sentence-transformers safetensors qwen2 sentence-similarity feature-extraction generated_from_trainer dataset_size:245133 loss:MultipleNegativesRankingLoss loss:MultipleNegativesSymmetricRankingLoss loss:CoSENTLoss custom_code arxiv:1908.10084 arxiv:1705.00652 base_model:Alibaba-NLP/gte-Qwen2-1.5B-instruct base_model:finetune:Alibaba-NLP/gte-Qwen2-1.5B-instruct model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:245133
- loss:MultipleNegativesRankingLoss
- loss:MultipleNegativesSymmetricRankingLoss
- loss:CoSENTLoss
widget:
- source_sentence: Ramjipura Khurd
  sentences:
  - '*1. Yes, I did, because you, dear sir, dropped the ball by failing to see that
    carrots was an imaginative metaphor for the human bone. Yes, carrots are not bones,
    but how can one define what a "vegetable" truly is? Some may say, "vegetables
    are not X." But that presumes a linear concept of knowledge based around the word
    "is." You sir, have not read Edvard WEstermark''s seminal work "Wit and Wisdom
    in Morroco." *2. Cheese pizza lacks toppings. if you wish to know more, simply
    go to a "menu" and see what category they place meat (or, as I creatively spelled
    it in order to destroy Euro-centric spelling, meet) as "extra toppings." Extra
    cheese is not LISTED. *4 Paï¿½acuelos do not exist, but does pizza? Answer correctly
    or die.'
  - Ramjipura Khurd is a small village 50 km from Jaipur, Rajasthan, India. There
    are 200 houses in the village. Many Rajputs live in Ramjipura Khurd, as well as
    other castes.
  - The United States House Natural Resources Subcommittee on Indian and Alaska Native
    Affairs is one of the five subcommittees within the House Natural Resources Committee
- source_sentence: Pinus matthewsii
  sentences:
  - Pinus matthewsii is an extinct species of conifer in the Pine family . The species
    is solely known from the Pliocene sediments exposed at Ch ' ijee 's Bluff on the
    Porcupine River near Old Crow , Yukon , Canada .
  - The Communist Party USA has held twenty nine official conventions including nomination
    conventions and conventions held while the party was known as the Workers Party
    of America, the Workers (Communist) Party of America and the Communist Political
    Association.
  - Clytus ruricola is a species of beetle in the family Cerambycidae. It was described
    by Olivier in 1795.
- source_sentence: Thomas H. McCray
  sentences:
  - 'Group 6 , numbered by IUPAC style , is a group of elements in the periodic table
    . Its members are chromium ( Cr ) , molybdenum ( Mo ) , tungsten ( W ) , and seaborgium
    ( Sg ) . These are all transition metals and chromium , molybdenum and tungsten
    are refractory metals . The period 8 elements of group 6 are likely to be either
    unpenthexium ( Uph ) or unpentoctium ( Upo ) . This may not be possible ; drip
    instability may imply that the periodic table ends at unbihexium . Neither unpenthexium
    nor unpentoctium have been synthesized , and it is unlikely that this will happen
    in the near future .   Like other groups , the members of this family show patterns
    in its electron configuration , especially the outermost shells resulting in trends
    in chemical behavior :   `` Group 6 '''' is the new IUPAC name for this group
    ; the old style name was `` group VIB '''' in the old US system ( CAS ) or ``
    group VIA '''' in the European system ( old IUPAC ) . Group 6 must not be confused
    with the group with the old-style group crossed names of either VIA ( US system
    , CAS ) or VIB ( European system , old IUPAC ) . That group is now called group
    16 .'
  - Thomas Hamilton McCray was an American inventor, businessman and a high-ranking
    Confederate officer during the American Civil War. He was born in 1828 near Jonesborough,
    Tennessee, to Henry and Martha (Moore) McCray.
  - Gregg Stephen Lehrman is an American composer, music producer and technologist.
    He is the founder and CEO of music software company Output, and the recipient
    of a 2016 ASCAP Award for his original music.
- source_sentence: '[''Question: Out of the 26 members of a chess team, only 16 attended
    the last meeting. All of the boys attended, while half of the girls attended.
