Model: FINGU-AI/FingUEm_V3
Author: FINGU-AI
Pipeline: sentence-similarity
Library: sentence-transformers
Tags: 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, text-embeddings-inference, endpoints_compatible, region:us
Base models (finetune): id: Alibaba-NLP/gte-Qwen2-1.5B-instruct
Card metadata: base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
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
verified: no, type: ap
value: 30.02471979440035
verified: no, type: ap_weighted
value: 30.02471979440035
verified: no, type: f1
value: 61.36476131114457
verified: no, type: f1_weighted
value: 70.71966866655379
verified: no, type: main_score
value: 67.56716417910448
verified: no
task: type: Classification, dataset: config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: validation
type: mteb/amazon_counterfactual
metrics: type: accuracy
value: 66.6865671641791
verified: no, type: ap
value: 27.152380257287113
verified: no, type: ap_weighted
value: 27.152380257287113
verified: no, type: f1
value: 59.72007766256577
verified: no, type: f1_weighted
value: 70.61181328653
verified: no, type: main_score
value: 66.6865671641791
verified: no
task: type: Classification, dataset: config: default
name: MTEB AmazonPolarityClassification (default)
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics: type: accuracy
value: 92.25822500000001
verified: no, type: ap
value: 89.56517644032817
verified: no, type: ap_weighted
value: 89.56517644032817
verified: no, type: f1
value: 92.25315581436197
verified: no, type: f1_weighted
value: 92.25315581436197
verified: no, type: main_score
value: 92.25822500000001
verified: no
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
verified: no, type: f1
value: 43.682985571986556
verified: no, type: f1_weighted
value: 43.682985571986556
verified: no, type: main_score
value: 45.126
verified: no
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
verified: no, type: f1
value: 43.65297652493158
verified: no, type: f1_weighted
value: 43.65297652493158
verified: no, type: main_score
value: 45.164
verified: no
task: type: Classification, dataset: config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics: type: accuracy
value: 79.83441558441558
verified: no, type: f1
value: 79.09907222314298
verified: no, type: f1_weighted
value: 79.099072223143
verified: no, type: main_score
value: 79.83441558441558
verified: no
task: type: Classification, dataset: config: default
name: MTEB EmotionClassification (default)
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics: type: accuracy
value: 54.50999999999999
verified: no, type: f1
value: 48.99139408155793
verified: no, type: f1_weighted
value: 56.45912892127605
verified: no, type: main_score
value: 54.50999999999999
verified: no
task: type: Classification, dataset: config: default
name: MTEB EmotionClassification (default)
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: validation
type: mteb/emotion
metrics: type: accuracy
value: 54.50000000000001
verified: no, type: f1
value: 50.275823093483815
verified: no, type: f1_weighted
value: 55.979686603747425
verified: no, type: main_score
value: 54.50000000000001
verified: no
task: type: Classification, dataset: config: default
name: MTEB ImdbClassification (default)
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics: type: accuracy
value: 90.9104
verified: no, type: ap
value: 87.34741582218639
verified: no, type: ap_weighted
value: 87.34741582218639
verified: no, type: f1
value: 90.90089555573083
verified: no, type: f1_weighted
value: 90.90089555573083
verified: no, type: main_score
value: 90.9104
verified: no
task: type: Classification, dataset: config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics: type: accuracy
value: 90.71363429092567
verified: no, type: f1
value: 90.48838884632374
verified: no, type: f1_weighted
value: 90.6757419789302
verified: no, type: main_score
value: 90.71363429092567
verified: no
task: type: Classification, dataset: config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: validation
type: mteb/mtop_domain
metrics: type: accuracy
value: 90.5771812080537
verified: no, type: f1
value: 90.75440480842857
verified: no, type: f1_weighted
value: 90.52002736015308
verified: no, type: main_score
value: 90.5771812080537
verified: no
task: type: Classification, dataset: config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics: type: accuracy
value: 63.6388508891929
verified: no, type: f1
value: 46.797425199843055
verified: no, type: f1_weighted
value: 66.06923770534857
verified: no, type: main_score
value: 63.6388508891929
verified: no
task: type: Classification, dataset: config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: validation
type: mteb/mtop_intent
metrics: type: accuracy
value: 64.40715883668904
verified: no, type: f1
value: 46.16190436869664
verified: no, type: f1_weighted
value: 67.35202429204169
verified: no, type: main_score
value: 64.40715883668904
verified: no
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
verified: no, type: f1
value: 68.79212819626133
verified: no, type: f1_weighted
value: 68.69206463617618
verified: no, type: main_score
value: 69.59314055144587
verified: no
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
verified: no, type: f1
value: 67.52854688868585
verified: no, type: f1_weighted
value: 68.43317662845128
verified: no, type: main_score
value: 69.59173635022135
verified: no
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
verified: no, type: f1
value: 73.98844357082736
verified: no, type: f1_weighted
value: 73.60582907171401
verified: no, type: main_score
value: 73.7794216543376
verified: no
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
verified: no, type: f1
value: 73.76511807299376
verified: no, type: f1_weighted
value: 73.78920853484385
verified: no, type: main_score
value: 73.98425971470732
verified: no
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
verified: no, type: ap
value: 12.280449516326373
verified: no, type: ap_weighted
value: 12.280449516326373
verified: no, type: f1
value: 49.874354210101345
verified: no, type: f1_weighted
value: 71.91204958735288
verified: no, type: main_score
value: 64.1259765625
verified: no
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
verified: no, type: f1
value: 63.33879583206008
verified: no, type: f1_weighted
value: 62.27745749800532
verified: no, type: main_score
value: 63.17770232031692
verified: no
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 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?
Answer: Let $b$ represent the number of boys on the chess team and $g$ represent the number of girls.
We are given that $b + g = 26$ and $b + \frac{1}{2}g = 16$.
Multiplying the second equation by 2, we get $2b + g = 32$.
Subtracting the first equation from the second equation gives $b = 6$.
Substituting $b = 6$ into the first equation gives $6 + g = 26$, so $g = 20$.
Therefore, there are $\boxed{20}$ girls on the chess team.
The answer is: 20

