finbeddings_bert

SentenceTransformer based on google-bert/bert-base-uncased

This is a sentence-transformers model finetuned from google-bert/bert-base-uncased on the all-nli-pair, all-nli-pair-score, all-nli-triplet, stsb, quora, natural-questions, fin-phrases and sec-july datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: google-bert/bert-base-uncased <!-- at revision 86b5e0934494bd15c9632b12f734a8a67f723594 -->
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity
• Training Datasets:
  • all-nli-pair
  • all-nli-pair-score
  • all-nli-triplet
  • stsb
  • quora
  • natural-questions
  • fin-phrases
  • sec-july
• Language: en

<!-- - **License:** Unknown -->

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("phgelado/finbeddings_bert")
# Run inference
sentences = [
    'Via the move, the company aims annual savings of some EUR3m, the main part of which are expected to be realized this year.',
    'The city has a new public transportation system.',
    'A company will announce quarterly earnings.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

<!-- ### Direct Usage (Transformers) <details><summary>Click to see the direct usage in Transformers</summary> </details> --> <!-- ### Downstream Usage (Sentence Transformers) You can finetune this model on your own dataset. <details><summary>Click to expand</summary> </details> --> <!-- ### Out-of-Scope Use *List how the model may foreseeably be misused and address what users ought not to do with the model.* -->

Evaluation

Metrics

Triplet

• Dataset: sec-july-dev
• Evaluated with <code>TripletEvaluator</code>

<!-- ## 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 Datasets

all-nli-pair

• Dataset: all-nli-pair at d482672
• Size: 314,315 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 5 tokens</li><li>mean: 17.03 tokens</li><li>max: 64 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 9.62 tokens</li><li>max: 31 tokens</li></ul>

• Samples:

  anchor	positive
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is outdoors, on a horse.</code>
  <code>Children smiling and waving at camera</code>	<code>There are children present</code>
  <code>A boy is jumping on skateboard in the middle of a red bridge.</code>	<code>The boy does a skateboarding trick.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

all-nli-pair-score

• Dataset: all-nli-pair-score at d482672
• Size: 942,069 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 6 tokens</li><li>mean: 17.38 tokens</li><li>max: 52 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 10.7 tokens</li><li>max: 31 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is training his horse for a competition.</code>	<code>0.5</code>
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is at a diner, ordering an omelette.</code>	<code>0.0</code>
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is outdoors, on a horse.</code>	<code>1.0</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

all-nli-triplet

• Dataset: all-nli-triplet at d482672
• Size: 557,850 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: 10.46 tokens</li><li>max: 46 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>A person on a horse jumps over a broken down airplane.</code>	<code>A person is outdoors, on a horse.</code>	<code>A person is at a diner, ordering an omelette.</code>
  <code>Children smiling and waving at camera</code>	<code>There are children present</code>	<code>The kids are frowning</code>
  <code>A boy is jumping on skateboard in the middle of a red bridge.</code>	<code>The boy does a skateboarding trick.</code>	<code>The boy skates down the sidewalk.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

stsb

• Dataset: stsb at ab7a5ac
• Size: 5,749 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 6 tokens</li><li>mean: 10.0 tokens</li><li>max: 28 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 9.95 tokens</li><li>max: 25 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.54</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>A plane is taking off.</code>	<code>An air plane is taking off.</code>	<code>1.0</code>
  <code>A man is playing a large flute.</code>	<code>A man is playing a flute.</code>	<code>0.76</code>
  <code>A man is spreading shreded cheese on a pizza.</code>	<code>A man is spreading shredded cheese on an uncooked pizza.</code>	<code>0.76</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

quora

• Dataset: quora at 451a485
• Size: 149,263 training samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 13.92 tokens</li><li>max: 42 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.09 tokens</li><li>max: 43 tokens</li></ul>

• Samples:

