adds model

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

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+hello

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/1_Pooling/config.json

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+{
+  "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
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/README.md

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+---
+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: When was the brewery founded by the family who resided in the Lemp
+    Mansion founded?
+  sentences:
+  - Latrobe Brewing Company Latrobe Brewing Company in Latrobe, Pennsylvania, founded
+    in 1839, was one of the largest breweries in the United States and the maker of
+    "Rolling Rock" beer (famous for its small green bottles). It was purchased by
+    Labatt Brewing Company in 1987, which in turn was purchased in 1995 by the Belgian
+    brewing conglomerate corporation Interbrew, which merged later into InBev in 2004.
+  - Lemp Mansion The Lemp Mansion (3322 DeMenil Place, St. Louis, Missouri) is a historical
+    house in Benton Park, St. Louis, Missouri. It is also the site of four suicides
+    by Lemp family members after the death of the son Frederick Lemp, whose William
+    J. Lemp Brewing Co. dominated the St. Louis beer market before Prohibition with
+    its Falstaff beer brand. The mansion is said to be haunted by members of the Lemp
+    family.
+  - 'Man of the House (song) "Man of the House" is a song co-written and recorded
+    by American country music artist Chuck Wicks. It was released in January 2009
+    as the third single from the album "Starting Now". The song reached #27 on the
+    "Billboard" Hot Country Songs chart. The song was written by Wicks and Michael
+    Mobley.'
+- source_sentence: Who is the translator and researcher who served as a dean at Hartford
+    Junior College and translated her Belgian-French partner's 1968 work into English?
+  sentences:
+  - William L. Shirer William Lawrence Shirer (February 23, 1904 – December 28, 1993)
+    was an American journalist and war correspondent. He wrote "The Rise and Fall
+    of the Third Reich", a history of Nazi Germany that has been read by many and
+    cited in scholarly works for more than 50 years. Originally a foreign correspondent
+    for the "Chicago Tribune" and the International News Service, Shirer was the first
+    reporter hired by Edward R. Murrow for what would become a CBS radio team of journalists
+    known as "Murrow's Boys". He became known for his broadcasts from Berlin, from
+    the rise of the Nazi dictatorship through the first year of World War II (1940).
+    With Murrow, he organized the first broadcast world news roundup, a format still
+    followed by news broadcasts.
+  - 'The Abyss (Yourcenar novel) The Abyss (French: L''Œuvre au noir ) is a 1968 novel
+    by the Belgian-French writer Marguerite Yourcenar. Its narrative centers on the
+    life and death of Zeno, a physician, philosopher, scientist and alchemist born
+    in Bruges during the Renaissance era. The book was published in France in 1968
+    and was met with immediate popular interest as well as critical acclaim, obtaining
+    the Prix Femina with unanimous votes the year of its publication. The English
+    translation by Grace Frick has been published under the title "The Abyss" or alternatively
+    Zeno of Bruges. Belgian filmmaker André Delvaux adapted it into a film in 1988.'
+  - Pierre L. van den Berghe Pierre L. van den Berghe (born 1933) is professor emeritus
+    of sociology and anthropology at the University of Washington, where he has worked
+    since 1965. Born in the Belgian Congo to Belgian parents, and spending World War
+    II in occupied Belgium, he was an early witness to ethnic conflict and racism,
+    which eventually led him to become a leading authority on ethnic relations. He
+    has conducted field work in South Africa, Mexico, Guatemala, Iran, Lebanon, Nigeria,
+    Peru, and Israel. Early in his career, he lectured at the University of Natal
+    alongside Leo Kuper and Fatima Meer. A student of Talcott Parsons at Harvard (receiving
+    the Ph.D. in 1960), he nevertheless had little interest in structural functionalism
+    and was one of the first proponents of sociobiological approaches to social phenomena.
+- source_sentence: Them Crooked Vultures and The Vines are both part of what music
+    genres?
+  sentences:
+  - Friendly Center Friendly Center is a large, open-air shopping center located in
+    northwestern Greensboro, North Carolina, near the intersection of Wendover Avenue
+    and Friendly Avenue. The shopping center opened in August 1957, and with its inward
+    orientation, Friendly Center could be classified as an outdoor lifestyle center.
+    Its anchor tenants include Belk, Macy's, and Sears. Other tenants include Barnes
+    & Noble, Old Navy, The Grande Theatre is a 16-screen multiplex cinema operated
+    by Regal Cinemas. It also contains Harris Teeter's flagship supermarket location
+    encompassing 72,000 square feet (6,700 m2) and Whole Foods Market. There are specialty
+    "foodie" stores tucked away in the back corner by Harris Teeter such as the Savory
+    Spice Shop and Midtown Olive Oil. It features a number of national retailers such
+    as Banana Republic, Victoria's Secret, The Limited, Bath & Body Works, Express,
+    The GAP, Eddie Bauer, Talbots, Birkenstock Feet First, Pier 1, and New York &
+    Company.
+  - Twin Wild Twin Wild is a British four-piece alternative rock band. Formed in 2012,
+    the band is made up of the collective creative energies of Richard Hutchison (vocals,
+    guitars), Imran Mair (drums), David Cuzner (guitars) and Edward Thomas (bass).
+    In 2014, the band self-released their track "Fears", which garnered over half
+    a million plays on Soundcloud and charted in Spotify’s Top 20 viral chart. Their
+    style of music has been compared to the likes of Foals, The Neighbourhood, and
+    Bastille. The band have been hailed by Edith Bowman as "The love child of Bastille
+    and Biffy Clyro".
+  - Them Crooked Vultures Them Crooked Vultures is a rock supergroup formed in Los
+    Angeles in 2009 by John Paul Jones (former member of Led Zeppelin) on bass and
+    keyboards, Dave Grohl (of Foo Fighters and formerly of Nirvana) on drums and backing
+    vocals, and Josh Homme (of Queens of the Stone Age, Eagles of Death Metal and
+    formerly of Kyuss) on guitar and vocals. The group also includes guitarist Alain
+    Johannes during live performances. The band began recording in February 2009,
+    and performed their first gig on August 9, 2009, in Chicago, followed by a European
+    debut on August 19. On October 1 the group embarked on a worldwide tour titled
+    "Deserve the Future" with dates going into 2010. The band's first single "New
+    Fang" was released in October 2009, followed by the group's self-titled debut
+    album the following month, debuting at number 12 on the "Billboard" 200. The group
+    won the 2011 Grammy Award for Best Hard Rock Performance for "New Fang".
+- source_sentence: What type of collection does Nådens år and Agnetha Fältskog have
+    in common?
+  sentences:
+  - Gardiner Island (Nunavut) Gardiner Island is one of the many uninhabited Canadian
+    arctic islands in Qikiqtaaluk Region, Nunavut. It is a Baffin Island offshore
+    island located in Frobisher Bay south of the capital city of Iqaluit.
+  - Nådens år Nådens år (The Year of Grace) is the third studio album by the Swedish
+    rock artist Ulf Lundell. It was released in April 1978 on EMI and Parlophone.
+    It was recorded in EMI Studio, Stockholm, and produced by Kjell Andersson and
+    Lundell. It includes "Snön faller och vi med den" ("The snow is falling and we
+    are too"), one of Lundell's more famous songs. Agnetha Fältskog is involved in
