Hugging Face's logo

bwang0911
/
bge-int8

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:60315
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Model card Files
xet
Community
bge-int8 / README.md
bwang0911's picture
bwang0911
Add new SentenceTransformer model
55b98ca verified
preview code
|
raw
history blame contribute delete
120 kB
 ---
language:
- en
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:60315
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: Which university did Cheryl Miller attend?
sentences:
- Cheryl Miller male or female, to be named an All-American by "Parade" magazine
four times. Averaging 32.8 points and 15.0 rebounds a game, Miller was Street
& Smith's national High School Player of the Year in both 1981 and 1982. In her
senior year she scored 105 points in a game against Norte Vista High School. She
set California state records for points scored in a single season (1156), and
points scored in a high school career (3405). At the University of Southern California
(USC), the 6 ft. 2 in. (1.87 m) Miller played the forward position. She was a
four-year letter
- 1979 Formula One season 1979 Formula One season The 1979 Formula One season was
the 33rd season of FIA Formula One motor racing. It featured the 1979 World Championship
of F1 Drivers and the 1979 International Cup for F1 Constructors which were contested
concurrently over a fifteen-round series which commenced on 21 January 1979, and
ended on 7 October. The season also included three non-championship Formula One
races. Jody Scheckter of Scuderia Ferrari won the 1979 World Championship of F1
Drivers while Scuderia Ferrari won 1979 International Cup for F1 Constructors.
Gilles Villeneuve made it a 1–2 for Ferrari in the championship, concluding a
- Cheryl Miller April 30, 2014, she was named the women's basketball coach at Langston
University by athletic director Mike Garrett. On May 26, 2016, she was named the
women's basketball coach at California State Los Angeles by athletic director
Mike Garrett. Cheryl Miller serves as a sideline reporter for the "NBA on TNT"’s
Thursday night doubleheader coverage for TNT Sports. She also made appearances
on NBA TV during the 2008-09 NBA season as a reporter and analyst. Miller joined
Turner Sports in September 1995 as an analyst and reporter for the "NBA on TBS"
and TNT. She did make occasional appearances as
- source_sentence: For what did Georgie O'Keefe become famous?
sentences:
- The Day the Earth Stood Still The Day the Earth Stood Still The Day the Earth
Stood Still (a.k.a. Farewell to the Master and Journey to the World) is a 1951
American black-and-white science fiction film from 20th Century Fox, produced
by Julian Blaustein and directed by Robert Wise. The film stars Michael Rennie,
Patricia Neal, Billy Gray, Hugh Marlowe, and Sam Jaffe. The screenplay was written
by Edmund H. North, based on the 1940 science fiction short story "Farewell to
the Master" by Harry Bates, and the film score was composed by Bernard Herrmann.
The storyline for "The Day the Earth Stood Still" involves a
- Brian Keefe Bryant University in Smithfield, R.I. for four seasons (2001-05).
In his final season, he helped the Bryant Bulldogs earn a trip to the Division
II Championship in 2005. NBA Career Keefe started his career in professional basketball
at the San Antonio Spurs where he served as video coordinator under head coach
Gregg Popovich, and won a ring as part of the Spur’s 2007 championship in his
second season. Keefe was selected by former Spurs assistant GM Sam Presti and
former Spurs assistant coach PJ Carlesimo to join them in laying the groundwork
for what would become the Oklahoma City Thunder.
- What Have We Become? playlist in April 2014. The cover painting is by David Storey.
"What Have We Become?" received generally positive reviews from music critics.