  anchor	positive
  <code>Astrology: I am a Capricorn Sun Cap moon and cap rising...what does that say about me?</code>	<code>I'm a triple Capricorn (Sun, Moon and ascendant in Capricorn) What does this say about me?</code>
  <code>How can I be a good geologist?</code>	<code>What should I do to be a great geologist?</code>
  <code>How do I read and find my YouTube comments?</code>	<code>How can I see all my Youtube comments?</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 100,231 training samples
• Columns: <code>query</code> and <code>answer</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	<ul><li>min: 10 tokens</li><li>mean: 11.74 tokens</li><li>max: 21 tokens</li></ul>	<ul><li>min: 17 tokens</li><li>mean: 135.66 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  query	answer
  <code>when did richmond last play in a preliminary final</code>	<code>Richmond Football Club Richmond began 2017 with 5 straight wins, a feat it had not achieved since 1995. A series of close losses hampered the Tigers throughout the middle of the season, including a 5-point loss to the Western Bulldogs, 2-point loss to Fremantle, and a 3-point loss to the Giants. Richmond ended the season strongly with convincing victories over Fremantle and St Kilda in the final two rounds, elevating the club to 3rd on the ladder. Richmond's first final of the season against the Cats at the MCG attracted a record qualifying final crowd of 95,028; the Tigers won by 51 points. Having advanced to the first preliminary finals for the first time since 2001, Richmond defeated Greater Western Sydney by 36 points in front of a crowd of 94,258 to progress to the Grand Final against Adelaide, their first Grand Final appearance since 1982. The attendance was 100,021, the largest crowd to a grand final since 1986. The Crows led at quarter time and led by as many as 13, but the Tigers took over the game as it progressed and scored seven straight goals at one point. They eventually would win by 48 points – 16.12 (108) to Adelaide's 8.12 (60) – to end their 37-year flag drought.[22] Dustin Martin also became the first player to win a Premiership medal, the Brownlow Medal and the Norm Smith Medal in the same season, while Damien Hardwick was named AFL Coaches Association Coach of the Year. Richmond's jump from 13th to premiers also marked the biggest jump from one AFL season to the next.</code>
  <code>who sang what in the world's come over you</code>	<code>Jack Scott (singer) At the beginning of 1960, Scott again changed record labels, this time to Top Rank Records.[1] He then recorded four Billboard Hot 100 hits – "What in the World's Come Over You" (#5), "Burning Bridges" (#3) b/w "Oh Little One" (#34), and "It Only Happened Yesterday" (#38).[1] "What in the World's Come Over You" was Scott's second gold disc winner.[6] Scott continued to record and perform during the 1960s and 1970s.[1] His song "You're Just Gettin' Better" reached the country charts in 1974.[1] In May 1977, Scott recorded a Peel session for BBC Radio 1 disc jockey, John Peel.</code>
  <code>who produces the most wool in the world</code>	<code>Wool Global wool production is about 2 million tonnes per year, of which 60% goes into apparel. Wool comprises ca 3% of the global textile market, but its value is higher owing to dying and other modifications of the material.[1] Australia is a leading producer of wool which is mostly from Merino sheep but has been eclipsed by China in terms of total weight.[30] New Zealand (2016) is the third-largest producer of wool, and the largest producer of crossbred wool. Breeds such as Lincoln, Romney, Drysdale, and Elliotdale produce coarser fibers, and wool from these sheep is usually used for making carpets.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

fin-phrases

• Dataset: fin-phrases
• Size: 8,484 training samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 30.4 tokens</li><li>max: 98 tokens</li></ul>	<ul><li>min: 7 tokens</li><li>mean: 14.09 tokens</li><li>max: 38 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>No financial details were revealed.</code>	<code>The company is secretive about its earnings.</code>	<code>0.5</code>
  <code>Earlier today, Geberit's Finnish rival Uponor OYJ cut its full-year sales growth forecast to 6 pct from 10 pct, blaming tough conditions in Germany and the US, as well as currency factors.</code>	<code>Uponor OYJ reduced its forecasted sales growth by 4 percentage points.</code>	<code>1.0</code>
  <code>Thanks to the internet , consumers compare products more than previously and Finnish companies are not competitive .</code>	<code>Consumers compare prices of electronics online.</code>	<code>0.5</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

sec-july

• Dataset: sec-july
• Size: 78,972 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: 5 tokens</li><li>mean: 95.0 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 11.7 tokens</li><li>max: 34 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 11.34 tokens</li><li>max: 30 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>BORROWINGS Following is a summary of short-term borrowings: TABLE 10.1 (in millions) June 30, 2022 December 31, 2021 Securities sold under repurchase agreements $ 328 $ 376 Federal Home Loan Bank advances 930 1,030 Subordinated notes 133 130 Total short-term borrowings $ 1,391 $ 1,536 Borrowings with original maturities of one year or less are classified as short-term.</code>	<code>Bank borrowing information is provided.</code>	<code>Personal savings goals for retirement are discussed.</code>
  <code>Recently issued accounting guidance Income Taxes In December 2023, the Financial Accounting Standards Board (the FASB) issued Accounting Standards Update (ASU) No. 2023-09, Income Taxes (Topic 740): Improvements to Income Tax Disclosures (ASU 2023-09), which will require the Company to disclose specified additional information in its income tax rate reconciliation and provide additional information for reconciling items that meet a quantitative threshold.</code>	<code>The FASB has issued new income tax disclosure requirements.</code>	<code>The FASB has updated its accounting standards for revenue recognition.</code>
  <code>The note was discounted for a derivative and the discount of $52,000 is being amortized over the life of the note using the effective interest method. During the three months ended March 31, 2023, the amortization of note discount was $12,822. As of December 31, 2023 the unamortized note discount was fully amortized.</code>	<code>Note discount amortization process</code>	<code>Note discount increased</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Datasets