+    the song. The cover picture shows Lundell sitting on a rock next to a dog and
+    was taken in Åre in 1977. "Nådens år" achieved Gold status in Sweden.
+  - Åsa Elzén Åsa Elzén is an artist whose work is informed by feminist theory, intersectionality
+    and post-colonialism. Elzén was born in Sweden in 1972 and currently lives and
+    works in Berlin.
+- source_sentence: Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the
+    biography of a 19th century actor born in which year ?
+  sentences:
+  - Herbert Campbell Herbert Campbell (22 December 1844 – 19 July 1904) born Herbert
+    Edward Story was an English comedian and actor who appeared in music hall, Victorian
+    burlesques and musical comedies during the Victorian era. He was famous for starring,
+    for forty years, in the Theatre Royal, Drury Lane's annual Christmas pantomimes,
+    predominantly as a dame.
+  - Leptinella Leptinella is a genus of alpine flowering plant in the Asteraceae family,
+    comprising 33 species, distributed in New Guinea, Australia, New Zealand, South
+    Africa, and South America. Many of the species are endemic to New Zealand.
+  - Red Velvet (play) Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with
+    the biography of the 19th century actor Ira Aldridge and his taking the role of
+    "Othello". It premiered at the Tricycle Theatre (directed by its new artistic
+    director Indhu Rubasingham) from 11 October to 24 November 2012, with Aldridge
+    played by Adrian Lester.
+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: 0.8761239943208708
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.1238760056791292
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8770705158542357
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8761239943208708
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8770705158542357
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8761239943208708
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.1250591575958353
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8797917652626597
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8761239943208708
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8797917652626597
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8724562233790819
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.13073828679602462
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8783719829626124
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8728111689540937
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8783719829626124
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8710364410790346
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.143989588263133
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8764789398958827
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.867841930903928
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8764789398958827
+      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 = [
+    'Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of a 19th century actor born in which year ?',
+    'Red Velvet (play) Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of the 19th century actor Ira Aldridge and his taking the role of "Othello". It premiered at the Tricycle Theatre (directed by its new artistic director Indhu Rubasingham) from 11 October to 24 November 2012, with Aldridge played by Adrian Lester.',
+    "Herbert Campbell Herbert Campbell (22 December 1844 – 19 July 1904) born Herbert Edward Story was an English comedian and actor who appeared in music hall, Victorian burlesques and musical comedies during the Victorian era. He was famous for starring, for forty years, in the Theatre Royal, Drury Lane's annual Christmas pantomimes, predominantly as a dame.",
+]
+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]
+```
+
+<!--
+### 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
+
+#### 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** | **0.8761** |
+| dot_accuracy        | 0.1239     |
+| manhattan_accuracy  | 0.8771     |
+| euclidean_accuracy  | 0.8761     |
+| max_accuracy        | 0.8771     |
+
+#### 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** | **0.8761** |
+| dot_accuracy        | 0.1251     |
+| manhattan_accuracy  | 0.8798     |
+| euclidean_accuracy  | 0.8761     |
+| max_accuracy        | 0.8798     |
+
+#### 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** | **0.8725** |
+| dot_accuracy        | 0.1307     |
+| manhattan_accuracy  | 0.8784     |
+| euclidean_accuracy  | 0.8728     |
+| max_accuracy        | 0.8784     |
+
+#### 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** | **0.871** |
+| dot_accuracy        | 0.144     |
+| manhattan_accuracy  | 0.8765    |
+| euclidean_accuracy  | 0.8678    |
+| max_accuracy        | 0.8765    |
+
+<!--
+## 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
+
+#### 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: 8 tokens</li><li>mean: 24.08 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 100.12 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 88.02 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                           | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What nationality was the player named MVP in 2017 World Baseball Classic – Pool C ?</code> | <code>2017 World Baseball Classic – Pool C Pool C of the First Round of the 2017 World Baseball Classic was held at Marlins Park, Miami, Florida, United States, from March 9 to 12, 2017, between Canada, Colombia, the Dominican Republic, and the United States. Pool C was a round-robin tournament. Each team played the other three teams once, with the top two teams – the Dominican Republic and the United States – advancing to Pool F, one of two second-round pools. Manny Machado of the Dominican Republic was named MVP for the first-round Pool C bracket of the WBC, after batting .357.</code> | <code>2017 World Baseball Classic – Qualifier 2 Qualifier 2 of the Qualifying Round of the 2017 World Baseball Classic was held at Estadio B'Air, Mexicali, Mexico from March 17 to 20, 2016.</code>                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | <code>Karl Kraepelin specialized in the study of what predatory arachnids?</code>                | <code>Karl Kraepelin Karl Matthias Friedrich Magnus Kraepelin (14 December 1848 Neustrelitz – 28 June 1915 Hamburg), was a German naturalist who specialised in the study of scorpions, centipedes, spiders and solfugids, and was noted for his monograph ""Scorpiones und Pedipalpi"" (Berlin) in 1899, which was an exhaustive survey of the taxonomy of the Order Scorpiones. From 1889–1914 he was Director of the "Naturhistorisches Museum Hamburg ", which was destroyed during World War II, and worked on myriapods from 1901–1916.</code>                                                              | <code>Teuthology Teuthology (from Greek "τεῦθος" , "cuttlefish, squid", and -λογία , "-logia") is the study of cephalopods.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>Who directed the 1990 American crime film in which Vito Pick me played a bit part?</code>  | <code>Vito Picone Vito Picone (born March 20, 1941) is the lead singer of The Elegants, and along with Jimmy Mochella is a remaining original member. He has also played bit parts in "Goodfellas", "Analyze This", and "The Sopranos".</code>                                                                                                                                                                                                                                                                                                                                                                    | <code>The Rookie (1990 film) The Rookie is a 1990 American buddy cop film directed by Clint Eastwood and produced by Howard G. Kazanjian, Steven Siebert and David Valdes. It was written from a screenplay conceived by Boaz Yakin and Scott Spiegel. The film stars Charlie Sheen, Clint Eastwood, Raúl Juliá, Sônia Braga, Lara Flynn Boyle, and Tom Skerritt. Eastwood plays a veteran police officer teamed up with a younger detective played by Sheen ("the rookie"), whose intent is to take down a German crime lord in downtown Los Angeles following months of investigation into an exotic car theft ring.