bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/README.md

---
base_model: BAAI/bge-small-en
datasets:
- sentence-transformers/hotpotqa
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy
- dot_accuracy
- manhattan_accuracy
- euclidean_accuracy
- max_accuracy
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:76064
- loss:MatryoshkaLoss
- loss:TripletLoss
widget:
- source_sentence: The person who released "Sun Arise" was born in what year?
  sentences:
  - Peter Frampton Peter Kenneth Frampton (born 22 April 1950) is an English rock
    musician, singer, songwriter, producer, and guitarist. He was previously associated
    with the bands Humble Pie and The Herd. At the end of his 'group' career was Frampton's
    international breakthrough album his live release, "Frampton Comes Alive!" The
    album sold in the United States more than 8 million copies and spawned several
    single hits. Since then he has released several major albums. He has also worked
    with David Bowie and both Matt Cameron and Mike McCready from Pearl Jam, among
    others.
  - Sun Arise "Sun Arise" is the fourth single released by the Australian singer-songwriter
    Rolf Harris. Released in January 1961 in Australia and October 1962 in the UK,
    it was Harris' third charting hit in Australia (following "The Big Black Hat"
    in 1960) and second in the UK (following "Tie Me Kangaroo Down, Sport" also 1960).
    Unlike his early chart hits, "Sun Arise" was not a comedy record, but came within
    the genre of world music with its didgeridoo-inspired sound.
  - Circa Survive Circa Survive is an American rock band from the Philadelphia suburb
    of Doylestown, formed in 2004. The band, led by Anthony Green, consists of former
    members from Saosin, This Day Forward, and Taken.
- source_sentence: What year was Chuang Chia-jung's partner in the 2010 MPS Group
    Championships – Doubles born?
  sentences:
  - Ko Olina Station and Center Ko Olina Station and Ko Olina Center make up a lifestyle
    center in the resort town of Ko Olina, a neighborhood in Kapolei, Hawaii. The
    shopping mall opened in 2009 and consists of two centers located across a street
    from each other. Ko Olina Station debuted in 2009, while the more recent Ko Olina
    Center finished construction in 2010. The centers contain a total of approximately
    31 retail tenants, with the majority of them being native Hawaiian businesses,
    such as ABC Stores and Peter Merriman's MonkeyPod Kitchen.
  - 2010 MPS Group Championships – Doubles Chuang Chia-jung and Sania Mirza were the
    defenders of championship title, but Mirza chose not to compete.
  - Lu Chia-hung Lu Chia-hung (; born 4 March 1997) is a Taiwanese male badminton
    player.
- source_sentence: What son of Zeus in Greek mythology was said to have fatheres an
    Argonaut seer?
  sentences:
  - All Net Resort and Arena All Net Resort and Arena is a planned entertainment complex
    in Las Vegas. A project of businessman and former basketball player Jackie Robinson,
    the complex would encompass a resort hotel, a retail and restaurant streetscape,
    and a multi-purpose indoor arena with a retractable roof. Its location is set
    on the Strip at the former site of a Wet 'n Wild waterpark, next to the SLS Las
    Vegas in Winchester, Nevada. Designed by the Cuningham Group, it was planned to
    open in 2017, but is delayed until 2018 or 2019.
  - 'Piras (mythology) In Greek mythology, Piras (Ancient Greek: Πείραντα) was a king
    of Argos, otherwise also known as Piren, Peiren, Peiras, Peirasus and Piranthus.'
  - Idmon In Greek mythology, Idmon was an Argonaut seer. Allegedly a son of Apollo,
    he had Abas (or Ampycus) as his mortal father. His mother was Asteria, daughter
    of Coronus, or Cyrene, or else Antianeira, daughter of Pheres. By Laothoe he had
    a son Thestor. Idmon foresaw his own death in the Argonaut expedition, but joined
    anyway. During the outbound voyage of "Argo", a boar killed him in the land of
    the Mariandyni, in Bithynia.
- source_sentence: In what year was the drama film in which Dorothy Duffy played Rose
    / Patricia released?
  sentences:
  - Keith Davis (safety) Keith Lamont Davis (born December 30, 1978) is a former American
    football safety in the National Football League for the Dallas Cowboys. He played
    college football at Sam Houston State University.
  - Dorothy Duffy Dorothy Duffy (born in Douglas Bridge, Northern Ireland) is an Irish
    actress. She is best known for her performance as Rose / Patricia in "The Magdalene
    Sisters".
  - The Franchise Affair (film) The Franchise Affair is a 1951 British thriller film
    directed by Lawrence Huntington and starring Michael Denison, Dulcie Gray, Anthony
    Nicholls and Marjorie Fielding. It is a faithful adaptation of the novel "The
    Franchise Affair" by Josephine Tey.
- source_sentence: Was McDull, Kung Fu Kindergarten or Pettson and Findus created
    first?
  sentences:
  - 'Tabaluga Tabaluga is a media franchise featuring a fictional little green Dragon
    of the same name, created by German Rock musician Peter Maffay, children''s songwriter
    and the author . The artist Helme Heine drew the image of Tabaluga as it is currently
    known. The character Tabaluga was first introduced by Peter Maffay in a musical
    fairy tale "Tabaluga ... oder die Reise zur Vernunft" (Tabaluga or... The Journey
    to Reason) in 1983. This first studio album was the step to success: within the
    next years some Helme Heine books, four sequel concept studio albums, two resounding
    tours, a stage musical, "Tabaluga und Lilli" ("Tabaluga and Lilli"), based on
    the third concept album and many TV Cartoons which have been broadcasting in over
    100 countries round the world followed and a children''s game show. Over 100 kindergartens
    and child care groups carry the word "Tabaluga" in their names.'
  - 2005–06 FC Bayern Munich season FC Bayern Munich won the domestic double, beating
    Werder Bremen by five points in Bundesliga, and defeating Eintracht Frankfurt
    1–0 in the DFB-Pokal final, thanks to a goal from Claudio Pizarro. The season
    was in spite of that tainted due to a big defeat to Milan in the UEFA Champions
    League, losing out 5–2 on aggregate in the Last 16. At the end of the season,
    Bayern signed German football's wonderkid Lukas Podolski from Köln.
  - 'Pettson and Findus Pettson and Findus (Swedish: "Pettson och Findus" ) is a series
    of children''s books written and illustrated by Swedish author Sven Nordqvist.
    The books feature an old farmer (Pettson) and his cat (Findus) who live in a small
    ramshackle farmhouse in the countryside. The first of the Pettson och Findus book
    to be published was "Pannkakstårtan" in 1984 (first published in English in 1985
    as "Pancake Pie").'
model-index:
- name: BGE-base-en-v1.5-Hotpotqa
  results:
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: dim 384
      type: dim_384
    metrics:
    - type: cosine_accuracy
      value: 0.8853525792711784
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.11464742072882159
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8862991008045433
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8853525792711784
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8862991008045433
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy
      value: 0.8840511121628017
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.11571225745385708
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8851159488878372
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8841694273544723
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8851159488878372
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy
      value: 0.8829862754377662
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.11831519167061051
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.8823946994794132
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.8836961665877898
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.8836961665877898
      name: Max Accuracy
  - task:
      type: triplet
      name: Triplet
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy
      value: 0.8815664931377188
      name: Cosine Accuracy
    - type: dot_accuracy
      value: 0.12434926644581164
      name: Dot Accuracy
    - type: manhattan_accuracy
      value: 0.88180312352106
      name: Manhattan Accuracy
    - type: euclidean_accuracy
      value: 0.88180312352106
      name: Euclidean Accuracy
    - type: max_accuracy
      value: 0.88180312352106
      name: Max Accuracy
---

