+    Icedragon". Beginning in August 2009 she then began to write the Fifty Shades
+    books.[12][13]'
+  - Army Combat Uniform In May 2014, the Army unofficially announced that the Operational
+    Camouflage Pattern (OCP) would replace UCP on the ACU. The original "Scorpion"
+    pattern was developed at United States Army Soldier Systems Center by Crye Precision
+    in 2002 for the Objective Force Warrior program. Crye later modified and trademarked
+    their version of the pattern as MultiCam, which was selected for use by U.S. soldiers
+    in Afghanistan in 2010. After talks to officially adopt MultiCam broke down over
+    costs in late 2013, the Army began experimenting with the original Scorpion pattern,
+    creating a variant code named "Scorpion W2", noting that while a pattern can be
+    copyrighted, a color palette cannot and that beyond 50 meters the actual pattern
+    is "not that relevant." The pattern resembles MultiCam with muted greens, light
+    beige, and dark brown colors, but uses fewer beige and brown patches and no vertical
+    twig and branch elements.[12] On 31 July 2014, the Army formally announced that
+    the pattern would begin being issued in uniforms in summer 2015. The official
+    name is intended to emphasize its use beyond Afghanistan to all combatant commands.[13]
+    The UCP pattern is planned to be fully replaced by the OCP on the ACU by 1 October
+    2019.[14] ACUs printed in OCP first became available for purchase on 1 July 2015,
+    with deployed soldiers already being issued uniforms and equipment in the new
+    pattern.[15]
+- source_sentence: what was agenda 21 of earth summit of rio de janeiro
+  sentences:
+  - 'Jab Harry Met Sejal Jab Harry Met Sejal (English: When Harry Met Sejal) is a
+    2017 Indian romantic comedy film written and directed by Imtiaz Ali. It features
+    Shah Rukh Khan and Anushka Sharma in the lead roles,[1] their third collaboration
+    after Rab Ne Bana Di Jodi (2008) and Jab Tak Hai Jaan (2012). Pre-production of
+    the film begun in April 2015 and principal photography commenced in August 2016
+    in Prague, Amsterdam, Vienna, Lisbon and Budapest.'
+  - Agenda 21 Agenda 21 is a non-binding, action plan of the United Nations with regard
+    to sustainable development.[1] It is a product of the Earth Summit (UN Conference
+    on Environment and Development) held in Rio de Janeiro, Brazil, in 1992. It is
+    an action agenda for the UN, other multilateral organizations, and individual
+    governments around the world that can be executed at local, national, and global
+    levels.
+  - Pencil Most manufacturers, and almost all in Europe, designate their pencils with
+    the letters H (commonly interpreted as "hardness") to B (commonly "blackness"),
+    as well as F (usually taken to mean "fineness", although F pencils are no more
+    fine or more easily sharpened than any other grade. also known as "firm" in Japan[68]).
+    The standard writing pencil is graded HB.[69] This designation might have been
+    first used in the early 20th century by Brookman, an English pencil maker. It
+    used B for black and H for hard; a pencil's grade was described by a sequence
+    or successive Hs or Bs such as BB and BBB for successively softer leads, and HH
+    and HHH for successively harder ones.[70] The Koh-i-Noor Hardtmuth pencil manufacturers
+    claim to have first used the HB designations, with H standing for Hardtmuth, B
+    for the company's location of Budějovice, and F for Franz Hardtmuth, who was responsible
+    for technological improvements in pencil manufacture.[71][72]
+datasets:
+- sentence-transformers/natural-questions
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+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
+model-index:
+- name: bge-large-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: NanoQuoraRetrieval
+      type: NanoQuoraRetrieval
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.88
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.98
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.88
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3999999999999999
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.25999999999999995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.13599999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7673333333333332
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.922
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.966
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9933333333333334
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9311833586321692
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9228888888888889
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9056754689754689
+      name: Cosine Map@100
+    - type: cosine_accuracy@1
+      value: 0.88
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.96
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.98
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 1.0
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.88
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.3999999999999999
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.25999999999999995
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.13599999999999998
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.7673333333333332
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.922
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.966
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9933333333333334
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.9311833586321692
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.9228888888888889
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.9056754689754689
+      name: Cosine Map@100
+---
+
+# bge-large-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset. It maps sentences & paragraphs to a 1024-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:** [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
+- **Language:** en
+- **License:** mit
+
+### 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': True, 'architecture': 'BertModel'})
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("DannyAI/embedding_fine_tuning_with_prompts_bge_large_en_v1.5")
+# Run inference
+queries = [
+    "what was agenda 21 of earth summit of rio de janeiro",
+]
+documents = [