</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: 24.53 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 103.87 tokens</li><li>max: 407 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 88.32 tokens</li><li>max: 356 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Which actress, known for her role as Harper Munroe on the MTV comedy series "Happyland", starred alongside Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite in the film A Sort of Homecoming?</code> | <code>A Sort of Homecoming (film) A Sort of Homecoming is an American drama directed by Maria Burton, her fifth feature film. The films stars Katherine McNamara, Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite. The film premiered March 14, 2015 at the Omaha Film Festival.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>Nellie Bellflower Nellie Bellflower (born May 1, 1946 in Phoenix, Arizona) is an American actress and voice artist who provided the voice of Princess Ariel in the Ruby-Spears animated television series "Thundarr the Barbarian". She has also been in "The Last Unicorn" (voice), Rankin/Bass "The Return of the King", "Americathon", the miniseries "East of Eden", and guest roles on various TV shows such as "Barnaby Jones", "Barney Miller", "Starsky and Hutch", and "Happy Days" as Fonzie's ex-fiancée Maureen Johnson, a.k.a. "The Lone Stripper", in the Season 2 episode of the series titled "Fonzie's Getting Married" (episode #13). Nellie has been involved in movie production with three projects: "The Girl in Melanie Klein" (2008), "Miss Pettigrew Lives for a Day" (2008) and "Finding Neverland" (2004), for which she was nominated for an Academy Award as Producer for Best Picture. She is married to Michael Mislove.</code> |
+  | <code>Between Pizza Fusion and Pizzeria Venti, which restaurant emphasizes organic ingredients and green building methods?</code>                                                                                          | <code>Pizza Fusion Pizza Fusion is a Deerfield Beach, Florida-based pizza restaurant chain. Using mostly organic ingredients and emphasizing green building methods, the restaurants operate under the tagline Saving the Earth, One Pizza at a Time.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | <code>Pizza Schmizza Pizza Schmizza is an American pizza chain with 23 locations throughout the Portland, Oregon area, and two in southern Oregon. Pizza Schmizza, primarily selling thin crust pizza by-the-slice.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  | <code>What company distributed the stop motion spin-off special "The Year Without a Santa Claus," which aired on December 10, 1974?</code>                                                                                 | <code>A Miser Brothers' Christmas A Miser Brothers' Christmas is a stop motion spin-off special based on some of the characters from the 1974 Rankin-Bass special "The Year Without a Santa Claus". Distributed by Warner Bros. Animation under their Warner Premiere label (the rights holders of the post-1974 Rankin-Bass library) and Toronto-based Cuppa Coffee Studios, the one-hour special premiered on ABC Family on Saturday, December 13, 2008, during the network's annual The 25 Days of Christmas programming. Mickey Rooney and George S. Irving reprised their respective roles as Santa Claus and Heat Miser at ages 88 and 86. Snow Miser, originally portrayed by Dick Shawn who died in 1987, was voiced by Juan Chioran, while Mrs. Claus, voiced by Shirley Booth in the original, was portrayed by Catherine Disher (because Booth had died in 1992). The movie aimed to emulate the Rankin/Bass animation style. This is the last Christmas special to feature Mickey Rooney as Santa Claus, as he died in 2014, as well as the last time George Irving voiced Heat Miser, as he died in 2016.</code> | <code>Holidaze: The Christmas That Almost Didn't Happen Holidaze: The Christmas That Almost Didn't Happen is an American stop-motion animated Christmas television special directed by David H. Brooks, that originally aired in 2006 and produced by BixPix Entertainment, Once Upon a Frog and Madison Street Entertainment. The show's plot has Rusty Reindeer (Fred Savage) the brother of Rudolph the Red Nosed Reindeer joining a support group for depressed holiday icons, and he and the other characters search for the meaning of Christmas and help a young boy (Dylan and Cole Sprouse) to get on Santa's nice list. Rusty's cohorts include Candie, the Easter Bunny (Gladys Knight); Mr. C, the grouchy cherub (Paul Rodriguez); Albert, the Thanksgiving Turkey (Harland Williams); And Trick and Treat (Brenda Song and Emily Osment) the teenage Halloween Ghosts.</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`: 50
+- `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`: 50
+- `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   | 19.5758       | 19.3933 | 0.9552                  | 0.9663                  | 0.9668                  | 0.9359                 |
+| 0.6731  | 100  | 19.4573       | 19.0971 | 0.9571                  | 0.9646                  | 0.9653                  | 0.9450                 |
+| 1.0097  | 150  | 19.1409       | 18.4070 | 0.9385                  | 0.9434                  | 0.9473                  | 0.9307                 |
+| 1.3462  | 200  | 18.6431       | 17.3292 | 0.9126                  | 0.9164                  | 0.9184                  | 0.9094                 |
+| 1.6828  | 250  | 18.2288       | 16.8751 | 0.9063                  | 0.9071                  | 0.9100                  | 0.9023                 |
+| 2.0194  | 300  | 18.0425       | 16.6981 | 0.9020                  | 0.9032                  | 0.9045                  | 0.8990                 |
+| 2.3559  | 350  | 17.9458       | 16.6155 | 0.9037                  | 0.9013                  | 0.9022                  | 0.8984                 |
+| 2.6925  | 400  | 17.8525       | 16.5536 | 0.8978                  | 0.8971                  | 0.8974                  | 0.8948                 |
+| 3.0290  | 450  | 17.7529       | 16.5136 | 0.8980                  | 0.8956                  | 0.8953                  | 0.8951                 |
+| 3.3656  | 500  | 17.6709       | 16.4824 | 0.8932                  | 0.8914                  | 0.8928                  | 0.8907                 |
+| 3.7021  | 550  | 17.5348       | 16.4632 | 0.8863                  | 0.8858                  | 0.8859                  | 0.8849                 |
+| 4.0387  | 600  | 17.4198       | 16.4601 | 0.8852                  | 0.8862                  | 0.8859                  | 0.8839                 |
+| 4.3753  | 650  | 17.3673       | 16.4405 | 0.8854                  | 0.8864                  | 0.8865                  | 0.8842                 |
+| 4.7118  | 700  | 17.2603       | 16.4356 | 0.8835                  | 0.8838                  | 0.8838                  | 0.8807                 |
+| 5.0484  | 750  | 17.1807       | 16.4443 | 0.8850                  | 0.8864                  | 0.8859                  | 0.8838                 |
+| 5.3849  | 800  | 17.1629       | 16.4202 | 0.8848                  | 0.8862                  | 0.8867                  | 0.8842                 |
+| 5.7215  | 850  | 17.0747       | 16.4162 | 0.8854                  | 0.8875                  | 0.8869                  | 0.8837                 |
+| 6.0581  | 900  | 17.0161       | 16.4192 | 0.8852                  | 0.8863                  | 0.8856                  | 0.8856                 |
