SentenceTransformer based on microsoft/deberta-v3-small
This is a sentence-transformers model finetuned from microsoft/deberta-v3-small on the nli-pairs, sts-label, vitaminc-pairs, qnli-contrastive, scitail-pairs-qa, scitail-pairs-pos, xsum-pairs, compression-pairs, sciq_pairs, qasc_pairs, openbookqa_pairs, msmarco_pairs, nq_pairs, trivia_pairs, quora_pairs and gooaq_pairs 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: microsoft/deberta-v3-small
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 tokens
- Similarity Function: Cosine Similarity
- Training Datasets:
- Language: en
Model Sources
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DebertaV2Model
(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
model = SentenceTransformer("bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp")
sentences = [
'What language did Tesla study while in school?',
'Tesla was the fourth of five children.',
'Rev. Jimmy Creech was defrocked after a highly publicized church trial in 1999 on account of his participation in same-sex union ceremonies.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
Evaluation
Metrics
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.2521 |
spearman_cosine |
0.2589 |
pearson_manhattan |
0.3044 |
spearman_manhattan |
0.3014 |
pearson_euclidean |
0.2598 |
spearman_euclidean |
0.2608 |
pearson_dot |
0.0812 |
spearman_dot |
0.0754 |
pearson_max |
0.3044 |
spearman_max |
0.3014 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.2521 |
spearman_cosine |
0.2589 |
pearson_manhattan |
0.3044 |
spearman_manhattan |
0.3014 |
pearson_euclidean |
0.2598 |
spearman_euclidean |
0.2608 |
pearson_dot |
0.0812 |
spearman_dot |
0.0754 |
pearson_max |
0.3044 |
spearman_max |
0.3014 |
Semantic Similarity
Metric |
Value |
pearson_cosine |
0.7933 |
spearman_cosine |
0.7975 |
pearson_manhattan |
0.7981 |
spearman_manhattan |
0.7881 |
pearson_euclidean |
0.7953 |
spearman_euclidean |
0.7855 |
pearson_dot |
0.7743 |
spearman_dot |
0.7647 |
pearson_max |
0.7981 |
spearman_max |
0.7975 |
Training Details
Training Datasets
nli-pairs
- Dataset: nli-pairs at d482672
- Size: 50,000 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 5 tokens
- mean: 16.62 tokens
- max: 62 tokens
|
- min: 4 tokens
- mean: 9.46 tokens
- max: 29 tokens
|
- Samples:
sentence1 |
sentence2 |
A person on a horse jumps over a broken down airplane. |
A person is outdoors, on a horse. |
Children smiling and waving at camera |
There are children present |
A boy is jumping on skateboard in the middle of a red bridge. |
The boy does a skateboarding trick. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
sts-label
vitaminc-pairs
- Dataset: vitaminc-pairs at be6febb
- Size: 24,996 training samples
- Columns:
label
, sentence1
, and sentence2
- Approximate statistics based on the first 1000 samples:
|
label |
sentence1 |
sentence2 |
type |
int |
string |
string |
details |
|
- min: 6 tokens
- mean: 16.65 tokens
- max: 50 tokens
|
- min: 8 tokens
- mean: 36.9 tokens
- max: 161 tokens
|
- Samples:
label |
sentence1 |
sentence2 |
1 |
Linkin Park sold more than 30 million singles and 130 million records worldwide . |
Linkin Park has sold over 100 million albums and 31 million singles worldwide , making a total of over 131 million records sold worldwide with 32,000,000 albums and 33,000,000 singles sold in the US as of June 2017 . |
1 |
Anibal Sanchez has played for the Atlanta Braves . |
He has played in Major League Baseball ( MLB ) for the Florida/Miami Marlins , Detroit Tigers and Atlanta Braves . |
1 |
Frankenweenie has under 37 reviews on Metacritic , and a score above 74 . |
Metacritic , which assigns a weighted average score out of 100 to reviews from mainstream critics , gives the film a score of 75 based on 35 reviews . |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
qnli-contrastive
- Dataset: qnli-contrastive at bcdcba7
- Size: 50,000 training samples
- Columns:
sentence1
, sentence2
, and label
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