The album received an average score of 76/100 from 14 reviews on Metacritic, indicating
"generally favorable reviews". In his review for AllMusic, David Jeffries wrote
that, "Anyone who enjoys their pop with extra wry and some sobering awareness
should love What Have We Become?, but it's the Beautiful South faithful who will
rightfully gush over the release, as these antiheroes have lost none of their
touch or fatalistic flair." What Have We Become? What Have We Become? is
- source_sentence: How much time did Jonah spend in the belly of the whale?
sentences:
- Book of Jonah all their efforts fail and they are eventually forced to throw Jonah
overboard. As a result, the storm calms and the sailors then offer sacrifices
to God. Jonah is miraculously saved by being swallowed by a large fish, in whose
belly he spends three days and three nights. While in the great fish, Jonah prays
to God in his affliction and commits to thanksgiving and to paying what he has
vowed. God then commands the fish to vomit Jonah out. God again commands Jonah
to travel to Nineveh and prophesy to its inhabitants. This time he goes and enters
the
- Jonah Who Lived in the Whale Jonah Who Lived in the Whale Jonah Who Lived in the
Whale (), in the United States released as (Look to the Sky) is a 1993 Italian-French
drama film directed by Roberto Faenza, based on the autobiographical novel by
the writer Jona Oberski entitled "Childhood", focused on the drama of the Holocaust.
It was entered into the 18th Moscow International Film Festival, where it won
the Prix of Ecumenical Jury. Jonah is a four-year-old Dutch boy who lives in Amsterdam
during the Second World War. After the occupation of the city by the Germans,
he was deported to the concentration
- Rain Man Rain Man Rain Man is a 1988 American comedy-drama road movie directed
by Barry Levinson and written by Barry Morrow and Ronald Bass. It tells the story
of an abrasive, selfish young wheeler-dealer Charlie Babbitt (Tom Cruise), who
discovers that his estranged father has died and bequeathed all of his multimillion-dollar
estate to his other son, Raymond (Dustin Hoffman), an autistic savant, of whose
existence Charlie was unaware. Charlie is left with only his father's car and
collection of rose bushes. In addition to the two leads, Valeria Golino stars
as Charlie's girlfriend, Susanna. Morrow created the character of Raymond
- source_sentence: In which country are Tangier and Casablanca?
sentences:
- Casablanca–Tangier high-speed rail line by a new high-speed right of way, with
construction scheduled to begin in 2020. Two electrification types are used—from
Tangier to Kenitra the new trackage was built with 25 kV at 50 Hz, while the line
from Kenitra to Casablanca retained the existing 3 kV DC catenary. The ETCS-type
signal system was installed by Ansaldo STS and Cofely Ineo. At the launch of service
in 2018, the travel time between Casablanca and Tangier was reduced from 4 hours
and 45 minutes to 2 hours and 10 minutes. The completion of dedicated high-speed
trackage into Casablanca would further reduce the end-to-end
- 'Maybellene Maybellene "Maybellene" is one of the first rock and roll songs. It
was written and recorded in 1955 by Chuck Berry, and inspired/adapted from the
Western Swing fiddle tune "Ida Red", which was recorded in 1938 by Bob Wills and
his Texas Playboys. Berry''s song tells the story of a hot rod race and a broken
romance. It was released in July 1955 as a single by Chess Records, of Chicago,
Illinois. It was Berry''s first single and his first hit. "Maybellene" is considered
one of the pioneering rock songs: "Rolling Stone" magazine wrote, "Rock & roll
guitar starts here."'
- Casablanca–Tangier high-speed rail line travel time to 1 hour and 30 minutes.
The 12 Alstom Euroduplex trainsets operating on the line are bilevel trains, each
comprised two power cars and eight passenger cars. The passenger capacity is 533
across two first-class cars, five second-class cars, and a food-service car. Casablanca–Tangier
high-speed rail line The Casablanca—Tangier high-speed rail line is a high-speed
rail line in Morocco that is the first on the African continent. The line was
inaugurated on 15 November 2018 by King Mohammed VI of Morocco following over
a decade of planning and construction by Moroccan national railway company ONCF.