# BGE-base-en-v1.5-Hotpotqa

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-small-en](https://huggingface.co/BAAI/bge-small-en) on the [sentence-transformers/hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa) dataset. It maps sentences & paragraphs to a 384-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-small-en](https://huggingface.co/BAAI/bge-small-en) <!-- at revision 2275a7bdee235e9b4f01fa73aa60d3311983cfea -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - [sentence-transformers/hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa)
- **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': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 384, '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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Was McDull, Kung Fu Kindergarten or Pettson and Findus created first?',
    'Pettson and Findus Pettson and Findus (Swedish: "Pettson och Findus" ) is a series of children\'s books written and illustrated by Swedish author Sven Nordqvist. The books feature an old farmer (Pettson) and his cat (Findus) who live in a small ramshackle farmhouse in the countryside. The first of the Pettson och Findus book to be published was "Pannkakstårtan" in 1984 (first published in English in 1985 as "Pancake Pie").',
    'Tabaluga Tabaluga is a media franchise featuring a fictional little green Dragon of the same name, created by German Rock musician Peter Maffay, children\'s songwriter and the author . The artist Helme Heine drew the image of Tabaluga as it is currently known. The character Tabaluga was first introduced by Peter Maffay in a musical fairy tale "Tabaluga ... oder die Reise zur Vernunft" (Tabaluga or... The Journey to Reason) in 1983. This first studio album was the step to success: within the next years some Helme Heine books, four sequel concept studio albums, two resounding tours, a stage musical, "Tabaluga und Lilli" ("Tabaluga and Lilli"), based on the third concept album and many TV Cartoons which have been broadcasting in over 100 countries round the world followed and a children\'s game show. Over 100 kindergartens and child care groups carry the word "Tabaluga" in their names.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```

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</details>
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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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## Evaluation

### Metrics

#### Triplet
* Dataset: `dim_384`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8854** |
| dot_accuracy        | 0.1146     |
| manhattan_accuracy  | 0.8863     |
| euclidean_accuracy  | 0.8854     |
| max_accuracy        | 0.8863     |

#### Triplet
* Dataset: `dim_256`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8841** |
| dot_accuracy        | 0.1157     |
| manhattan_accuracy  | 0.8851     |
| euclidean_accuracy  | 0.8842     |
| max_accuracy        | 0.8851     |

#### Triplet
* Dataset: `dim_128`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value     |
|:--------------------|:----------|
| **cosine_accuracy** | **0.883** |
| dot_accuracy        | 0.1183    |
| manhattan_accuracy  | 0.8824    |
| euclidean_accuracy  | 0.8837    |
| max_accuracy        | 0.8837    |

#### Triplet
* Dataset: `dim_64`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| **cosine_accuracy** | **0.8816** |
| dot_accuracy        | 0.1243     |
| manhattan_accuracy  | 0.8818     |
| euclidean_accuracy  | 0.8818     |
| max_accuracy        | 0.8818     |

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## Bias, Risks and Limitations