+| 6.3946  | 950  | 17.0146       | 16.4033 | 0.8849                  | 0.8854                  | 0.8856                  | 0.8844                 |
+| 6.7312  | 1000 | 16.9393       | 16.4053 | 0.8829                  | 0.8839                  | 0.8848                  | 0.8835                 |
+| 7.0677  | 1050 | 16.899        | 16.4162 | 0.8826                  | 0.8829                  | 0.8833                  | 0.8818                 |
+| 7.4043  | 1100 | 16.9112       | 16.4051 | 0.8829                  | 0.8835                  | 0.8833                  | 0.8820                 |
+| 7.7408  | 1150 | 16.8508       | 16.4044 | 0.8822                  | 0.8825                  | 0.8830                  | 0.8820                 |
+| 8.0774  | 1200 | 16.8104       | 16.4063 | 0.8816                  | 0.8816                  | 0.8814                  | 0.8817                 |
+| 8.4140  | 1250 | 16.8212       | 16.4040 | 0.8835                  | 0.8822                  | 0.8822                  | 0.8820                 |
+| 8.7505  | 1300 | 16.7743       | 16.3934 | 0.8822                  | 0.8824                  | 0.8817                  | 0.8810                 |
+| 9.0871  | 1350 | 16.7383       | 16.3963 | 0.8810                  | 0.8820                  | 0.8807                  | 0.8800                 |
+| 9.4236  | 1400 | 16.743        | 16.4067 | 0.8819                  | 0.8822                  | 0.8819                  | 0.8798                 |
+| 9.7602  | 1450 | 16.7047       | 16.3959 | 0.8804                  | 0.8810                  | 0.8810                  | 0.8797                 |
+| 10.0968 | 1500 | 16.6782       | 16.3986 | 0.8788                  | 0.8791                  | 0.8796                  | 0.8784                 |
+| 10.4333 | 1550 | 16.6708       | 16.4016 | 0.8794                  | 0.8792                  | 0.8797                  | 0.8791                 |
+| 10.7699 | 1600 | 16.6485       | 16.3963 | 0.8790                  | 0.8801                  | 0.8791                  | 0.8781                 |
+| 11.1064 | 1650 | 16.6205       | 16.4012 | 0.8779                  | 0.8787                  | 0.8793                  | 0.8771                 |
+| 11.4430 | 1700 | 16.6095       | 16.4131 | 0.8786                  | 0.8790                  | 0.8794                  | 0.8791                 |
+| 11.7796 | 1750 | 16.5891       | 16.4070 | 0.8807                  | 0.8805                  | 0.8810                  | 0.8801                 |
+| 12.1161 | 1800 | 16.5619       | 16.3963 | 0.8794                  | 0.8800                  | 0.8797                  | 0.8780                 |
+| 12.4527 | 1850 | 16.5467       | 16.3991 | 0.8796                  | 0.8806                  | 0.8804                  | 0.8790                 |
+| 12.7892 | 1900 | 16.5398       | 16.3970 | 0.8792                  | 0.8798                  | 0.8801                  | 0.8788                 |
+| 13.1258 | 1950 | 16.5047       | 16.3964 | 0.8796                  | 0.8804                  | 0.8804                  | 0.8788                 |
+| 13.4623 | 2000 | 16.4985       | 16.4025 | 0.8793                  | 0.8798                  | 0.8807                  | 0.8790                 |
+| 13.7989 | 2050 | 16.4852       | 16.4107 | 0.8801                  | 0.8810                  | 0.8800                  | 0.8793                 |
+| 14.1355 | 2100 | 16.4526       | 16.3929 | 0.8797                  | 0.8801                  | 0.8809                  | 0.8779                 |
+| 14.4720 | 2150 | 16.4343       | 16.4075 | 0.8788                  | 0.8791                  | 0.8797                  | 0.8774                 |
+| 14.8086 | 2200 | 16.4244       | 16.4027 | 0.8804                  | 0.8819                  | 0.8820                  | 0.8809                 |
+| 15.1451 | 2250 | 16.3947       | 16.4102 | 0.8791                  | 0.8792                  | 0.8803                  | 0.8773                 |
+| 15.4817 | 2300 | 16.3827       | 16.4042 | 0.8804                  | 0.8813                  | 0.8813                  | 0.8781                 |
+| 15.8183 | 2350 | 16.3719       | 16.4003 | 0.8801                  | 0.8818                  | 0.8820                  | 0.8791                 |
+| 16.1548 | 2400 | 16.3403       | 16.4132 | 0.8781                  | 0.8791                  | 0.8799                  | 0.8767                 |
+| 16.4914 | 2450 | 16.3357       | 16.4149 | 0.8804                  | 0.8809                  | 0.8807                  | 0.8792                 |
+| 16.8279 | 2500 | 16.3203       | 16.4081 | 0.8804                  | 0.8814                  | 0.8816                  | 0.8791                 |
+| 17.1645 | 2550 | 16.2986       | 16.4139 | 0.8798                  | 0.8800                  | 0.8820                  | 0.8791                 |
+| 17.5011 | 2600 | 16.2923       | 16.4062 | 0.8786                  | 0.8792                  | 0.8799                  | 0.8768                 |
+| 17.8376 | 2650 | 16.2649       | 16.4106 | 0.8800                  | 0.8807                  | 0.8814                  | 0.8787                 |
+| 18.1742 | 2700 | 16.2505       | 16.4188 | 0.8786                  | 0.8793                  | 0.8803                  | 0.8771                 |
+| 18.5107 | 2750 | 16.226        | 16.4149 | 0.8771                  | 0.8781                  | 0.8780                  | 0.8766                 |
+| 18.8473 | 2800 | 16.2106       | 16.4230 | 0.8780                  | 0.8799                  | 0.8791                  | 0.8767                 |
+| 19.1838 | 2850 | 16.2052       | 16.4351 | 0.8770                  | 0.8777                  | 0.8785                  | 0.8745                 |
+| 19.5204 | 2900 | 16.186        | 16.4331 | 0.8777                  | 0.8793                  | 0.8792                  | 0.8762                 |
+| 19.8570 | 2950 | 16.1496       | 16.4377 | 0.8774                  | 0.8781                  | 0.8780                  | 0.8771                 |
+| 20.1935 | 3000 | 16.151        | 16.4407 | 0.8766                  | 0.8780                  | 0.8780                  | 0.8751                 |
+| 20.5301 | 3050 | 16.1081       | 16.4426 | 0.8759                  | 0.8775                  | 0.8774                  | 0.8749                 |
+| 20.8666 | 3100 | 16.0864       | 16.4412 | 0.8774                  | 0.8781                  | 0.8787                  | 0.8746                 |
+| 21.2032 | 3150 | 16.0934       | 16.4547 | 0.8768                  | 0.8783                  | 0.8794                  | 0.8746                 |
+| 21.5398 | 3200 | 16.0382       | 16.4589 | 0.8742                  | 0.8752                  | 0.8766                  | 0.8723                 |
+| 21.8763 | 3250 | 16.0279       | 16.4668 | 0.8752                  | 0.8766                  | 0.8773                  | 0.8728                 |
+| 22.2129 | 3300 | 16.0327       | 16.4737 | 0.8742                  | 0.8768                  | 0.8773                  | 0.8727                 |
+| 22.5494 | 3350 | 15.979        | 16.4686 | 0.8740                  | 0.8771                  | 0.8771                  | 0.8722                 |
+| 22.8860 | 3400 | 15.9622       | 16.4736 | 0.8743                  | 0.8760                  | 0.8765                  | 0.8721                 |
+| 23.2225 | 3450 | 15.9881       | 16.4802 | 0.8743                  | 0.8757                  | 0.8755                  | 0.8723                 |
+| 23.5591 | 3500 | 15.9482       | 16.4821 | 0.8725                  | 0.8761                  | 0.8761                  | 0.8710                 |
+
+
+### 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}
+}
+```
+
+<!--
+## 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.*
+-->