It is the
- source_sentence: Where in Australia was swashbuckling Errol Flynn born?
sentences:
- 'Errol Flynn early in his career: Errol Flynn Errol Leslie Thomson Flynn (20 June
1909 – 14 October 1959) was an Australian-born American actor during the Golden
Age of Hollywood. Considered the natural successor to Douglas Fairbanks, he achieved
worldwide fame for his romantic swashbuckler roles in Hollywood films, as well
as frequent partnerships with Olivia de Havilland. He was best known for his role
as Robin Hood in "The Adventures of Robin Hood" (1938); his portrayal of the character
was named by the American Film Institute as the 18th greatest hero in American
film history. His other famous roles included the'
- Phillips Recording Phillips Recording Phillips Recording is the short name widely
used to refer to the Sam C. Phillips Recording Studio opened at 639 Madison Avenue
in Memphis, Tennessee, by Sam Phillips in 1960. Internationally regarded at that
time as a state-of-the-art facility, it was built to fill the needs of the Sun
Records recording label that the older, smaller Sun Records Studio was no longer
able to handle. This Memphis studio was originally a division of a larger corporation,
Sam Phillips Recording Service, Inc., which also briefly included under its umbrella
a Nashville studio, where famed CBS Records producer Billy Sherrill
- Errol Flynn Blood" (1935), Major Geoffrey Vickers in "The Charge of the Light
Brigade" (1936), as well as a number of Westerns, such as "Dodge City" (1939),
"Santa Fe Trail" (1940), and "San Antonio" (1945). Errol Leslie Flynn was born
on 20 June 1909 in Battery Point, a suburb of Hobart, Tasmania, Australia. His
father, Theodore Thomson Flynn, was a lecturer (1909) and later professor (1911)
of biology at the University of Tasmania. His mother was born Lily Mary Young,
but shortly after marrying Theodore at St John's Church of England, Birchgrove,
Sydney, on 23 January 1909, she changed her first name
datasets:
- sentence-transformers/trivia-qa-triplet
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.28
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.46
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.54
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.68
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.28
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.132
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.12166666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22666666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.2773333333333333
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.349
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2921247797723984
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3988253968253968
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.23009905552923093
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.6
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.84
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.52
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.496
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.4320000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06294234345262387
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.13008183594343983
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1826677141588478
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.28710629918570024
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5202322797992843
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7049126984126982
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3763292112580843
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.64
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.92
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.64
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18799999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09599999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6166666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8566666666666666
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8766666666666666
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8966666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7846547160527625
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7672222222222221
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.740638888888889
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.3
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.42
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.3
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.18
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15541269841269842
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.22260317460317464
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.26460317460317456
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.3058253968253968
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2790870219513927
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.35669047619047617
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24703484886940344
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.66
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.78
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.92
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.66
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3466666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.244
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.33
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.61
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.71
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6313501479198645
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7391587301587301
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.5492849385578099
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.38
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.74
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.38
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666669
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12000000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.38