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### Recommendations

*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details

### Training Dataset

#### sentence-transformers/hotpotqa

* Dataset: [sentence-transformers/hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa) at [f07d3cd](https://huggingface.co/datasets/sentence-transformers/hotpotqa/tree/f07d3cd2d290ea2e83ed35e33d67d6a4658b8786)
* Size: 76,064 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                             | positive                                                                             | negative                                                                            |
  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                             | string                                                                               | string                                                                              |
  | details | <ul><li>min: 7 tokens</li><li>mean: 25.02 tokens</li><li>max: 103 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 100.08 tokens</li><li>max: 315 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 89.42 tokens</li><li>max: 375 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                                                                       | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>What type of songs is the singer of Saahore Baahubali best known for?</code>                                                                                                           | <code>Saahore Baahubali "Saahore Baahubali" (English: Glory be to Baahubali) is a Telugu song from the 2017 film . Sung by Daler Mehndi, the song is composed by M. M. Keeravani, with lyrics penned by his father Siva Shakti Datta and Kodi Ramakrishna. Most of the lyrics were composed in Sanskrit.</code>                                                                                                                                                                                                                                                                                                            | <code>Anupama Deshpande Anupama Deshapande is a Bollywood playback singer who has won the Filmfare Award for Best Female Playback Singer for her folk song "Sohni Chinab Di" in the film "Sohni Mahiwal" (1984). This song was originally meant for Asha Bhonsle who since was busy those days. Therefore, Annu Malik recorded this song in the voice of Anupama Deshpande so that it could later on dubbed by Asha Bhonsle. But on listening the song, Asha Bhonsle sportingly advised to retain the song as it was, in the voice of Anupama Deshpande by giving full credit to the anupama's singing talent. She has sung a total of 124 songs in 92 films.</code> |
  | <code>'Dot TV' was owned and operated by a Pan-European satellite broadcasting, on-demand internet streaming media, broadband and telephone services company with headquarters where?</code> | <code>.tv (TV channel) .tv (Pronounced as 'Dot TV', referred to onscreen as .tv - the technology channel) was a British television channel dedicated to technology. .tv was owned and operated by British Sky Broadcasting. The channel began broadcasting on 1 September 1996 as "The Computer Channel" and broadcast between 18:00 and 20:00. The broadcasting hours were increased to midday-midnight when "The Computer Channel" (later .tv) started broadcasting on British Sky Broadcasting's digital satellite platform, Sky Digital in 1998. In 1999 the channel interviewed then Microsoft CEO Bill Gates.</code> | <code>Movistar TV Movistar TV is an IPTV service operated by Telefónica. The service was started as a commercial test pilot in the city of Alicante in 2001 and later extended to some major cities such as Madrid and Barcelona in April 2004. In 2013, Movistar Imagenio was rebranded to Movistar TV.</code>                                                                                                                                                                                                                                                                                                                                                      |
  | <code>Elvira Madigan's father was born in what year?</code>                                                                                                                                  | <code>Gisela Brož Gisela Antonia Brož (Brosch) (also sometimes referred to as Gisela Madigan), (4 April 1865 - 1945) was an Austrian-American circus performer, tight rope dancer, and clown. Her parents were shoemaker Joseph Brož and his wife Maria. She went to convent school in Siebenbürgen and at the age of 15 she got to know the circus family Madigans with John and Laura who at that time toured with circus Krembser in Vienna. Gisela became their foster child and got to learn tight rope dancing, this along with the couple's two year younger daughter Elvira Madigan.</code>                        | <code>Elvira Casazza Elvira Casazza (15 November 1887 – 24 January 1965) was an Italian mezzo-soprano opera singer (also known as Elvira Mari-Casazza). One of Toscanini's favourite singers, she was considered an outstanding interpreter of Mistress Quickly in Verdi's "Falstaff" during the 1920s and created several roles in Italian operas of the early 20th century.</code>                                                                                                                                                                                                                                                                                 |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "TripletLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Evaluation Dataset