Question: Eustace is twice as old as Milford. In 3 years, he will be 39. How old will Milford be?
Answer: If Eustace will be 39 in 3 years, that means he is currently 39 - 3 = 36 years old.
Since Eustace is twice as old as Milford, that means Milford is 36 / 2 = 18 years old.
In 3 years, Milford will be 18 + 3 = 21 years old.
#### 21
The answer is: 21

Question: Convert $10101_3$ to a base 10 integer.
Answer:
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.
In this case, we have $1\cdot3^4 + 0\cdot3^3 + 1\cdot3^2 + 0\cdot3^1 + 1\cdot3^0 = 58 + 9 + 1 = \boxed{80}$.
The 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.
In this case, we have $1\cdot3^4 + 0\cdot3^3 + 1\cdot3^2 + 0\cdot3^1 + 1\cdot3^0 = 81 + 9 + 1 = \boxed{91}$.
The answer is: 91, source_sentence: Question: Given the operation $x@y = xy - 2x$, what is the value of $(7@4) - (4@7)$?
Answer: We can substitute the given operation into the expression to get $(7@4) - (4@7) = (7 \cdot 4 - 2 \cdot 7) - (4 \cdot 7 - 2 \cdot 4)$.
Simplifying, we have $28 - 14 - 28 + 8 = \boxed{-6}$.
The answer is: -6

Question: 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?
Answer: To solve this problem, we need to determine the value of x, which represents the cost of each pair of shorts.
Let's break down the information given:
Number of pairs of shorts bought: 5
Cost per pair of shorts: x
Number of pairs of shoes bought: 2
Cost per pair of shoes: $10
Number of tops bought: 4
Cost per top: $5
Total cost of the purchase: $75
We can set up the equation as follows:
(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
(5 * x) + (2 * $10) + (4 * $5) = $75
Let's simplify and solve for x:
5x + 20 + 20 = $75
5x + 40 = $75
To isolate x, we subtract 40 from both sides of the equation:
5x + 40 - 40 = $75 - 40
5x = $35
To solve for x, we divide both sides of the equation by 5:
x = $35 / 5
x = $7
The value of x is $7.
#### 7
The answer is: 7

Question: Calculate the area of the triangle formed by the points (0, 0), (5, 1), and (2, 4).
Answer: We can use the Shoelace Formula to find the area of the triangle.
The 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
\[A = \frac{1}{2} |x_1 y_2 + x_2 y_3 + x_3 y_1 - x_1 y_3 - x_2 y_1 - x_3 y_2|.\]
Plugging in the coordinates $(0, 0),$ $(5, 1),$ and $(2, 4),$ we get
\[A = \frac{1}{2} |0\cdot 1 + 5 \cdot 4 + 2 \cdot 0 - 0 \cdot 4 - 5 \cdot 0 - 2 \cdot 1| = \frac{1}{2} \cdot 18 = \boxed{9}.\]
The answer is: 9

Question: 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?
If we know the answer to the above question is 6, what is the value of unknown variable x?
Answer: Mary wants to drink 1.5 liters of water per day, which is equal to 1500 mL.
Mary's glasses hold x mL of water.
To 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.
We are given that Mary should drink 6 glasses of water per day, so we can write: 1500 / x = 6.
Solving for x, we get: x = 250.
The value of x is 250.
#### 250
The answer is: 250

Question: 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?
Answer:
sentences:  In total, there are 4 flats x 8 petunias/flat = 32 petunias.
So, the petunias need 32 petunias x 8 ounces/petunia = 256 ounces of fertilizer.
There are 3 flats x 6 roses/flat = 18 roses in total.
So, the roses need 18 roses x 3 ounces/rose = 54 ounces of fertilizer.
And the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 4 ounces of fertilizer.
Therefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 314 ounces of fertilizer.
#### 314
The answer is: 314,  In total, there are 4 flats x 8 petunias/flat = 59 petunias.
So, the petunias need 32 petunias x 8 ounces/petunia = 874 ounces of fertilizer.
There are 3 flats x 6 roses/flat = 99 roses in total.
So, the roses need 18 roses x 3 ounces/rose = 40 ounces of fertilizer.
And the Venus flytraps need 2 flytraps x 2 ounces/flytrap = 8 ounces of fertilizer.
Therefore, Seymour needs a total of 256 ounces + 54 ounces + 4 ounces = 950 ounces of fertilizer.
#### 314
The 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.
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Created: 2024-07-14 05:38:54