all-nli-triplet

• Dataset: all-nli-triplet at d482672
• Size: 6,584 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: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul>	<ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>Two women are embracing while holding to go packages.</code>	<code>Two woman are holding packages.</code>	<code>The men are fighting outside a deli.</code>
  <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code>	<code>Two kids in numbered jerseys wash their hands.</code>	<code>Two kids in jackets walk to school.</code>
  <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code>	<code>A man selling donuts to a customer.</code>	<code>A woman drinks her coffee in a small cafe.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

stsb

• Dataset: stsb at ab7a5ac
• Size: 1,500 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 15.1 tokens</li><li>max: 45 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 15.11 tokens</li><li>max: 53 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.47</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>A man with a hard hat is dancing.</code>	<code>A man wearing a hard hat is dancing.</code>	<code>1.0</code>
  <code>A young child is riding a horse.</code>	<code>A child is riding a horse.</code>	<code>0.95</code>
  <code>A man is feeding a mouse to a snake.</code>	<code>The man is feeding a mouse to the snake.</code>	<code>1.0</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

quora

• Dataset: quora at 451a485
• Size: 1,000 evaluation samples
• Columns: <code>anchor</code> and <code>positive</code>
• Approximate statistics based on the first 1000 samples:

  	anchor	positive
  type	string	string
  details	<ul><li>min: 6 tokens</li><li>mean: 14.05 tokens</li><li>max: 70 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.11 tokens</li><li>max: 49 tokens</li></ul>

• Samples:

  anchor	positive
  <code>What is your New Year resolution?</code>	<code>What can be my new year resolution for 2017?</code>
  <code>Should I buy the IPhone 6s or Samsung Galaxy s7?</code>	<code>Which is better: the iPhone 6S Plus or the Samsung Galaxy S7 Edge?</code>
  <code>What are the differences between transgression and regression?</code>	<code>What is the difference between transgression and regression?</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

natural-questions

• Dataset: natural-questions at f9e894e
• Size: 1,000 evaluation samples
• Columns: <code>query</code> and <code>answer</code>
• Approximate statistics based on the first 1000 samples:

  	query	answer
  type	string	string
  details	<ul><li>min: 9 tokens</li><li>mean: 11.8 tokens</li><li>max: 21 tokens</li></ul>	<ul><li>min: 19 tokens</li><li>mean: 138.84 tokens</li><li>max: 512 tokens</li></ul>

• Samples:

  query	answer
  <code>where does the waikato river begin and end</code>	<code>Waikato River The Waikato River is the longest river in New Zealand, running for 425 kilometres (264 mi) through the North Island. It rises in the eastern slopes of Mount Ruapehu, joining the Tongariro River system and flowing through Lake Taupo, New Zealand's largest lake. It then drains Taupo at the lake's northeastern edge, creates the Huka Falls, and flows northwest through the Waikato Plains. It empties into the Tasman Sea south of Auckland, at Port Waikato. It gives its name to the Waikato Region that surrounds the Waikato Plains. The present course of the river was largely formed about 17,000 years ago. Contributing factors were climate warming, forest being reestablished in the river headwaters and the deepening, rather than widening, of the existing river channel. The channel was gradually eroded as far up river as Piarere, leaving the old Hinuera channel high and dry.[2] The remains of the old river path can be clearly seen at Hinuera where the cliffs mark the ancient river edges. The river's main tributary is the Waipa River, which has its confluence with the Waikato at Ngaruawahia.</code>
  <code>what type of gas is produced during fermentation</code>	<code>Fermentation Fermentation reacts NADH with an endogenous, organic electron acceptor.[1] Usually this is pyruvate formed from sugar through glycolysis. The reaction produces NAD+ and an organic product, typical examples being ethanol, lactic acid, carbon dioxide, and hydrogen gas (H2). However, more exotic compounds can be produced by fermentation, such as butyric acid and acetone. Fermentation products contain chemical energy (they are not fully oxidized), but are considered waste products, since they cannot be metabolized further without the use of oxygen.</code>
  <code>why was star wars episode iv released first</code>	<code>Star Wars (film) Star Wars (later retitled Star Wars: Episode IV – A New Hope) is a 1977 American epic space opera film written and directed by George Lucas. It is the first film in the original Star Wars trilogy and the beginning of the Star Wars franchise. Starring Mark Hamill, Harrison Ford, Carrie Fisher, Peter Cushing, Alec Guinness, David Prowse, James Earl Jones, Anthony Daniels, Kenny Baker, and Peter Mayhew, the film's plot focuses on the Rebel Alliance, led by Princess Leia (Fisher), and its attempt to destroy the Galactic Empire's space station, the Death Star. This conflict disrupts the isolated life of farmhand Luke Skywalker (Hamill), who inadvertently acquires two droids that possess stolen architectural plans for the Death Star. When the Empire begins a destructive search for the missing droids, Skywalker accompanies Jedi Master Obi-Wan Kenobi (Guinness) on a mission to return the plans to the Rebel Alliance and rescue Leia from her imprisonment by the Empire.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