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/model.safetensors

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+version https://git-lfs.github.com/spec/v1
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+size 133462128

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/modules.json

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+[
+  {
+    "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"
+  }
+]

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/optimizer.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:28967fd32631134d32ce5bda35f836a5ffb5285f4e47518202b6950dbdac6ebd
+size 265864826

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-3500/sentence_bert_config.json

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+  "do_lower_case": true
+}

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/README.md

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+---
+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: When was the brewery founded by the family who resided in the Lemp
+    Mansion founded?
+  sentences:
+  - Latrobe Brewing Company Latrobe Brewing Company in Latrobe, Pennsylvania, founded
+    in 1839, was one of the largest breweries in the United States and the maker of
+    "Rolling Rock" beer (famous for its small green bottles). It was purchased by
+    Labatt Brewing Company in 1987, which in turn was purchased in 1995 by the Belgian
+    brewing conglomerate corporation Interbrew, which merged later into InBev in 2004.
+  - Lemp Mansion The Lemp Mansion (3322 DeMenil Place, St. Louis, Missouri) is a historical
+    house in Benton Park, St. Louis, Missouri. It is also the site of four suicides
+    by Lemp family members after the death of the son Frederick Lemp, whose William
+    J. Lemp Brewing Co. dominated the St. Louis beer market before Prohibition with
+    its Falstaff beer brand. The mansion is said to be haunted by members of the Lemp
+    family.
+  - 'Man of the House (song) "Man of the House" is a song co-written and recorded
+    by American country music artist Chuck Wicks. It was released in January 2009
+    as the third single from the album "Starting Now". The song reached #27 on the
+    "Billboard" Hot Country Songs chart. The song was written by Wicks and Michael
+    Mobley.'
+- source_sentence: Who is the translator and researcher who served as a dean at Hartford
+    Junior College and translated her Belgian-French partner's 1968 work into English?
+  sentences:
+  - William L. Shirer William Lawrence Shirer (February 23, 1904 – December 28, 1993)
+    was an American journalist and war correspondent. He wrote "The Rise and Fall
+    of the Third Reich", a history of Nazi Germany that has been read by many and
+    cited in scholarly works for more than 50 years. Originally a foreign correspondent
+    for the "Chicago Tribune" and the International News Service, Shirer was the first
+    reporter hired by Edward R. Murrow for what would become a CBS radio team of journalists
+    known as "Murrow's Boys". He became known for his broadcasts from Berlin, from
+    the rise of the Nazi dictatorship through the first year of World War II (1940).
+    With Murrow, he organized the first broadcast world news roundup, a format still
+    followed by news broadcasts.
+  - 'The Abyss (Yourcenar novel) The Abyss (French: L''Œuvre au noir ) is a 1968 novel
+    by the Belgian-French writer Marguerite Yourcenar. Its narrative centers on the
+    life and death of Zeno, a physician, philosopher, scientist and alchemist born
+    in Bruges during the Renaissance era. The book was published in France in 1968
+    and was met with immediate popular interest as well as critical acclaim, obtaining
+    the Prix Femina with unanimous votes the year of its publication. The English
+    translation by Grace Frick has been published under the title "The Abyss" or alternatively
+    Zeno of Bruges. Belgian filmmaker André Delvaux adapted it into a film in 1988.'
+  - Pierre L. van den Berghe Pierre L. van den Berghe (born 1933) is professor emeritus
+    of sociology and anthropology at the University of Washington, where he has worked
+    since 1965. Born in the Belgian Congo to Belgian parents, and spending World War
+    II in occupied Belgium, he was an early witness to ethnic conflict and racism,
+    which eventually led him to become a leading authority on ethnic relations. He
+    has conducted field work in South Africa, Mexico, Guatemala, Iran, Lebanon, Nigeria,
+    Peru, and Israel. Early in his career, he lectured at the University of Natal
+    alongside Leo Kuper and Fatima Meer. A student of Talcott Parsons at Harvard (receiving
+    the Ph.D. in 1960), he nevertheless had little interest in structural functionalism
+    and was one of the first proponents of sociobiological approaches to social phenomena.
+- source_sentence: Them Crooked Vultures and The Vines are both part of what music
+    genres?
+  sentences:
+  - Friendly Center Friendly Center is a large, open-air shopping center located in
+    northwestern Greensboro, North Carolina, near the intersection of Wendover Avenue
+    and Friendly Avenue. The shopping center opened in August 1957, and with its inward
+    orientation, Friendly Center could be classified as an outdoor lifestyle center.
+    Its anchor tenants include Belk, Macy's, and Sears. Other tenants include Barnes
+    & Noble, Old Navy, The Grande Theatre is a 16-screen multiplex cinema operated
+    by Regal Cinemas. It also contains Harris Teeter's flagship supermarket location
+    encompassing 72,000 square feet (6,700 m2) and Whole Foods Market. There are specialty
+    "foodie" stores tucked away in the back corner by Harris Teeter such as the Savory
+    Spice Shop and Midtown Olive Oil. It features a number of national retailers such
+    as Banana Republic, Victoria's Secret, The Limited, Bath & Body Works, Express,
+    The GAP, Eddie Bauer, Talbots, Birkenstock Feet First, Pier 1, and New York &
+    Company.
+  - Twin Wild Twin Wild is a British four-piece alternative rock band. Formed in 2012,
+    the band is made up of the collective creative energies of Richard Hutchison (vocals,
+    guitars), Imran Mair (drums), David Cuzner (guitars) and Edward Thomas (bass).
+    In 2014, the band self-released their track "Fears", which garnered over half
+    a million plays on Soundcloud and charted in Spotify’s Top 20 viral chart. Their
+    style of music has been compared to the likes of Foals, The Neighbourhood, and
+    Bastille. The band have been hailed by Edith Bowman as "The love child of Bastille
+    and Biffy Clyro".
+  - Them Crooked Vultures Them Crooked Vultures is a rock supergroup formed in Los
+    Angeles in 2009 by John Paul Jones (former member of Led Zeppelin) on bass and
+    keyboards, Dave Grohl (of Foo Fighters and formerly of Nirvana) on drums and backing
+    vocals, and Josh Homme (of Queens of the Stone Age, Eagles of Death Metal and
+    formerly of Kyuss) on guitar and vocals. The group also includes guitarist Alain
+    Johannes during live performances. The band began recording in February 2009,
+    and performed their first gig on August 9, 2009, in Chicago, followed by a European
+    debut on August 19. On October 1 the group embarked on a worldwide tour titled
+    "Deserve the Future" with dates going into 2010. The band's first single "New
+    Fang" was released in October 2009, followed by the group's self-titled debut
+    album the following month, debuting at number 12 on the "Billboard" 200. The group
+    won the 2011 Grammy Award for Best Hard Rock Performance for "New Fang".
+- source_sentence: What type of collection does Nådens år and Agnetha Fältskog have
+    in common?
+  sentences:
+  - Gardiner Island (Nunavut) Gardiner Island is one of the many uninhabited Canadian
+    arctic islands in Qikiqtaaluk Region, Nunavut. It is a Baffin Island offshore
+    island located in Frobisher Bay south of the capital city of Iqaluit.
+  - Nådens år Nådens år (The Year of Grace) is the third studio album by the Swedish
+    rock artist Ulf Lundell. It was released in April 1978 on EMI and Parlophone.
+    It was recorded in EMI Studio, Stockholm, and produced by Kjell Andersson and
+    Lundell. It includes "Snön faller och vi med den" ("The snow is falling and we
+    are too"), one of Lundell's more famous songs. Agnetha Fältskog is involved in
+    the song. The cover picture shows Lundell sitting on a rock next to a dog and
+    was taken in Åre in 1977. "Nådens år" achieved Gold status in Sweden.
+  - Åsa Elzén Åsa Elzén is an artist whose work is informed by feminist theory, intersectionality
+    and post-colonialism. Elzén was born in Sweden in 1972 and currently lives and
+    works in Berlin.
+- source_sentence: Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the
+    biography of a 19th century actor born in which year ?
+  sentences:
+  - Herbert Campbell Herbert Campbell (22 December 1844 – 19 July 1904) born Herbert
+    Edward Story was an English comedian and actor who appeared in music hall, Victorian
+    burlesques and musical comedies during the Victorian era. He was famous for starring,
+    for forty years, in the Theatre Royal, Drury Lane's annual Christmas pantomimes,
+    predominantly as a dame.
+  - Leptinella Leptinella is a genus of alpine flowering plant in the Asteraceae family,
+    comprising 33 species, distributed in New Guinea, Australia, New Zealand, South