label |
type |
string |
string |
int |
details |
- min: 6 tokens
- mean: 13.54 tokens
- max: 38 tokens
|
- min: 9 tokens
- mean: 35.96 tokens
- max: 136 tokens
|
|
- Samples:
sentence1 |
sentence2 |
label |
By what means did the British govern Tuvalu? |
The Ellice Islands were administered as British protectorate by a Resident Commissioner from 1892 to 1916 as part of the British Western Pacific Territories (BWPT), and then as part of the Gilbert and Ellice Islands colony from 1916 to 1974. |
0 |
Who is the current head of BBC Television? |
As a division within the BBC, Television was formerly known as BBC Vision for a few years in the early 21st century, until its name reverted to Television in 2013. |
0 |
What was the PLDA formerly known as? |
The Professional Lighting Designers Association (PLDA), formerly known as ELDA is an organisation focusing on the promotion of the profession of Architectural Lighting Design. |
0 |
- Loss:
OnlineContrastiveLoss
scitail-pairs-qa
- Dataset: scitail-pairs-qa at 0cc4353
- Size: 14,987 training samples
- Columns:
sentence2
and sentence1
- Approximate statistics based on the first 1000 samples:
|
sentence2 |
sentence1 |
type |
string |
string |
details |
- min: 7 tokens
- mean: 15.63 tokens
- max: 41 tokens
|
- min: 6 tokens
- mean: 14.73 tokens
- max: 41 tokens
|
- Samples:
sentence2 |
sentence1 |
People stopped adding lead to gasoline because of environmental pollution. |
Why did people stop adding lead to gasoline? |
The pleura that surrounds the lungs consists of two layers. |
The pleura that surrounds the lungs consists of how many layers? |
Thermal energy constitutes the total kinetic energy of all the atoms that make up an object. |
What kind of energy constitutes the total kinetic energy of all the atoms that make up an object? |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
scitail-pairs-pos
- Dataset: scitail-pairs-pos at 0cc4353
- Size: 8,600 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 8 tokens
- mean: 24.02 tokens
- max: 71 tokens
|
- min: 7 tokens
- mean: 15.66 tokens
- max: 39 tokens
|
- Samples:
sentence1 |
sentence2 |
TELEPHONE (818) 354-5011 PHOTO CAPTION P-23254 C & BW S-1-62 Dec. 4, 1980 Voyager 1 looked back at Saturn on Nov. 16, 1980, four days after the spacecraft flew past the planet, to observe the appearance of Saturn and its rings from this unique perspective. |
The voyager 1 spacecraft visited saturn in 1980. |
atoms may share one pair of electrons (single bonds), two pairs (double bonds), or three pairs (triple bonds). |
In a carbon triple bond, three pairs of electrons are shared. |
One gram of protein contains four calories. |
One gram of proteins provides four calories of energy. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
xsum-pairs
compression-pairs
sciq_pairs
- Dataset: sciq_pairs at 2c94ad3
- Size: 11,679 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 7 tokens
- mean: 17.26 tokens
- max: 60 tokens
|
- min: 2 tokens
- mean: 84.37 tokens
- max: 512 tokens
|
- Samples:
sentence1 |
sentence2 |
What type of organism is commonly used in preparation of foods such as cheese and yogurt? |
Mesophiles grow best in moderate temperature, typically between 25°C and 40°C (77°F and 104°F). Mesophiles are often found living in or on the bodies of humans or other animals. The optimal growth temperature of many pathogenic mesophiles is 37°C (98°F), the normal human body temperature. Mesophilic organisms have important uses in food preparation, including cheese, yogurt, beer and wine. |
What phenomenon makes global winds blow northeast to southwest or the reverse in the northern hemisphere and northwest to southeast or the reverse in the southern hemisphere? |
Without Coriolis Effect the global winds would blow north to south or south to north. But Coriolis makes them blow northeast to southwest or the reverse in the Northern Hemisphere. The winds blow northwest to southeast or the reverse in the southern hemisphere. |
Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always what? |
Summary Changes of state are examples of phase changes, or phase transitions. All phase changes are accompanied by changes in the energy of a system. Changes from a more-ordered state to a less-ordered state (such as a liquid to a gas) areendothermic. Changes from a less-ordered state to a more-ordered state (such as a liquid to a solid) are always exothermic. The conversion of a solid to a liquid is called fusion (or melting). The energy required to melt 1 mol of a substance is its enthalpy of fusion (ΔHfus). The energy change required to vaporize 1 mol of a substance is the enthalpy of vaporization (ΔHvap). The direct conversion of a solid to a gas is sublimation. The amount of energy needed to sublime 1 mol of a substance is its enthalpy of sublimation (ΔHsub) and is the sum of the enthalpies of fusion and vaporization. Plots of the temperature of a substance versus heat added or versus heating time at a constant rate of heating are calledheating curves. Heating curves relate temperature changes to phase transitions. A superheated liquid, a liquid at a temperature and pressure at which it should be a gas, is not stable. A cooling curve is not exactly the reverse of the heating curve because many liquids do not freeze at the expected temperature. Instead, they form a supercooled liquid, a metastable liquid phase that exists below the normal melting point. Supercooled liquids usually crystallize on standing, or adding a seed crystal of the same or another substance can induce crystallization. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
qasc_pairs
- Dataset: qasc_pairs at a34ba20
- Size: 8,134 training samples
- Columns:
id
, sentence1
, and sentence2
- Approximate statistics based on the first 1000 samples:
|
id |
sentence1 |
sentence2 |
type |
string |
string |
string |
details |
- min: 17 tokens
- mean: 21.35 tokens
- max: 27 tokens
|
- min: 5 tokens
- mean: 11.47 tokens
- max: 25 tokens
|
- min: 14 tokens
- mean: 35.55 tokens
- max: 66 tokens
|
- Samples:
id |
sentence1 |
sentence2 |
3E7TUJ2EGCLQNOV1WEAJ2NN9ROPD9K |
What type of water formation is formed by clouds? |
beads of water are formed by water vapor condensing. Clouds are made of water vapor.. Beads of water can be formed by clouds. |
3LS2AMNW5FPNJK3C3PZLZCPX562OQO |
Where do beads of water come from? |
beads of water are formed by water vapor condensing. Condensation is the change of water vapor to a liquid.. Vapor turning into a liquid leaves behind beads of water |
3TMFV4NEP8DPIPCI8H9VUFHJG8V8W3 |
What forms beads of water? |
beads of water are formed by water vapor condensing. An example of water vapor is steam.. Steam forms beads of water. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
openbookqa_pairs
- Dataset: openbookqa_pairs at 388097e
- Size: 2,740 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 3 tokens
- mean: 13.83 tokens
- max: 78 tokens
|
- min: 4 tokens
- mean: 11.37 tokens
- max: 30 tokens
|
- Samples:
sentence1 |
sentence2 |
The sun is responsible for |
the sun is the source of energy for physical cycles on Earth |
When food is reduced in the stomach |
digestion is when stomach acid breaks down food |
Stars are |
a star is made of gases |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
msmarco_pairs
- Dataset: msmarco_pairs
- Size: 50,000 training samples
- Columns:
query
, positive
, negative
, and label
- Approximate statistics based on the first 1000 samples:
|
query |
positive |
negative |
label |
type |
string |
string |
string |
float |
details |
- min: 4 tokens
- mean: 8.61 tokens
- max: 27 tokens
|
- min: 18 tokens
- mean: 75.09 tokens
- max: 206 tokens
|
- min: 15 tokens
- mean: 72.59 tokens
- max: 216 tokens
|
- min: -0.5
- mean: 0.04
- max: 0.6
|
- Samples:
query |
positive |
negative |
label |
what are the liberal arts? |
liberal arts. 1. the academic course of instruction at a college intended to provide general knowledge and comprising the arts, humanities, natural sciences, and social sciences, as opposed to professional or technical subjects. |