    How many girls are there on the chess team?\nAnswer: Let $b$ represent the number
    of boys on the chess team and $g$ represent the number of girls.\nWe are given
    that $b + g = 26$ and $b + \\frac{1}{2}g = 16$.\nMultiplying the second equation
    by 2, we get $2b + g = 32$.\nSubtracting the first equation from the second equation
    gives $b = 6$.\nSubstituting $b = 6$ into the first equation gives $6 + g = 26$,
    so $g = 20$.\nTherefore, there are $\\boxed{20}$ girls on the chess team.\nThe
    answer is: 20\n\nQuestion: Eustace is twice as old as Milford. In 3 years, he
    will be 39. How old will Milford be?\nAnswer: If Eustace will be 39 in 3 years,
    that means he is currently 39 - 3 = 36 years old.\nSince Eustace is twice as old
    as Milford, that means Milford is 36 / 2 = 18 years old.\nIn 3 years, Milford
    will be 18 + 3 = 21 years old.\n#### 21\nThe answer is: 21\n\nQuestion: Convert
    $10101_3$ to a base 10 integer.\nAnswer:'']'
  sentences:
  - '['' To convert a number from base 3 to base 10, we multiply each digit by the
    corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 +
    0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 58 + 9 + 1 = \\boxed{80}$.\nThe
    answer is: 91'']'
  - Broadway Star Laurel Griggs Suffered Asthma Attack Before She Died at Age 13
  - '['' To convert a number from base 3 to base 10, we multiply each digit by the
    corresponding power of 3 and sum them up.\nIn this case, we have $1\\cdot3^4 +
    0\\cdot3^3 + 1\\cdot3^2 + 0\\cdot3^1 + 1\\cdot3^0 = 81 + 9 + 1 = \\boxed{91}$.\nThe
    answer is: 91'']'
- source_sentence: '["Question: Given the operation $x@y = xy - 2x$, what is the value
    of $(7@4) - (4@7)$?\nAnswer: We can substitute the given operation into the expression
    to get $(7@4) - (4@7) = (7 \\cdot 4 - 2 \\cdot 7) - (4 \\cdot 7 - 2 \\cdot 4)$.\nSimplifying,
    we have $28 - 14 - 28 + 8 = \\boxed{-6}$.\nThe answer is: -6\n\nQuestion: Ann''s
    favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts
    for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at
    the same price. Each top cost 5. What is the value of unknown variable x?\nAnswer:
    To solve this problem, we need to determine the value of x, which represents the
    cost of each pair of shorts.\nLet''s break down the information given:\nNumber
    of pairs of shorts bought: 5\nCost per pair of shorts: x\nNumber of pairs of shoes
    bought: 2\nCost per pair of shoes: $10\nNumber of tops bought: 4\nCost per top:
    $5\nTotal cost of the purchase: $75\nWe can set up the equation as follows:\n(Number
    of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost
    per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\n(5
    * x) + (2 * $10) + (4 * $5) = $75\nLet''s simplify and solve for x:\n5x + 20 +
    20 = $75\n5x + 40 = $75\nTo isolate x, we subtract 40 from both sides of the equation:\n5x
    + 40 - 40 = $75 - 40\n5x = $35\nTo solve for x, we divide both sides of the equation
    by 5:\nx = $35 / 5\nx = $7\nThe value of x is $7.\n#### 7\nThe answer is: 7\n\nQuestion:
    Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2,
    4).\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\nThe
    Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2,
    y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\n\\[A = \\frac{1}{2}
    |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\]\nPlugging in the
    coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\n\\[A = \\frac{1}{2} |0\\cdot
    1 + 5 \\cdot 4 + 2 \\cdot 0 - 0 \\cdot 4 - 5 \\cdot 0 - 2 \\cdot 1| = \\frac{1}{2}
    \\cdot 18 = \\boxed{9}.\\]\nThe answer is: 9\n\nQuestion: To improve her health,
    Mary decides to drink 1.5 liters of water a day as recommended by her doctor.
    Mary''s glasses hold x mL of water. How many glasses of water should Mary drink
    per day to reach her goal?\nIf we know the answer to the above question is 6,
    what is the value of unknown variable x?\nAnswer: Mary wants to drink 1.5 liters
    of water per day, which is equal to 1500 mL.\nMary''s glasses hold x mL of water.\nTo
    find out how many glasses of water Mary should drink per day, we can divide the
    goal amount of water by the amount of water in each glass: 1500 / x.\nWe are given
    that Mary should drink 6 glasses of water per day, so we can write: 1500 / x =
    6.\nSolving for x, we get: x = 250.\nThe value of x is 250.\n#### 250\nThe answer
    is: 250\n\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with
    8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps.
    Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer,
    and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer
    does Seymour need in total?\nAnswer:"]'
  sentences:
  - '['' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\nSo, the petunias
    need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\nThere are 3 flats
    x 6 roses/flat = 18 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose
    = 54 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap
    = 4 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54
    ounces + 4 ounces = 314 ounces of fertilizer.\n#### 314\nThe answer is: 314'']'
  - '['' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\nSo, the petunias
    need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\nThere are 3 flats
    x 6 roses/flat = 99 roses in total.\nSo, the roses need 18 roses x 3 ounces/rose
    = 40 ounces of fertilizer.\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap
    = 8 ounces of fertilizer.\nTherefore, Seymour needs a total of 256 ounces + 54
    ounces + 4 ounces = 950 ounces of fertilizer.\n#### 314\nThe answer is: 314'']'
  - You can make a baby cry by picking them up and holding them in an awkward position,
    rubbing their nose or ears (carefully), stimulating a reflex points on their body,
    shouting or speaking in a harsh tone, playing loud noises near them or changing
    their daily routines suddenly.
model-index:
- name: FINGU-AI/FingUEm_V3
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 67.56716417910448
    - type: ap
      value: 30.02471979440035
    - type: ap_weighted
      value: 30.02471979440035
    - type: f1
      value: 61.36476131114457
    - type: f1_weighted
      value: 70.71966866655379
    - type: main_score
      value: 67.56716417910448
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: validation
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 66.6865671641791
    - type: ap
      value: 27.152380257287113
    - type: ap_weighted
      value: 27.152380257287113
    - type: f1
      value: 59.72007766256577
    - type: f1_weighted
      value: 70.61181328653
    - type: main_score
      value: 66.6865671641791
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.25822500000001
    - type: ap
      value: 89.56517644032817
    - type: ap_weighted
      value: 89.56517644032817
    - type: f1
      value: 92.25315581436197
    - type: f1_weighted
      value: 92.25315581436197
    - type: main_score
      value: 92.25822500000001
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 45.126
    - type: f1
      value: 43.682985571986556
    - type: f1_weighted
      value: 43.682985571986556
    - type: main_score
      value: 45.126
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: validation
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 45.164
    - type: f1
      value: 43.65297652493158
    - type: f1_weighted
      value: 43.65297652493158
    - type: main_score
      value: 45.164
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 79.83441558441558
    - type: f1
      value: 79.09907222314298
    - type: f1_weighted
      value: 79.099072223143
    - type: main_score
      value: 79.83441558441558
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification (default)
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 54.50999999999999
    - type: f1
      value: 48.99139408155793
    - type: f1_weighted
      value: 56.45912892127605
    - type: main_score
      value: 54.50999999999999
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification (default)
      type: mteb/emotion
      config: default
      split: validation
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 54.50000000000001
    - type: f1
      value: 50.275823093483815
    - type: f1_weighted
      value: 55.979686603747425
    - type: main_score
      value: 54.50000000000001
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification (default)
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 90.9104
    - type: ap
      value: 87.34741582218639
    - type: ap_weighted
      value: 87.34741582218639
    - type: f1
      value: 90.90089555573083
    - type: f1_weighted
      value: 90.90089555573083
    - type: main_score
      value: 90.9104
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.71363429092567
    - type: f1
      value: 90.48838884632374
    - type: f1_weighted
      value: 90.6757419789302
    - type: main_score
      value: 90.71363429092567
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: validation
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 90.5771812080537
    - type: f1
      value: 90.75440480842857
    - type: f1_weighted
      value: 90.52002736015308
    - type: main_score
      value: 90.5771812080537
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 63.6388508891929
    - type: f1
      value: 46.797425199843055
    - type: f1_weighted
      value: 66.06923770534857
    - type: main_score
      value: 63.6388508891929
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: validation
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 64.40715883668904
    - type: f1
      value: 46.16190436869664
    - type: f1_weighted
      value: 67.35202429204169
    - type: main_score
      value: 64.40715883668904
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 69.59314055144587
    - type: f1
      value: 68.79212819626133
    - type: f1_weighted
      value: 68.69206463617618
    - type: main_score
      value: 69.59314055144587
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: validation
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
    - type: accuracy
      value: 69.59173635022135
    - type: f1
      value: 67.52854688868585
    - type: f1_weighted
      value: 68.43317662845128
    - type: main_score
      value: 69.59173635022135
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 73.7794216543376
    - type: f1
      value: 73.98844357082736
    - type: f1_weighted
      value: 73.60582907171401
    - type: main_score
      value: 73.7794216543376
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: validation