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.74
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5328147829793286
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4702142857142857
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4805827799103662
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.5
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.56
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30000000000000004
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.3
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.244
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.01210979765940875
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.040583707862991376
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.05871448569598863
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.08206726954742757
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.26991337113740815
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.09944171039889445
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.72
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15999999999999998
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.132
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.07600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.46
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.61
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.69
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.46501655674505726
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.39988888888888885
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3974487222592024
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.86
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.96
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.96
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.86
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.4
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.25199999999999995
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.13599999999999998
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7473333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.9253333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.9420000000000001
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9933333333333334
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9203896722112936
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9073333333333332
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8924516594516595
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.48
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.24666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.204
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.132
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07366666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.15466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.21166666666666664
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.27266666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2719940457772305
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4480555555555556
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22300523301536854
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.52
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.64
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.17333333333333337
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.128
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.18
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.52
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.64
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4849234061490301
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3843809523809524
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.3930335420922445
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.56
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.66
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.68
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.84
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.56
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14400000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09599999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.54
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.64
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.66
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.84
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6789363300745337
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6334285714285713
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.6282075055376187
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.46938775510204084
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7346938775510204
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8163265306122449
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9183673469387755
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.46938775510204084
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.3945578231292517
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.38775510204081626
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.32653061224489793
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03566240843889317
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.08551618356765243
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1405258525735832
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2203603523232973
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.36905892568943005
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6109653385163588
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.2939021691226414
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.4514913657770801
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.6257456828885399
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6951020408163264
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.789105180533752
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.4514913657770801