The New York State Education Department requires 60 Liberal Arts credits in a Bachelor of Science program and 90 Liberal Arts credits in a Bachelor of Arts program. In the list of course descriptions, courses which are liberal arts for all students are identified by (Liberal Arts) after the course number. |
0.12154221534729004 |
what is the mechanism of action of fibrinolytic or thrombolytic drugs? |
Baillière's Clinical Haematology. 6 Mechanism of action of the thrombolytic agents. 6 Mechanism of action of the thrombolytic agents JEFFREY I. WEITZ Fibrin formed during the haemostatic, inflammatory or tissue repair process serves a temporary role, and must be degraded to restore normal tissue function and structure. |
Fibrinolytic drug. Fibrinolytic drug, also called thrombolytic drug, any agent that is capable of stimulating the dissolution of a blood clot (thrombus). Fibrinolytic drugs work by activating the so-called fibrinolytic pathway. |
-0.05174225568771362 |
what is normal plat count |
78 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL).The average platelet count is 237,000 per mcL in men and 266,000 per mcL in women.8 Followers. A. Platelets are the tiny blood cells that help stop bleeding by binding together to form a clump or plug at sites of injury inside blood vessels. A normal platelet count is between 150,000 and 450,000 platelets per microliter (one-millionth of a liter, abbreviated mcL). |
Your blood test results should be written in your maternity notes. Your platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range.If your platelet count is low, the blood test should be done again.This will keep track of whether or not your count is dropping.our platelet count will look something like Plat. 160x10.9/L, which means you have a platelet count of 160, which is in the normal range. If your platelet count is low, the blood test should be done again. This will keep track of whether or not your count is dropping. |
-0.037523627281188965 |
- Loss:
MarginMSELoss
nq_pairs
- Dataset: nq_pairs at f9e894e
- Size: 50,000 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 10 tokens
- mean: 11.77 tokens
- max: 21 tokens
|
- min: 16 tokens
- mean: 131.57 tokens
- max: 512 tokens
|
- Samples:
sentence1 |
sentence2 |
when did richmond last play in a preliminary final |
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. |
who sang what in the world's come over you |
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. |
who produces the most wool in the world |
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. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
trivia_pairs
- Dataset: trivia_pairs at a7c36e3
- Size: 50,000 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 8 tokens
- mean: 15.16 tokens
- max: 48 tokens
|
- min: 19 tokens
- mean: 456.87 tokens
- max: 512 tokens
|
- Samples:
sentence1 |
sentence2 |
Which American-born Sinclair won the Nobel Prize for Literature in 1930? |
The Nobel Prize in Literature 1930 The Nobel Prize in Literature 1930 Sinclair Lewis The Nobel Prize in Literature 1930 Sinclair Lewis Prize share: 1/1 The Nobel Prize in Literature 1930 was awarded to Sinclair Lewis "for his vigorous and graphic art of description and his ability to create, with wit and humour, new types of characters". Photos: Copyright © The Nobel Foundation Share this: To cite this page MLA style: "The Nobel Prize in Literature 1930". Nobelprize.org. Nobel Media AB 2014. Web. 18 Jan 2017. http://www.nobelprize.org/nobel_prizes/literature/laureates/1930/ |
Where in England was Dame Judi Dench born? |
Judi Dench - IMDb IMDb Actress |
In which decade did Billboard magazine first publish and American hit chart? |