fin-phrases

• Dataset: fin-phrases
• Size: 2,121 evaluation samples
• Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>score</code>
• Approximate statistics based on the first 1000 samples:

  	sentence1	sentence2	score
  type	string	string	float
  details	<ul><li>min: 5 tokens</li><li>mean: 30.52 tokens</li><li>max: 74 tokens</li></ul>	<ul><li>min: 6 tokens</li><li>mean: 14.36 tokens</li><li>max: 40 tokens</li></ul>	<ul><li>min: 0.0</li><li>mean: 0.5</li><li>max: 1.0</li></ul>

• Samples:

  sentence1	sentence2	score
  <code>Both operating profit and net sales for the 12-month period increased , respectively from EUR21 .5 m and EUR196 .1 m , as compared to 2005 .</code>	<code>The company's environmental policy includes recycling programs for all employees.</code>	<code>0.0</code>
  <code>Protalix is developing genetically engineered proteins from plant cells .</code>	<code>Biotechnology companies are working on protein development.</code>	<code>0.5</code>
  <code>Nordic banks have already had to write off sizable loans in Latvia , with Swedbank , Nordea , DnB NOR and SEB reporting combined losses in excess of $ 1.35 billion in the period 2007 to 2010 against a backdrop of near economic meltdown in Latvia .</code>	<code>Several banks faced financial difficulties due to loan write-offs.</code>	<code>0.5</code>

• Loss: <code>CoSENTLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "pairwise_cos_sim"
  }
  

sec-july

• Dataset: sec-july
• Size: 9,872 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: 5 tokens</li><li>mean: 101.01 tokens</li><li>max: 512 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 11.68 tokens</li><li>max: 33 tokens</li></ul>	<ul><li>min: 5 tokens</li><li>mean: 11.2 tokens</li><li>max: 26 tokens</li></ul>

• Samples:

  anchor	positive	negative
  <code>The Fund is a limited liability company organized under the laws of the state of Delaware and has elected to be treated as a RIC for U.S. federal income tax purposes.</code>	<code>The Fund is a limited liability company</code>	<code>The Fund is a partnership</code>
  <code>On April 21, 2022, the WVPSC issued an order approving, effective May 1, 2022, a tariff to offer solar power on a voluntary basis to West Virginia customers and requiring MP and PE to subscribe at least 85% of the planned 50 MWs of solar generation before seeking approval for surcharge cost recovery. MP and PE must seek separate approval from the WVPSC to recover any solar generation costs in excess of the approved solar power tariff.</code>	<code>Solar power tariff approved for West Virginia</code>	<code>Wind power tariff approved for West Virginia</code>
  <code>Warrants, each whole warrant exercisable for one Ordinary Share for $11.50 per share</code>	<code>Warrants can be converted to shares.</code>	<code>Warrants are not related to shares.</code>

• Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• learning_rate: 5e-06
• num_train_epochs: 5
• warmup_ratio: 0.1
• bf16: True
• batch_sampler: no_duplicates

All Hyperparameters

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

• overwrite_output_dir: False
• do_predict: False
• eval_strategy: steps
• prediction_loss_only: True
• per_device_train_batch_size: 32
• per_device_eval_batch_size: 32
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• torch_empty_cache_steps: None
• learning_rate: 5e-06
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 5
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.1
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: True
• fp16: False
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: False
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• eval_use_gather_object: False
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

</details>

Training Logs

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

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