+    Africa, and South America. Many of the species are endemic to New Zealand.
+  - Red Velvet (play) Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with
+    the biography of the 19th century actor Ira Aldridge and his taking the role of
+    "Othello". It premiered at the Tricycle Theatre (directed by its new artistic
+    director Indhu Rubasingham) from 11 October to 24 November 2012, with Aldridge
+    played by Adrian Lester.
+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: 0.8698532891623284
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.13014671083767157
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8737576904874585
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8698532891623284
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8737576904874585
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy
+      value: 0.86819687647894
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.1319214387127307
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8762423095125415
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8680785612872692
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8762423095125415
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8663038334122102
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.1386654046379555
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8742309512541411
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.865712257453857
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8742309512541411
+      name: Max Accuracy
+  - task:
+      type: triplet
+      name: Triplet
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy
+      value: 0.8634642688121155
+      name: Cosine Accuracy
+    - type: dot_accuracy
+      value: 0.1508518693800284
+      name: Dot Accuracy
+    - type: manhattan_accuracy
+      value: 0.8728111689540937
+      name: Manhattan Accuracy
+    - type: euclidean_accuracy
+      value: 0.8637008991954567
+      name: Euclidean Accuracy
+    - type: max_accuracy
+      value: 0.8728111689540937
+      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 = [
+    'Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of a 19th century actor born in which year ?',
+    'Red Velvet (play) Red Velvet is a 2012 play by Lolita Chakrabarti, dealing with the biography of the 19th century actor Ira Aldridge and his taking the role of "Othello". It premiered at the Tricycle Theatre (directed by its new artistic director Indhu Rubasingham) from 11 October to 24 November 2012, with Aldridge played by Adrian Lester.',
+    "Herbert Campbell Herbert Campbell (22 December 1844 – 19 July 1904) born Herbert Edward Story was an English comedian and actor who appeared in music hall, Victorian burlesques and musical comedies during the Victorian era. He was famous for starring, for forty years, in the Theatre Royal, Drury Lane's annual Christmas pantomimes, predominantly as a dame.",
+]
+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]
+```
+
+<!--
+### 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
+
+#### 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** | **0.8699** |
+| dot_accuracy        | 0.1301     |
+| manhattan_accuracy  | 0.8738     |
+| euclidean_accuracy  | 0.8699     |
+| max_accuracy        | 0.8738     |
+
+#### 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** | **0.8682** |
+| dot_accuracy        | 0.1319     |
+| manhattan_accuracy  | 0.8762     |
+| euclidean_accuracy  | 0.8681     |
+| max_accuracy        | 0.8762     |
+
+#### 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** | **0.8663** |
+| dot_accuracy        | 0.1387     |
+| manhattan_accuracy  | 0.8742     |
+| euclidean_accuracy  | 0.8657     |
+| max_accuracy        | 0.8742     |
+
+#### 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** | **0.8635** |
+| dot_accuracy        | 0.1509     |
+| manhattan_accuracy  | 0.8728     |
+| euclidean_accuracy  | 0.8637     |
+| max_accuracy        | 0.8728     |
+
+<!--
+## 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
+
+#### 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: 8 tokens</li><li>mean: 24.08 tokens</li><li>max: 95 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 100.12 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 88.02 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                           | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      |
+  |:-------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>What nationality was the player named MVP in 2017 World Baseball Classic – Pool C ?</code> | <code>2017 World Baseball Classic – Pool C Pool C of the First Round of the 2017 World Baseball Classic was held at Marlins Park, Miami, Florida, United States, from March 9 to 12, 2017, between Canada, Colombia, the Dominican Republic, and the United States. Pool C was a round-robin tournament. Each team played the other three teams once, with the top two teams – the Dominican Republic and the United States – advancing to Pool F, one of two second-round pools. Manny Machado of the Dominican Republic was named MVP for the first-round Pool C bracket of the WBC, after batting .357.</code> | <code>2017 World Baseball Classic – Qualifier 2 Qualifier 2 of the Qualifying Round of the 2017 World Baseball Classic was held at Estadio B'Air, Mexicali, Mexico from March 17 to 20, 2016.</code>                                                                                                                                                                                                                                                                                                                                                                                                                          |
+  | <code>Karl Kraepelin specialized in the study of what predatory arachnids?</code>                | <code>Karl Kraepelin Karl Matthias Friedrich Magnus Kraepelin (14 December 1848 Neustrelitz – 28 June 1915 Hamburg), was a German naturalist who specialised in the study of scorpions, centipedes, spiders and solfugids, and was noted for his monograph ""Scorpiones und Pedipalpi"" (Berlin) in 1899, which was an exhaustive survey of the taxonomy of the Order Scorpiones. From 1889–1914 he was Director of the "Naturhistorisches Museum Hamburg ", which was destroyed during World War II, and worked on myriapods from 1901–1916.</code>                                                              | <code>Teuthology Teuthology (from Greek "τεῦθος" , "cuttlefish, squid", and -λογία , "-logia") is the study of cephalopods.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            |
+  | <code>Who directed the 1990 American crime film in which Vito Pick me played a bit part?</code>  | <code>Vito Picone Vito Picone (born March 20, 1941) is the lead singer of The Elegants, and along with Jimmy Mochella is a remaining original member. He has also played bit parts in "Goodfellas", "Analyze This", and "The Sopranos".</code>                                                                                                                                                                                                                                                                                                                                                                    | <code>The Rookie (1990 film) The Rookie is a 1990 American buddy cop film directed by Clint Eastwood and produced by Howard G. Kazanjian, Steven Siebert and David Valdes. It was written from a screenplay conceived by Boaz Yakin and Scott Spiegel. The film stars Charlie Sheen, Clint Eastwood, Raúl Juliá, Sônia Braga, Lara Flynn Boyle, and Tom Skerritt. Eastwood plays a veteran police officer teamed up with a younger detective played by Sheen ("the rookie"), whose intent is to take down a German crime lord in downtown Los Angeles following months of investigation into an exotic car theft ring.</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: 24.53 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 19 tokens</li><li>mean: 103.87 tokens</li><li>max: 407 tokens</li></ul> | <ul><li>min: 17 tokens</li><li>mean: 88.32 tokens</li><li>max: 356 tokens</li></ul> |
+* Samples:
+  | anchor                                                                                                                                                                                                                     | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | negative                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             |
+  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Which actress, known for her role as Harper Munroe on the MTV comedy series "Happyland", starred alongside Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite in the film A Sort of Homecoming?</code> | <code>A Sort of Homecoming (film) A Sort of Homecoming is an American drama directed by Maria Burton, her fifth feature film. The films stars Katherine McNamara, Laura Marano, Parker Mack, Michelle Clunie and Kathleen Wilhoite. The film premiered March 14, 2015 at the Omaha Film Festival.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      | <code>Nellie Bellflower Nellie Bellflower (born May 1, 1946 in Phoenix, Arizona) is an American actress and voice artist who provided the voice of Princess Ariel in the Ruby-Spears animated television series "Thundarr the Barbarian". She has also been in "The Last Unicorn" (voice), Rankin/Bass "The Return of the King", "Americathon", the miniseries "East of Eden", and guest roles on various TV shows such as "Barnaby Jones", "Barney Miller", "Starsky and Hutch", and "Happy Days" as Fonzie's ex-fiancée Maureen Johnson, a.k.a. "The Lone Stripper", in the Season 2 episode of the series titled "Fonzie's Getting Married" (episode #13). Nellie has been involved in movie production with three projects: "The Girl in Melanie Klein" (2008), "Miss Pettigrew Lives for a Day" (2008) and "Finding Neverland" (2004), for which she was nominated for an Academy Award as Producer for Best Picture. She is married to Michael Mislove.</code> |
