metadata
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
model-index:
- name: FINGU-AI/FingUEm_V3
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
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:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: validation
type: mteb/amazon_counterfactual
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:
config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
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:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
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:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: validation
type: mteb/amazon_reviews_multi
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:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
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:
config: default
name: MTEB EmotionClassification (default)
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
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:
config: default
name: MTEB EmotionClassification (default)
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: validation
type: mteb/emotion
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:
config: default
name: MTEB ImdbClassification (default)
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
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:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
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:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: validation
type: mteb/mtop_domain
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:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
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:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: validation
type: mteb/mtop_intent
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:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
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:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: validation
type: mteb/amazon_massive_intent
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:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
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:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: validation
type: mteb/amazon_massive_scenario
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:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
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:
config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 63.17770232031692
- type: f1
value: 63.33879583206008
- type: f1_weighted
value: 62.27745749800532
- type: main_score
value: 63.17770232031692
task:
type: Classification
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 Pa�acuelos 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.
SentenceTransformer based on Alibaba-NLP/gte-Qwen2-1.5B-instruct
This is a sentence-transformers model finetuned from 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
- Maximum Sequence Length: 8192 tokens
- Output Dimensionality: 1536 tokens
- Similarity Function: Cosine Similarity
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': 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:
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("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]
Citation
BibTeX
Sentence Transformers
@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
@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
@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},
}