The US Billboard song chart The US Billboard song chart Search this site with Google Song chart US Billboard The Billboard magazine has published various music charts starting (with sheet music) in 1894, the first "Music Hit Parade" was published in 1936 , the first "Music Popularity Chart" was calculated in 1940 . These charts became less irregular until the weekly "Hot 100" was started in 1958 . The current chart combines sales, airplay and downloads. A music collector that calls himself Bullfrog has been consolidating the complete chart from 1894 to the present day. he has published this information in a comprehenive spreadsheet (which can be obtained at bullfrogspond.com/ ). The Bullfrog data assigns each song a unique identifier, something like "1968_076" (which just happens to be the Bee Gees song "I've Gotta Get A Message To You"). This "Whitburn Number" is provided to match with the books of Joel Whitburn and consists of the year and a ranking within the year. A song that first entered the charts in December and has a long run is listed the following year. This numbering scheme means that songs which are still in the charts cannot be assigned a final id, because their ranking might change. So the definitive listing for a year cannot be final until about April. In our listing we only use songs with finalised IDs, this means that every year we have to wait until last year's entries are finalised before using them. (Source bullfrogspond.com/ , the original version used here was 20090808 with extra data from: the 2009 data from 20091219 the 2010 data from 20110305 the 2011 data from 20120929 the 2012 data from 20130330 the 2013 data from 20150328 The 20150328 data was the last one produced before the Billboard company forced the data to be withdrawn. As far as we know there are no more recent data sets available. This pattern of obtaining the data for a particular year in the middle of the following one comes from the way that the Bullfrog project generates the identifier for a song (what they call the "Prefix" in the spreadsheet). Recent entries are identified with keys like "2015-008" while older ones have keys like "2013_177". In the second case the underscore is significant, it indicates that this was the 177th biggest song released in 2013. Now, of course, during the year no one knows where a particular song will rank, so the underscore names can't be assigned until every song from a particular year has dropped out of the charts, so recent records are temporarily assigned a name with a dash. In about May of the following year the rankings are calculated and the final identifiers are assigned. That is why we at the Turret can only grab this data retrospectively. Attributes The original spreadsheet has a number of attributes, we have limited our attention to just a few of them: 134 9 The songs with the most entries on the chart were White Christmas (with 33 versions and a total of 110 weeks) and Stardust (with 19 and a total of 106 weeks). position The peak position that songs reached in the charts should show an smooth curve from number one down to the lowest position. This chart has more songs in the lower peak positions than one would expect. Before 1991 the profile of peak positions was exactly as you would expect, that year Billboard introduced the concept of "Recurrent" tracks, that is they removed any track from the chart which had spent more than twenty weeks in the chart and had fallen to the lower positions. weeks The effect of the "Recurrent" process, by which tracks are removed if they have spent at least twenty weeks in the chart and have fallen to the lower reaches, can clearly be seen in the strange spike in this attribute. This "adjustment" was intended to promote newer songs and ensure the chart does not become "stale". In fact since it was introduced in 1991 the length of long chart runs has increased, this might reflect the more conscious efforts of record companies to "game" the charts by controlling release times and promotions, or it coul |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
quora_pairs
gooaq_pairs
- Dataset: gooaq_pairs at b089f72
- Size: 50,000 training samples
- Columns:
sentence1
and sentence2