+    'Agenda 21 Agenda 21 is a non-binding, action plan of the United Nations with regard to sustainable development.[1] It is a product of the Earth Summit (UN Conference on Environment and Development) held in Rio de Janeiro, Brazil, in 1992. It is an action agenda for the UN, other multilateral organizations, and individual governments around the world that can be executed at local, national, and global levels.',
+    'Jab Harry Met Sejal Jab Harry Met Sejal (English: When Harry Met Sejal) is a 2017 Indian romantic comedy film written and directed by Imtiaz Ali. It features Shah Rukh Khan and Anushka Sharma in the lead roles,[1] their third collaboration after Rab Ne Bana Di Jodi (2008) and Jab Tak Hai Jaan (2012). Pre-production of the film begun in April 2015 and principal photography commenced in August 2016 in Prague, Amsterdam, Vienna, Lisbon and Budapest.',
+    'Pencil Most manufacturers, and almost all in Europe, designate their pencils with the letters H (commonly interpreted as "hardness") to B (commonly "blackness"), as well as F (usually taken to mean "fineness", although F pencils are no more fine or more easily sharpened than any other grade. also known as "firm" in Japan[68]). The standard writing pencil is graded HB.[69] This designation might have been first used in the early 20th century by Brookman, an English pencil maker. It used B for black and H for hard; a pencil\'s grade was described by a sequence or successive Hs or Bs such as BB and BBB for successively softer leads, and HH and HHH for successively harder ones.[70] The Koh-i-Noor Hardtmuth pencil manufacturers claim to have first used the HB designations, with H standing for Hardtmuth, B for the company\'s location of Budějovice, and F for Franz Hardtmuth, who was responsible for technological improvements in pencil manufacture.[71][72]',
+]
+query_embeddings = model.encode_query(queries)
+document_embeddings = model.encode_document(documents)
+print(query_embeddings.shape, document_embeddings.shape)
+# [1, 1024] [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(query_embeddings, document_embeddings)
+print(similarities)
+# tensor([[0.9017, 0.2307, 0.2148]])
+```
+
+<!--
+### 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: `NanoQuoraRetrieval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "query_prompt": "query: ",
+      "corpus_prompt": "document: "
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.88       |
+| cosine_accuracy@3   | 0.96       |
+| cosine_accuracy@5   | 0.98       |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.88       |
+| cosine_precision@3  | 0.4        |
+| cosine_precision@5  | 0.26       |
+| cosine_precision@10 | 0.136      |
+| cosine_recall@1     | 0.7673     |
+| cosine_recall@3     | 0.922      |
+| cosine_recall@5     | 0.966      |
+| cosine_recall@10    | 0.9933     |
+| **cosine_ndcg@10**  | **0.9312** |
+| cosine_mrr@10       | 0.9229     |
+| cosine_map@100      | 0.9057     |
+
+#### Information Retrieval
+
+* Dataset: `NanoQuoraRetrieval`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) with these parameters:
+  ```json
+  {
+      "query_prompt": "query: ",
+      "corpus_prompt": "document: "
+  }
+  ```
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.88       |
+| cosine_accuracy@3   | 0.96       |
+| cosine_accuracy@5   | 0.98       |
+| cosine_accuracy@10  | 1.0        |
+| cosine_precision@1  | 0.88       |
+| cosine_precision@3  | 0.4        |
+| cosine_precision@5  | 0.26       |
+| cosine_precision@10 | 0.136      |
+| cosine_recall@1     | 0.7673     |
+| cosine_recall@3     | 0.922      |
+| cosine_recall@5     | 0.966      |
+| cosine_recall@10    | 0.9933     |
+| **cosine_ndcg@10**  | **0.9312** |
+| cosine_mrr@10       | 0.9229     |
+| cosine_map@100      | 0.9057     |
+
+<!--
+## 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: 64,147 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: 11.81 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 21 tokens</li><li>mean: 137.28 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query                                                                               | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>the internal revenue code is part of federal statutory law. true false</code> | <code>Internal Revenue Code The Internal Revenue Code (IRC), formally the Internal Revenue Code of 1986, is the domestic portion of federal statutory tax law in the United States, published in various volumes of the United States Statutes at Large, and separately as Title 26 of the United States Code (USC).[1] It is organized topically, into subtitles and sections, covering income tax (see Income tax in the United States), payroll taxes, estate taxes, gift taxes, and excise taxes; as well as procedure and administration. Its implementing agency is the Internal Revenue Service.</code>                                                                                                                                                     |
+  | <code>where is the pyramid temple at borobudur located</code>                       | <code>Borobudur Approximately 40 kilometres (25 mi) northwest of Yogyakarta and 86 kilometres (53 mi) west of Surakarta, Borobudur is located in an elevated area between two twin volcanoes, Sundoro-Sumbing and Merbabu-Merapi, and two rivers, the Progo and the Elo. According to local myth, the area known as Kedu Plain is a Javanese "sacred" place and has been dubbed "the garden of Java" due to its high agricultural fertility.[19] During the restoration in the early 20th century, it was discovered that three Buddhist temples in the region, Borobudur, Pawon and Mendut, are positioned along a straight line.[20] A ritual relationship between the three temples must have existed, although the exact ritual process is unknown.[14]</code> |
+  | <code>what does uncle stand for in the show man from uncle</code>                   | <code>The Man from U.N.C.L.E. Originally, co-creator Sam Rolfe wanted to leave the meaning of U.N.C.L.E. ambiguous so it could refer to either "Uncle Sam" or the United Nations.[2]:14 Concerns by Metro-Goldwyn-Mayer's (MGM) legal department about using "U.N." for commercial purposes resulted in the producers' clarification that U.N.C.L.E. was an acronym for the United Network Command for Law and Enforcement.[3] Each episode had an "acknowledgement" to the U.N.C.L.E. in the end titles.</code>                                                                                                                                                                                                                                                   |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 16,