#### sentence-transformers/hotpotqa

* Dataset: [sentence-transformers/hotpotqa](https://huggingface.co/datasets/sentence-transformers/hotpotqa) at [f07d3cd](https://huggingface.co/datasets/sentence-transformers/hotpotqa/tree/f07d3cd2d290ea2e83ed35e33d67d6a4658b8786)
* Size: 8,452 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
  |         | anchor                                                                              | positive                                                                            | negative                                                                            |
  |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                              | string                                                                              | string                                                                              |
  | details | <ul><li>min: 10 tokens</li><li>mean: 25.14 tokens</li><li>max: 130 tokens</li></ul> | <ul><li>min: 20 tokens</li><li>mean: 102.4 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 13 tokens</li><li>mean: 88.09 tokens</li><li>max: 358 tokens</li></ul> |
* Samples:
  | anchor                                                                                                                                            | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               |
  |:--------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>When was the English former professional footballer which Tslil Sela has an alledged relationship with born?</code>                         | <code>Tslil Sela Tslil Sela (Hebrew: צליל סלע‎ ‎ , born 26 October 1987) is an Israeli model, most known for her modeling work and for her alleged relationship with English footballer Rio Ferdinand. Sela is leading the campaign for KOOI fashion 2010, and Sanyang Motorcycles (SYM Motors) in Israel.</code>                                                                                                                                                                                                                                                | <code>Sam Collins (English footballer) Samuel Jason Collins (born 5 June 1977) is an English football manager and former footballer who played as a defender. His brother, Simon, is also a former professional footballer and manager.</code>                                                                                                                                                                                                                                                                                                                                                                                                                         |
  | <code>Gebhard Leberecht von Blucher the Prussian Generalfieldmarschall led his army against this famous commander in the Battle of Lepzig?</code> | <code>Gebhard Leberecht von Blücher Gebhard Leberecht von Blücher, Fürst von Wahlstatt (] ; 16 December 1742 – 12 September 1819), "Graf" (count), later elevated to "Fürst" (sovereign prince) von Wahlstatt, was a Prussian "Generalfeldmarschall" (field marshal). He earned his greatest recognition after leading his army against Napoleon I at the Battle of the Nations at Leipzig in 1813 and the Battle of Waterloo in 1815.</code>                                                                                                                    | <code>Karl Freiherr von Müffling Friedrich Karl Ferdinand Freiherr von Müffling, called Weiss (12 June 177510 January 1851) was a Prussian "Generalfeldmarschall" and military theorist. He served as Blücher's liaison officer in Wellington's headquarters during the Battle of Waterloo and was one of the organizers of the final victory over Napoleon. After the wars he served a diplomatic role at the Congress of Aix-la-Chappelle and was a major contributor to the development of the Prussian General Staff as Chief. Müffling also specialized in military topography and cartography.</code>                                                            |
  | <code>The Platonia Dilemma was introduced in the book "Metamagical Themas" which was written by an author born in what year?</code>               | <code>Platonia dilemma In the platonia dilemma introduced in Douglas Hofstadter's book "Metamagical Themas", an eccentric trillionaire gathers 20 people together, and tells them that if one and only one of them sends him a telegram (reverse charges) by noon the next day, that person will receive a billion dollars. If he receives more than one telegram, or none at all, no one will get any money, and cooperation between players is forbidden. In this situation, the superrational thing to do is to send a telegram with probability 1/20.</code> | <code>John Alexander Stewart (philosopher) John Alexander Stewart (19 October 1846 – 27 December 1933) was a Scottish writer, educator and philosopher. He was a university professor and classical lecturer at Christ Church, Oxford from 1875 to 1883, White's Professor of Moral Philosophy at Oxford, and professorial fellow of Corpus Christi College, from 1897 to his retirement in 1927. Throughout his academic career, he was an editor and author of works on Aristotle and considered one of the foremost experts on the subject. His best known books were "Notes on the Nicomachean Ethics of Aristotle" (1892) and "The Myths of Plato" (1905).</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "TripletLoss",
      "matryoshka_dims": [
          384,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