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.27701726844583985
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21967346938775512
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.15373312401883832
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.26965081394591983
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.40631678733158394
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.4672444533614047
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.552848152657576
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5000381566353087
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5576212653559591
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.42703540499164716
name: Cosine Map@100
---
# SentenceTransformer based on BAAI/bge-base-en-v1.5
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) dataset. 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:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 196 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet)
- **Language:** en
<!-- - **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': 196, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, '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()
)
```
## 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("bwang0911/bge-int8")
# Run inference
sentences = [
'Where in Australia was swashbuckling Errol Flynn born?',
'Errol Flynn Blood" (1935), Major Geoffrey Vickers in "The Charge of the Light Brigade" (1936), as well as a number of Westerns, such as "Dodge City" (1939), "Santa Fe Trail" (1940), and "San Antonio" (1945). Errol Leslie Flynn was born on 20 June 1909 in Battery Point, a suburb of Hobart, Tasmania, Australia. His father, Theodore Thomson Flynn, was a lecturer (1909) and later professor (1911) of biology at the University of Tasmania. His mother was born Lily Mary Young, but shortly after marrying Theodore at St John\'s Church of England, Birchgrove, Sydney, on 23 January 1909, she changed her first name',
'Errol Flynn early in his career: Errol Flynn Errol Leslie Thomson Flynn (20 June 1909 – 14 October 1959) was an Australian-born American actor during the Golden Age of Hollywood. Considered the natural successor to Douglas Fairbanks, he achieved worldwide fame for his romantic swashbuckler roles in Hollywood films, as well as frequent partnerships with Olivia de Havilland. He was best known for his role as Robin Hood in "The Adventures of Robin Hood" (1938); his portrayal of the character was named by the American Film Institute as the 18th greatest hero in American film history. His other famous roles included the',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.28 | 0.6 | 0.64 | 0.3 | 0.66 | 0.38 | 0.34 | 0.26 | 0.86 | 0.34 | 0.18 | 0.56 | 0.4694 |
| cosine_accuracy@3 | 0.46 | 0.78 | 0.9 | 0.38 | 0.78 | 0.5 | 0.5 | 0.48 | 0.96 | 0.48 | 0.52 | 0.66 | 0.7347 |
| cosine_accuracy@5 | 0.54 | 0.84 | 0.92 | 0.42 | 0.82 | 0.6 | 0.56 | 0.64 | 0.96 | 0.6 | 0.64 | 0.68 | 0.8163 |
| cosine_accuracy@10 | 0.68 | 0.92 | 0.94 | 0.48 | 0.92 | 0.74 | 0.6 | 0.72 | 1.0 | 0.7 | 0.8 | 0.84 | 0.9184 |
| cosine_precision@1 | 0.28 | 0.6 | 0.64 | 0.3 | 0.66 | 0.38 | 0.34 | 0.26 | 0.86 | 0.34 | 0.18 | 0.56 | 0.4694 |
| cosine_precision@3 | 0.18 | 0.52 | 0.3 | 0.18 | 0.3467 | 0.1667 | 0.3 | 0.16 | 0.4 | 0.2467 | 0.1733 | 0.2333 | 0.3946 |
| cosine_precision@5 | 0.132 | 0.496 | 0.188 | 0.128 | 0.244 | 0.12 | 0.3 | 0.132 | 0.252 | 0.204 | 0.128 | 0.144 | 0.3878 |
| cosine_precision@10 | 0.088 | 0.432 | 0.096 | 0.076 | 0.142 | 0.074 | 0.244 | 0.076 | 0.136 | 0.132 | 0.08 | 0.096 | 0.3265 |
| cosine_recall@1 | 0.1217 | 0.0629 | 0.6167 | 0.1554 | 0.33 | 0.38 | 0.0121 | 0.25 | 0.7473 | 0.0737 | 0.18 | 0.54 | 0.0357 |
| cosine_recall@3 | 0.2267 | 0.1301 | 0.8567 | 0.2226 | 0.52 | 0.5 | 0.0406 | 0.46 | 0.9253 | 0.1547 | 0.52 | 0.64 | 0.0855 |
| cosine_recall@5 | 0.2773 | 0.1827 | 0.8767 | 0.2646 | 0.61 | 0.6 | 0.0587 | 0.61 | 0.942 | 0.2117 | 0.64 | 0.66 | 0.1405 |
| cosine_recall@10 | 0.349 | 0.2871 | 0.8967 | 0.3058 | 0.71 | 0.74 | 0.0821 | 0.69 | 0.9933 | 0.2727 | 0.8 | 0.84 | 0.2204 |
| **cosine_ndcg@10** | **0.2921** | **0.5202** | **0.7847** | **0.2791** | **0.6314** | **0.5328** | **0.2699** | **0.465** | **0.9204** | **0.272** | **0.4849** | **0.6789** | **0.3691** |
| cosine_mrr@10 | 0.3988 | 0.7049 | 0.7672 | 0.3567 | 0.7392 | 0.4702 | 0.428 | 0.3999 | 0.9073 | 0.4481 | 0.3844 | 0.6334 | 0.611 |
| cosine_map@100 | 0.2301 | 0.3763 | 0.7406 | 0.247 | 0.5493 | 0.4806 | 0.0994 | 0.3974 | 0.8925 | 0.223 | 0.393 | 0.6282 | 0.2939 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"climatefever",
"dbpedia",
"fever",
"fiqa2018",
"hotpotqa",
"msmarco",
"nfcorpus",
"nq",
"quoraretrieval",
"scidocs",
"arguana",
"scifact",
"touche2020"
]
}
```
| Metric | Value |
|:--------------------|:--------|
| cosine_accuracy@1 | 0.4515 |
| cosine_accuracy@3 | 0.6257 |
| cosine_accuracy@5 | 0.6951 |
| cosine_accuracy@10 | 0.7891 |
| cosine_precision@1 | 0.4515 |
| cosine_precision@3 | 0.277 |
| cosine_precision@5 | 0.2197 |
| cosine_precision@10 | 0.1537 |
| cosine_recall@1 | 0.2697 |
| cosine_recall@3 | 0.4063 |
| cosine_recall@5 | 0.4672 |
| cosine_recall@10 | 0.5528 |
| **cosine_ndcg@10** | **0.5** |
| cosine_mrr@10 | 0.5576 |
| cosine_map@100 | 0.427 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### trivia
* Dataset: [trivia](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet) at [bfe9460](https://huggingface.co/datasets/sentence-transformers/trivia-qa-triplet/tree/bfe94607eb149a89fe8107e8c8d187977e587a7d)