+  | <code>Between Pizza Fusion and Pizzeria Venti, which restaurant emphasizes organic ingredients and green building methods?</code>                                                                                          | <code>Pizza Fusion Pizza Fusion is a Deerfield Beach, Florida-based pizza restaurant chain. Using mostly organic ingredients and emphasizing green building methods, the restaurants operate under the tagline Saving the Earth, One Pizza at a Time.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  | <code>Pizza Schmizza Pizza Schmizza is an American pizza chain with 23 locations throughout the Portland, Oregon area, and two in southern Oregon. Pizza Schmizza, primarily selling thin crust pizza by-the-slice.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           |
+  | <code>What company distributed the stop motion spin-off special "The Year Without a Santa Claus," which aired on December 10, 1974?</code>                                                                                 | <code>A Miser Brothers' Christmas A Miser Brothers' Christmas is a stop motion spin-off special based on some of the characters from the 1974 Rankin-Bass special "The Year Without a Santa Claus". Distributed by Warner Bros. Animation under their Warner Premiere label (the rights holders of the post-1974 Rankin-Bass library) and Toronto-based Cuppa Coffee Studios, the one-hour special premiered on ABC Family on Saturday, December 13, 2008, during the network's annual The 25 Days of Christmas programming. Mickey Rooney and George S. Irving reprised their respective roles as Santa Claus and Heat Miser at ages 88 and 86. Snow Miser, originally portrayed by Dick Shawn who died in 1987, was voiced by Juan Chioran, while Mrs. Claus, voiced by Shirley Booth in the original, was portrayed by Catherine Disher (because Booth had died in 1992). The movie aimed to emulate the Rankin/Bass animation style. This is the last Christmas special to feature Mickey Rooney as Santa Claus, as he died in 2014, as well as the last time George Irving voiced Heat Miser, as he died in 2016.</code> | <code>Holidaze: The Christmas That Almost Didn't Happen Holidaze: The Christmas That Almost Didn't Happen is an American stop-motion animated Christmas television special directed by David H. Brooks, that originally aired in 2006 and produced by BixPix Entertainment, Once Upon a Frog and Madison Street Entertainment. The show's plot has Rusty Reindeer (Fred Savage) the brother of Rudolph the Red Nosed Reindeer joining a support group for depressed holiday icons, and he and the other characters search for the meaning of Christmas and help a young boy (Dylan and Cole Sprouse) to get on Santa's nice list. Rusty's cohorts include Candie, the Easter Bunny (Gladys Knight); Mr. C, the grouchy cherub (Paul Rodriguez); Albert, the Thanksgiving Turkey (Harland Williams); And Trick and Treat (Brenda Song and Emily Osment) the teenage Halloween Ghosts.</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`: 50
+- `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`: 50
+- `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   | 19.5758       | 19.3933 | 0.9552                  | 0.9663                  | 0.9668                  | 0.9359                 |
+| 0.6731  | 100  | 19.4573       | 19.0971 | 0.9571                  | 0.9646                  | 0.9653                  | 0.9450                 |
+| 1.0097  | 150  | 19.1409       | 18.4070 | 0.9385                  | 0.9434                  | 0.9473                  | 0.9307                 |
+| 1.3462  | 200  | 18.6431       | 17.3292 | 0.9126                  | 0.9164                  | 0.9184                  | 0.9094                 |
+| 1.6828  | 250  | 18.2288       | 16.8751 | 0.9063                  | 0.9071                  | 0.9100                  | 0.9023                 |
+| 2.0194  | 300  | 18.0425       | 16.6981 | 0.9020                  | 0.9032                  | 0.9045                  | 0.8990                 |
+| 2.3559  | 350  | 17.9458       | 16.6155 | 0.9037                  | 0.9013                  | 0.9022                  | 0.8984                 |
+| 2.6925  | 400  | 17.8525       | 16.5536 | 0.8978                  | 0.8971                  | 0.8974                  | 0.8948                 |
+| 3.0290  | 450  | 17.7529       | 16.5136 | 0.8980                  | 0.8956                  | 0.8953                  | 0.8951                 |
+| 3.3656  | 500  | 17.6709       | 16.4824 | 0.8932                  | 0.8914                  | 0.8928                  | 0.8907                 |
+| 3.7021  | 550  | 17.5348       | 16.4632 | 0.8863                  | 0.8858                  | 0.8859                  | 0.8849                 |
+| 4.0387  | 600  | 17.4198       | 16.4601 | 0.8852                  | 0.8862                  | 0.8859                  | 0.8839                 |
+| 4.3753  | 650  | 17.3673       | 16.4405 | 0.8854                  | 0.8864                  | 0.8865                  | 0.8842                 |
+| 4.7118  | 700  | 17.2603       | 16.4356 | 0.8835                  | 0.8838                  | 0.8838                  | 0.8807                 |
+| 5.0484  | 750  | 17.1807       | 16.4443 | 0.8850                  | 0.8864                  | 0.8859                  | 0.8838                 |
+| 5.3849  | 800  | 17.1629       | 16.4202 | 0.8848                  | 0.8862                  | 0.8867                  | 0.8842                 |
+| 5.7215  | 850  | 17.0747       | 16.4162 | 0.8854                  | 0.8875                  | 0.8869                  | 0.8837                 |
+| 6.0581  | 900  | 17.0161       | 16.4192 | 0.8852                  | 0.8863                  | 0.8856                  | 0.8856                 |
+| 6.3946  | 950  | 17.0146       | 16.4033 | 0.8849                  | 0.8854                  | 0.8856                  | 0.8844                 |
+| 6.7312  | 1000 | 16.9393       | 16.4053 | 0.8829                  | 0.8839                  | 0.8848                  | 0.8835                 |
+| 7.0677  | 1050 | 16.899        | 16.4162 | 0.8826                  | 0.8829                  | 0.8833                  | 0.8818                 |
+| 7.4043  | 1100 | 16.9112       | 16.4051 | 0.8829                  | 0.8835                  | 0.8833                  | 0.8820                 |
+| 7.7408  | 1150 | 16.8508       | 16.4044 | 0.8822                  | 0.8825                  | 0.8830                  | 0.8820                 |
+| 8.0774  | 1200 | 16.8104       | 16.4063 | 0.8816                  | 0.8816                  | 0.8814                  | 0.8817                 |
+| 8.4140  | 1250 | 16.8212       | 16.4040 | 0.8835                  | 0.8822                  | 0.8822                  | 0.8820                 |
+| 8.7505  | 1300 | 16.7743       | 16.3934 | 0.8822                  | 0.8824                  | 0.8817                  | 0.8810                 |
+| 9.0871  | 1350 | 16.7383       | 16.3963 | 0.8810                  | 0.8820                  | 0.8807                  | 0.8800                 |
+| 9.4236  | 1400 | 16.743        | 16.4067 | 0.8819                  | 0.8822                  | 0.8819                  | 0.8798                 |
+| 9.7602  | 1450 | 16.7047       | 16.3959 | 0.8804                  | 0.8810                  | 0.8810                  | 0.8797                 |
+| 10.0968 | 1500 | 16.6782       | 16.3986 | 0.8788                  | 0.8791                  | 0.8796                  | 0.8784                 |
+| 10.4333 | 1550 | 16.6708       | 16.4016 | 0.8794                  | 0.8792                  | 0.8797                  | 0.8791                 |
+| 10.7699 | 1600 | 16.6485       | 16.3963 | 0.8790                  | 0.8801                  | 0.8791                  | 0.8781                 |
+| 11.1064 | 1650 | 16.6205       | 16.4012 | 0.8779                  | 0.8787                  | 0.8793                  | 0.8771                 |
+| 11.4430 | 1700 | 16.6095       | 16.4131 | 0.8786                  | 0.8790                  | 0.8794                  | 0.8791                 |
+| 11.7796 | 1750 | 16.5891       | 16.4070 | 0.8807                  | 0.8805                  | 0.8810                  | 0.8801                 |
+| 12.1161 | 1800 | 16.5619       | 16.3963 | 0.8794                  | 0.8800                  | 0.8797                  | 0.8780                 |
+| 12.4527 | 1850 | 16.5467       | 16.3991 | 0.8796                  | 0.8806                  | 0.8804                  | 0.8790                 |
+| 12.7892 | 1900 | 16.5398       | 16.3970 | 0.8792                  | 0.8798                  | 0.8801                  | 0.8788                 |