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

- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 20
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `resume_from_checkpoint`: bge-small-hotpotwa-matryoshka
- `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`: 16
- `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`: 20
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `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`: True
- `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}
- `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`: bge-small-hotpotwa-matryoshka
- `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    | dim_128_cosine_accuracy | dim_256_cosine_accuracy | dim_384_cosine_accuracy | dim_64_cosine_accuracy |
|:------:|:----:|:-------------:|:-------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|
| 0.3366 | 50   | 19.5492       | 19.2604 | 0.9585                  | 0.9657                  | 0.9663                  | 0.9432                 |
| 0.6731 | 100  | 19.1976       | 18.2958 | 0.9359                  | 0.9392                  | 0.9425                  | 0.9276                 |
| 1.0097 | 150  | 18.4746       | 16.9846 | 0.9053                  | 0.9075                  | 0.9085                  | 0.8996                 |
| 1.3462 | 200  | 18.0684       | 16.6869 | 0.9030                  | 0.9051                  | 0.9049                  | 0.8959                 |
| 1.6828 | 250  | 17.8979       | 16.5780 | 0.9017                  | 0.9030                  | 0.9016                  | 0.8954                 |
| 2.0194 | 300  | 17.7545       | 16.5135 | 0.8977                  | 0.8991                  | 0.8984                  | 0.8925                 |
| 2.3559 | 350  | 17.6046       | 16.4917 | 0.8894                  | 0.8894                  | 0.8907                  | 0.8862                 |
| 2.6925 | 400  | 17.4434       | 16.4926 | 0.8874                  | 0.8862                  | 0.8875                  | 0.8858                 |
| 3.0290 | 450  | 17.3278       | 16.4757 | 0.8854                  | 0.8861                  | 0.8869                  | 0.8859                 |
| 3.3656 | 500  | 17.247        | 16.4735 | 0.8830                  | 0.8841                  | 0.8854                  | 0.8816                 |


### Framework Versions
- Python: 3.10.10
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.33.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1

## Citation

### BibTeX

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

#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning}, 
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}
```

#### TripletLoss
```bibtex
@misc{hermans2017defense,
    title={In Defense of the Triplet Loss for Person Re-Identification}, 
    author={Alexander Hermans and Lucas Beyer and Bastian Leibe},
    year={2017},
    eprint={1703.07737},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}
```

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