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
type |
string |
string |
details |
- min: 8 tokens
- mean: 11.6 tokens
- max: 21 tokens
|
- min: 13 tokens
- mean: 57.74 tokens
- max: 127 tokens
|
- Samples:
sentence1 |
sentence2 |
is toprol xl the same as metoprolol? |
Metoprolol succinate is also known by the brand name Toprol XL. It is the extended-release form of metoprolol. Metoprolol succinate is approved to treat high blood pressure, chronic chest pain, and congestive heart failure. |
are you experienced cd steve hoffman? |
The Are You Experienced album was apparently mastered from the original stereo UK master tapes (according to Steve Hoffman - one of the very few who has heard both the master tapes and the CDs produced over the years). ... The CD booklets were a little sparse, but at least they stayed true to the album's original design. |
how are babushka dolls made? |
Matryoshka dolls are made of wood from lime, balsa, alder, aspen, and birch trees; lime is probably the most common wood type. ... After cutting, the trees are stripped of most of their bark, although a few inner rings of bark are left to bind the wood and keep it from splitting. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
Evaluation Datasets
nli-pairs
- Dataset: nli-pairs at d482672
- Size: 6,808 evaluation samples
- Columns:
anchor
and positive
- Approximate statistics based on the first 1000 samples:
|
anchor |
positive |
type |
string |
string |
details |
- min: 5 tokens
- mean: 17.64 tokens
- max: 63 tokens
|
- min: 4 tokens
- mean: 9.67 tokens
- max: 29 tokens
|
- Samples:
anchor |
positive |
Two women are embracing while holding to go packages. |
Two woman are holding packages. |
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. |
Two kids in numbered jerseys wash their hands. |
A man selling donuts to a customer during a world exhibition event held in the city of Angeles |
A man selling donuts to a customer. |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
scitail-pairs-pos
- Dataset: scitail-pairs-pos at 0cc4353
- Size: 1,304 evaluation samples
- Columns:
sentence1
, sentence2
, and label
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
label |
type |
string |
string |
int |
details |
- min: 5 tokens
- mean: 22.52 tokens
- max: 67 tokens
|
- min: 8 tokens
- mean: 15.34 tokens
- max: 36 tokens
|
|
- Samples:
sentence1 |
sentence2 |
label |
An introduction to atoms and elements, compounds, atomic structure and bonding, the molecule and chemical reactions. |
Replace another in a molecule happens to atoms during a substitution reaction. |
0 |
Wavelength The distance between two consecutive points on a sinusoidal wave that are in phase; |
Wavelength is the distance between two corresponding points of adjacent waves called. |
1 |
humans normally have 23 pairs of chromosomes. |
Humans typically have 23 pairs pairs of chromosomes. |
1 |
- Loss:
GISTEmbedLoss
with these parameters:{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.05}
qnli-contrastive
- Dataset: qnli-contrastive at bcdcba7
- Size: 5,463 evaluation samples
- Columns:
sentence1
, sentence2
, and label
- Approximate statistics based on the first 1000 samples:
|
sentence1 |
sentence2 |
label |
type |
string |
string |
int |
details |
- min: 6 tokens
- mean: 14.13 tokens
- max: 36 tokens
|
- min: 4 tokens
- mean: 36.58 tokens
- max: 225 tokens
|
|
- Samples:
sentence1 |
sentence2 |
label |
What came into force after the new constitution was herald? |
As of that day, the new constitution heralding the Second Republic came into force. |
0 |
What is the first major city in the stream of the Rhine? |
The most important tributaries in this area are the Ill below of Strasbourg, the Neckar in Mannheim and the Main across from Mainz. |
0 |
What is the minimum required if you want to teach in Canada? |
In most provinces a second Bachelor's Degree such as a Bachelor of Education is required to become a qualified teacher. |
0 |
- Loss:
OnlineContrastiveLoss
Training Hyperparameters