+| 13.1258 | 1950 | 16.5047       | 16.3964 | 0.8796                  | 0.8804                  | 0.8804                  | 0.8788                 |
+| 13.4623 | 2000 | 16.4985       | 16.4025 | 0.8793                  | 0.8798                  | 0.8807                  | 0.8790                 |
+| 13.7989 | 2050 | 16.4852       | 16.4107 | 0.8801                  | 0.8810                  | 0.8800                  | 0.8793                 |
+| 14.1355 | 2100 | 16.4526       | 16.3929 | 0.8797                  | 0.8801                  | 0.8809                  | 0.8779                 |
+| 14.4720 | 2150 | 16.4343       | 16.4075 | 0.8788                  | 0.8791                  | 0.8797                  | 0.8774                 |
+| 14.8086 | 2200 | 16.4244       | 16.4027 | 0.8804                  | 0.8819                  | 0.8820                  | 0.8809                 |
+| 15.1451 | 2250 | 16.3947       | 16.4102 | 0.8791                  | 0.8792                  | 0.8803                  | 0.8773                 |
+| 15.4817 | 2300 | 16.3827       | 16.4042 | 0.8804                  | 0.8813                  | 0.8813                  | 0.8781                 |
+| 15.8183 | 2350 | 16.3719       | 16.4003 | 0.8801                  | 0.8818                  | 0.8820                  | 0.8791                 |
+| 16.1548 | 2400 | 16.3403       | 16.4132 | 0.8781                  | 0.8791                  | 0.8799                  | 0.8767                 |
+| 16.4914 | 2450 | 16.3357       | 16.4149 | 0.8804                  | 0.8809                  | 0.8807                  | 0.8792                 |
+| 16.8279 | 2500 | 16.3203       | 16.4081 | 0.8804                  | 0.8814                  | 0.8816                  | 0.8791                 |
+| 17.1645 | 2550 | 16.2986       | 16.4139 | 0.8798                  | 0.8800                  | 0.8820                  | 0.8791                 |
+| 17.5011 | 2600 | 16.2923       | 16.4062 | 0.8786                  | 0.8792                  | 0.8799                  | 0.8768                 |
+| 17.8376 | 2650 | 16.2649       | 16.4106 | 0.8800                  | 0.8807                  | 0.8814                  | 0.8787                 |
+| 18.1742 | 2700 | 16.2505       | 16.4188 | 0.8786                  | 0.8793                  | 0.8803                  | 0.8771                 |
+| 18.5107 | 2750 | 16.226        | 16.4149 | 0.8771                  | 0.8781                  | 0.8780                  | 0.8766                 |
+| 18.8473 | 2800 | 16.2106       | 16.4230 | 0.8780                  | 0.8799                  | 0.8791                  | 0.8767                 |
+| 19.1838 | 2850 | 16.2052       | 16.4351 | 0.8770                  | 0.8777                  | 0.8785                  | 0.8745                 |
+| 19.5204 | 2900 | 16.186        | 16.4331 | 0.8777                  | 0.8793                  | 0.8792                  | 0.8762                 |
+| 19.8570 | 2950 | 16.1496       | 16.4377 | 0.8774                  | 0.8781                  | 0.8780                  | 0.8771                 |
+| 20.1935 | 3000 | 16.151        | 16.4407 | 0.8766                  | 0.8780                  | 0.8780                  | 0.8751                 |
+| 20.5301 | 3050 | 16.1081       | 16.4426 | 0.8759                  | 0.8775                  | 0.8774                  | 0.8749                 |
+| 20.8666 | 3100 | 16.0864       | 16.4412 | 0.8774                  | 0.8781                  | 0.8787                  | 0.8746                 |
+| 21.2032 | 3150 | 16.0934       | 16.4547 | 0.8768                  | 0.8783                  | 0.8794                  | 0.8746                 |
+| 21.5398 | 3200 | 16.0382       | 16.4589 | 0.8742                  | 0.8752                  | 0.8766                  | 0.8723                 |
+| 21.8763 | 3250 | 16.0279       | 16.4668 | 0.8752                  | 0.8766                  | 0.8773                  | 0.8728                 |
+| 22.2129 | 3300 | 16.0327       | 16.4737 | 0.8742                  | 0.8768                  | 0.8773                  | 0.8727                 |
+| 22.5494 | 3350 | 15.979        | 16.4686 | 0.8740                  | 0.8771                  | 0.8771                  | 0.8722                 |
+| 22.8860 | 3400 | 15.9622       | 16.4736 | 0.8743                  | 0.8760                  | 0.8765                  | 0.8721                 |
+| 23.2225 | 3450 | 15.9881       | 16.4802 | 0.8743                  | 0.8757                  | 0.8755                  | 0.8723                 |
+| 23.5591 | 3500 | 15.9482       | 16.4821 | 0.8725                  | 0.8761                  | 0.8761                  | 0.8710                 |
+| 23.8957 | 3550 | 15.9228       | 16.4996 | 0.8726                  | 0.8748                  | 0.8751                  | 0.8709                 |
+| 24.2322 | 3600 | 15.9418       | 16.4973 | 0.8709                  | 0.8728                  | 0.8734                  | 0.8699                 |
+| 24.5688 | 3650 | 15.896        | 16.4985 | 0.8696                  | 0.8716                  | 0.8727                  | 0.8686                 |
+| 24.9053 | 3700 | 15.8788       | 16.5172 | 0.8691                  | 0.8715                  | 0.8717                  | 0.8662                 |
+| 25.2419 | 3750 | 15.9147       | 16.5062 | 0.8677                  | 0.8706                  | 0.8712                  | 0.8662                 |
+| 25.5785 | 3800 | 15.857        | 16.5058 | 0.8683                  | 0.8717                  | 0.8732                  | 0.8663                 |
+| 25.9150 | 3850 | 15.8291       | 16.5207 | 0.8674                  | 0.8702                  | 0.8706                  | 0.8644                 |
+| 26.2516 | 3900 | 15.8802       | 16.5233 | 0.8678                  | 0.8697                  | 0.8714                  | 0.8664                 |
+| 26.5881 | 3950 | 15.846        | 16.5170 | 0.8686                  | 0.8713                  | 0.8717                  | 0.8655                 |
+| 26.9247 | 4000 | 15.8012       | 16.5336 | 0.8663                  | 0.8682                  | 0.8699                  | 0.8635                 |
+
+
+### 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}
+}
+```
+
+<!--
+## 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.*
+-->

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
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+  "label2id": {
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

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+    "type": "sentence_transformers.models.Transformer"
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+  },
+  {
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+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/special_tokens_map.json

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+    "lstrip": false,
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+    "single_word": false
+  }
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-4000/tokenizer_config.json

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+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
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+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/1_Pooling/config.json

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+{
+  "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
+}

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

@@ -0,0 +1,652 @@
+---
+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]
+```
+
+<!--
+### 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
+
+#### 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     |
+
+<!--
+## 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
+
+#### 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}
+}
+```
+
+<!--
+## 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.*
+-->

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/config.json

@@ -0,0 +1,31 @@
+{
+  "_name_or_path": "BAAI/bge-small-en",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
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+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.41.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.41.2",
+    "pytorch": "2.1.2+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/model.safetensors

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+size 133462128

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/modules.json

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+[
+  {
+    "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"
+  }
+]

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/optimizer.pt

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/rng_state.pth

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/scheduler.pt

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bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/sentence_bert_config.json

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

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/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,
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+    "single_word": false
+  },
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+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

bge-small-hotpotwa-matryoshka-fine-tuned-50/checkpoint-500/tokenizer_config.json

@@ -0,0 +1,57 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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