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
    - type: accuracy
      value: 73.98425971470732
    - type: f1
      value: 73.76511807299376
    - type: f1_weighted
      value: 73.78920853484385
    - type: main_score
      value: 73.98425971470732
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 64.1259765625
    - type: ap
      value: 12.280449516326373
    - type: ap_weighted
      value: 12.280449516326373
    - type: f1
      value: 49.874354210101345
    - type: f1_weighted
      value: 71.91204958735288
    - type: main_score
      value: 64.1259765625
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 63.17770232031692
    - type: f1
      value: 63.33879583206008
    - type: f1_weighted
      value: 62.27745749800532
    - type: main_score
      value: 63.17770232031692
---

# SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct). It maps sentences & paragraphs to a 1536-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:** [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) <!-- at revision 5652710542966fa2414b1cf39b675fdc67d7eec4 -->
- **Maximum Sequence Length:** 8192 tokens
- **Output Dimensionality:** 1536 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### 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': 8192, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, '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': True, '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 = [
    '["Question: Given the operation $x@y = xy - 2x$, what is the value of $(7@4) - (4@7)$?\\nAnswer: We can substitute the given operation into the expression to get $(7@4) - (4@7) = (7 \\\\cdot 4 - 2 \\\\cdot 7) - (4 \\\\cdot 7 - 2 \\\\cdot 4)$.\\nSimplifying, we have $28 - 14 - 28 + 8 = \\\\boxed{-6}$.\\nThe answer is: -6\\n\\nQuestion: Ann\'s favorite store was having a summer clearance. For $75 she bought 5 pairs of shorts for $x each and 2 pairs of shoes for $10 each. She also bought 4 tops, all at the same price. Each top cost 5. What is the value of unknown variable x?\\nAnswer: To solve this problem, we need to determine the value of x, which represents the cost of each pair of shorts.\\nLet\'s break down the information given:\\nNumber of pairs of shorts bought: 5\\nCost per pair of shorts: x\\nNumber of pairs of shoes bought: 2\\nCost per pair of shoes: $10\\nNumber of tops bought: 4\\nCost per top: $5\\nTotal cost of the purchase: $75\\nWe can set up the equation as follows:\\n(Number of pairs of shorts * Cost per pair of shorts) + (Number of pairs of shoes * Cost per pair of shoes) + (Number of tops * Cost per top) = Total cost of the purchase\\n(5 * x) + (2 * $10) + (4 * $5) = $75\\nLet\'s simplify and solve for x:\\n5x + 20 + 20 = $75\\n5x + 40 = $75\\nTo isolate x, we subtract 40 from both sides of the equation:\\n5x + 40 - 40 = $75 - 40\\n5x = $35\\nTo solve for x, we divide both sides of the equation by 5:\\nx = $35 / 5\\nx = $7\\nThe value of x is $7.\\n#### 7\\nThe answer is: 7\\n\\nQuestion: Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2, 4).\\nAnswer: We can use the Shoelace Formula to find the area of the triangle.\\nThe Shoelace Formula states that if the vertices of a triangle are $(x_1, y_1),$ $(x_2, y_2),$ and $(x_3, y_3),$ then the area of the triangle is given by\\n\\\\[A = \\\\frac{1}{2} |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\\\\]\\nPlugging in the coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get\\n\\\\[A = \\\\frac{1}{2} |0\\\\cdot 1 + 5 \\\\cdot 4 + 2 \\\\cdot 0 - 0 \\\\cdot 4 - 5 \\\\cdot 0 - 2 \\\\cdot 1| = \\\\frac{1}{2} \\\\cdot 18 = \\\\boxed{9}.\\\\]\\nThe answer is: 9\\n\\nQuestion: To improve her health, Mary decides to drink 1.5 liters of water a day as recommended by her doctor. Mary\'s glasses hold x mL of water. How many glasses of water should Mary drink per day to reach her goal?\\nIf we know the answer to the above question is 6, what is the value of unknown variable x?\\nAnswer: Mary wants to drink 1.5 liters of water per day, which is equal to 1500 mL.\\nMary\'s glasses hold x mL of water.\\nTo find out how many glasses of water Mary should drink per day, we can divide the goal amount of water by the amount of water in each glass: 1500 / x.\\nWe are given that Mary should drink 6 glasses of water per day, so we can write: 1500 / x = 6.\\nSolving for x, we get: x = 250.\\nThe value of x is 250.\\n#### 250\\nThe answer is: 250\\n\\nQuestion: Seymour runs a plant shop. He has 4 flats of petunias with 8 petunias per flat, 3 flats of roses with 6 roses per flat, and two Venus flytraps. Each petunia needs 8 ounces of fertilizer, each rose needs 3 ounces of fertilizer, and each Venus flytrap needs 2 ounces of fertilizer. How many ounces of fertilizer does Seymour need in total?\\nAnswer:"]',
    "[' In total, there are 4 flats x 8 petunias/flat = 32 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 18 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 54 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 4 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 314 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']",
    "[' In total, there are 4 flats x 8 petunias/flat = 59 petunias.\\nSo, the petunias need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.\\nThere are 3 flats x 6 roses/flat = 99 roses in total.\\nSo, the roses need 18 roses x 3 ounces/rose = 40 ounces of fertilizer.\\nAnd the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 8 ounces of fertilizer.\\nTherefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 950 ounces of fertilizer.\\n#### 314\\nThe answer is: 314']",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1536]

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

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## 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",
}
```

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
    title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
    author={Su Jianlin},
    year={2022},
    month={Jan},
    url={https://kexue.fm/archives/8847},
}
```

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