Non-Default Hyperparameters
eval_strategy
: steps
per_device_train_batch_size
: 30
per_device_eval_batch_size
: 16
learning_rate
: 1e-05
weight_decay
: 5e-06
num_train_epochs
: 2
lr_scheduler_type
: cosine
warmup_ratio
: 0.5
save_safetensors
: False
fp16
: True
push_to_hub
: True
hub_model_id
: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
hub_strategy
: checkpoint
batch_sampler
: no_duplicates
All Hyperparameters
Click to expand
overwrite_output_dir
: False
do_predict
: False
eval_strategy
: steps
prediction_loss_only
: True
per_device_train_batch_size
: 30
per_device_eval_batch_size
: 16
per_gpu_train_batch_size
: None
per_gpu_eval_batch_size
: None
gradient_accumulation_steps
: 1
eval_accumulation_steps
: None
learning_rate
: 1e-05
weight_decay
: 5e-06
adam_beta1
: 0.9
adam_beta2
: 0.999
adam_epsilon
: 1e-08
max_grad_norm
: 1.0
num_train_epochs
: 2
max_steps
: -1
lr_scheduler_type
: cosine
lr_scheduler_kwargs
: {}
warmup_ratio
: 0.5
warmup_steps
: 0
log_level
: passive
log_level_replica
: warning
log_on_each_node
: True
logging_nan_inf_filter
: True
save_safetensors
: False
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
: False
fp16
: True
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
: True
resume_from_checkpoint
: None
hub_model_id
: bobox/DeBERTaV3-small-GeneralSentenceTransformer-v2-checkpoints-tmp
hub_strategy
: checkpoint
hub_private_repo
: False
hub_always_push
: False
gradient_checkpointing
: False
gradient_checkpointing_kwargs
: None
include_inputs_for_metrics
: False
eval_do_concat_batches
: True
fp16_backend
: auto
push_to_hub_model_id
: None
push_to_hub_organization
: None
mp_parameters
:
auto_find_batch_size
: False
full_determinism
: False
torchdynamo
: None
ray_scope
: last
ddp_timeout
: 1800
torch_compile
: False
torch_compile_backend
: None
torch_compile_mode
: None
dispatch_batches
: None
split_batches
: None
include_tokens_per_second
: False
include_num_input_tokens_seen
: False
neftune_noise_alpha
: None
optim_target_modules
: None
batch_eval_metrics
: False
batch_sampler
: no_duplicates
multi_dataset_batch_sampler
: proportional
Training Logs
Epoch |
Step |
Training Loss |
scitail-pairs-pos loss |
nli-pairs loss |
qnli-contrastive loss |
sts-test_spearman_cosine |
0 |
0 |
- |
3.4975 |
4.3370 |
4.4702 |
0.2589 |
0.1 |
1757 |
3.8346 |
2.3231 |
2.8535 |
3.0973 |
- |
0.2 |
3514 |
1.8532 |
0.9755 |
1.3508 |
2.0603 |
- |
0.3 |
5271 |
1.2185 |
0.7407 |
0.9381 |
1.2534 |
- |
0.4 |
7028 |
0.9584 |
0.6616 |
0.7495 |
0.5140 |
- |
0.5 |
8785 |
0.8157 |
0.6057 |
0.6550 |
0.3295 |
- |
0.6 |
10542 |
0.6698 |
0.5821 |
0.5809 |
0.2423 |
- |
0.7 |
12299 |
0.6497 |
0.5040 |
0.5178 |
0.2409 |
- |
0.8 |
14056 |
0.5737 |
0.4942 |
0.5019 |
0.1500 |
- |
0.9 |
15813 |
0.5896 |
0.4757 |
0.4804 |
0.1465 |
- |
1.0 |
17570 |
0.5174 |
0.5253 |
0.4587 |
0.0534 |
- |
1.1 |
19327 |
0.5059 |
0.5493 |
0.4587 |
0.0278 |
- |
1.2 |
21084 |
0.4654 |
0.4850 |
0.4415 |
0.0517 |
- |
1.3 |
22841 |
0.4224 |
0.4292 |
0.3957 |
0.0938 |
- |
1.4 |
24598 |
0.4125 |
0.4624 |
0.3794 |
0.0839 |
- |
1.5 |
26355 |
0.4072 |
0.4481 |
0.3878 |
0.0681 |
- |
1.6 |
28112 |
0.3572 |
0.4953 |
0.3716 |
0.0674 |
- |
1.7 |
29869 |
0.371 |
0.4767 |
0.3622 |
0.0600 |
- |
1.8 |
31626 |
0.3332 |
0.4659 |
0.3600 |
0.0561 |
- |
1.9 |
33383 |
0.3695 |
0.4604 |
0.3567 |
0.0614 |
- |
2.0 |
35140 |
0.3315 |
0.4712 |
0.3597 |
0.0540 |
0.7975 |
Framework Versions
- Python: 3.10.13
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2
- Accelerate: 0.30.1
- Datasets: 2.19.2
- Tokenizers: 0.19.1
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",
}
GISTEmbedLoss
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
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},
}
MarginMSELoss
@misc{hofstätter2021improving,
title={Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation},
author={Sebastian Hofstätter and Sophia Althammer and Michael Schröder and Mete Sertkan and Allan Hanbury},
year={2021},
eprint={2010.02666},
archivePrefix={arXiv},
primaryClass={cs.IR}
}