+      "gather_across_devices": false
+  }
+  ```
+
+### 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: 16,037 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: 11.67 tokens</li><li>max: 22 tokens</li></ul> | <ul><li>min: 12 tokens</li><li>mean: 134.64 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | query                                                     | answer                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  |:----------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>when did last harry potter movie come out</code>    | <code>Harry Potter (film series) Harry Potter is a British-American film series based on the Harry Potter novels by author J. K. Rowling. The series is distributed by Warner Bros. and consists of eight fantasy films, beginning with Harry Potter and the Philosopher's Stone (2001) and culminating with Harry Potter and the Deathly Hallows – Part 2 (2011).[2][3] A spin-off prequel series will consist of five films, starting with Fantastic Beasts and Where to Find Them (2016). The Fantastic Beasts films mark the beginning of a shared media franchise known as J. K. Rowling's Wizarding World.[4]</code>                                                                                                                                                      |
+  | <code>where did the saying debbie downer come from</code> | <code>Debbie Downer The character's name, Debbie Downer, is a slang phrase which refers to someone who frequently adds bad news and negative feelings to a gathering, thus bringing down the mood of everyone around them. Dratch's character would usually appear at social gatherings and interrupt the conversation to voice negative opinions and pronouncements. She is especially concerned about the rate of feline AIDS, a subject that she would bring up on more than one occasion, saying it was the number one killer of domestic cats.</code>                                                                                                                                                                                                                      |
+  | <code>the financial crisis of 2008 was caused by</code>   | <code>Financial crisis of 2007–2008 It began in 2007 with a crisis in the subprime mortgage market in the United States, and developed into a full-blown international banking crisis with the collapse of the investment bank Lehman Brothers on September 15, 2008.[5] Excessive risk-taking by banks such as Lehman Brothers helped to magnify the financial impact globally.[6] Massive bail-outs of financial institutions and other palliative monetary and fiscal policies were employed to prevent a possible collapse of the world financial system. The crisis was nonetheless followed by a global economic downturn, the Great Recession. The European debt crisis, a crisis in the banking system of the European countries using the euro, followed later.</code> |
+* Loss: [<code>CachedMultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim",
+      "mini_batch_size": 16,
+      "gather_across_devices": false
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 5
+- `per_device_eval_batch_size`: 5
+- `learning_rate`: 2e-05
+- `max_steps`: 100
+- `warmup_ratio`: 0.1
+- `seed`: 30
+- `bf16`: True
+- `load_best_model_at_end`: True
+- `prompts`: {'query': 'query: ', 'answer': 'document: '}
+- `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`: 5
+- `per_device_eval_batch_size`: 5
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_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`: 3.0
+- `max_steps`: 100
+- `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`: 30
+- `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`: True
+- `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}
+- `parallelism_config`: None
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch_fused
+- `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`: None
+- `hub_always_push`: False
+- `hub_revision`: None
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `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
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `liger_kernel_config`: None
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: {'query': 'query: ', 'answer': 'document: '}
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+- `router_mapping`: {}
+- `learning_rate_mapping`: {}
+
+</details>
+
+### Training Logs
+| Epoch      | Step    | Training Loss | Validation Loss | NanoQuoraRetrieval_cosine_ndcg@10 |
+|:----------:|:-------:|:-------------:|:---------------:|:---------------------------------:|
+| -1         | -1      | -             | -               | 0.9583                            |
+| **0.0078** | **100** | **0.0063**    | **0.0029**      | **0.9312**                        |
+| -1         | -1      | -             | -               | 0.9312                            |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.12.11
+- Sentence Transformers: 5.1.0
+- Transformers: 4.56.1
+- PyTorch: 2.8.0+cu126
+- Accelerate: 1.10.1
+- Datasets: 4.0.0
+- Tokenizers: 0.22.0
+
+## 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",
+}
+```
+
+#### CachedMultipleNegativesRankingLoss
+```bibtex
+@misc{gao2021scaling,
+    title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup},
+    author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan},
+    year={2021},
+    eprint={2101.06983},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+<!--
+## 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 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "transformers_version": "4.56.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "sentence_transformers": "5.1.0",
+    "transformers": "4.56.1",
+    "pytorch": "2.8.0+cu126"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b1fe310985cdd22d996028fa139271c157e1a4e5aa3115e9f1575cdf8b003097
+size 1340612432

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

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

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}