* Size: 60,315 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 15.15 tokens</li><li>max: 42 tokens</li></ul> | <ul><li>min: 113 tokens</li><li>mean: 138.92 tokens</li><li>max: 196 tokens</li></ul> | <ul><li>min: 111 tokens</li><li>mean: 137.88 tokens</li><li>max: 196 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:--------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>Which American-born Sinclair won the Nobel Prize for Literature in 1930?</code> | <code>Sinclair Lewis Sinclair Lewis Harry Sinclair Lewis (February 7, 1885 – January 10, 1951) was an American novelist, short-story writer, and playwright. In 1930, he became the first writer from the United States to receive the Nobel Prize in Literature, which was awarded "for his vigorous and graphic art of description and his ability to create, with wit and humor, new types of characters." His works are known for their insightful and critical views of American capitalism and materialism between the wars. He is also respected for his strong characterizations of modern working women. H. L. Mencken wrote of him, "[If] there</code> | <code>Nobel Prize in Literature analyze its importance on potential future Nobel Prize in Literature laureates. Only Alice Munro (2009) has been awarded with both. The Neustadt International Prize for Literature is regarded as one of the most prestigious international literary prizes, often referred to as the American equivalent to the Nobel Prize. Like the Nobel or the Man Booker International Prize, it is awarded not for any one work, but for an entire body of work. It is frequently seen as an indicator of who may be awarded the Nobel Prize in Literature. Gabriel García Márquez (1972 Neustadt, 1982 Nobel), Czesław Miłosz (1978 Neustadt,</code> |
| <code>Where in England was Dame Judi Dench born?</code> | <code>Judi Dench regular contact with the theatre. Her father, a physician, was also the GP for the York theatre, and her mother was its wardrobe mistress. Actors often stayed in the Dench household. During these years, Judi Dench was involved on a non-professional basis in the first three productions of the modern revival of the York Mystery Plays in 1951, 1954 and 1957. In the third production she played the role of the Virgin Mary, performed on a fixed stage in the Museum Gardens. Though she initially trained as a set designer, she became interested in drama school as her brother Jeff</code> | <code>Judi Dench to independence, published in August 2014, a few weeks before the Scottish referendum. In September 2018, Dench criticized the response to the sexual misconduct allegations made against actor Kevin Spacey, referring to him as a "good friend". Judi Dench Dame Judith Olivia Dench (born 9 December 1934) is an English actress. Dench made her professional debut in 1957 with the Old Vic Company. Over the following few years, she performed in several of Shakespeare's plays, in such roles as Ophelia in "Hamlet", Juliet in "Romeo and Juliet", and Lady Macbeth in "Macbeth". Although most of her work during this period</code> |
| <code>From which country did Angola achieve independence in 1975?</code> | <code>Corruption in Angola they really are. Angola's colonial era ended with the Angolan War of Independence against Portugal occurred between 1970 and 1975. Independence did not produce a unified Angola, however; the country plunged into years of civil war between the National Union for the Total Independence of Angola (UNITA) and the governing Popular Movement for the Liberation of Angola (MPLA). 30 years of war would produce historical legacies that combine to allow for the persistence of a highly corrupt government system. The Angolan civil war was fought between the pro-western UNITA and the communist MPLA and had the characteristics typical of a</code> | <code>Cuban intervention in Angola Cuban intervention in Angola In November 1975, on the eve of Angola's independence, Cuba launched a large-scale military intervention in support of the leftist People's Movement for the Liberation of Angola (MPLA) against United States-backed interventions by South Africa and Zaire in support of two right-wing independence movements competing for power in the country, the National Liberation Front of Angola (FNLA) and the National Union for the Total Independence of Angola (UNITA). By the end of 1975 the Cuban military in Angola numbered more than 25,000 troops. Following the withdrawal of Zaire and South Africa, Cuban forces remained in Angola</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 128
- `learning_rate`: 1e-05
- `warmup_ratio`: 0.1
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 1e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `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
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0212 | 10 | 2.7514 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0424 | 20 | 2.7415 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0636 | 30 | 2.5319 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0847 | 40 | 2.3283 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1059 | 50 | 2.0535 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1271 | 60 | 1.8257 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1483 | 70 | 1.6569 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1695 | 80 | 1.5127 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.1907 | 90 | 1.3586 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2119 | 100 | 1.3002 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2331 | 110 | 1.2825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2542 | 120 | 1.1649 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2754 | 130 | 1.1589 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.2966 | 140 | 1.1404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3178 | 150 | 1.1462 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3390 | 160 | 1.1297 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3602 | 170 | 1.0774 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.3814 | 180 | 1.0845 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4025 | 190 | 1.0574 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4237 | 200 | 1.1048 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4449 | 210 | 1.0817 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4661 | 220 | 1.0603 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.4873 | 230 | 1.0383 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5085 | 240 | 1.0197 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5297 | 250 | 1.0979 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5508 | 260 | 1.0303 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5720 | 270 | 1.0363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.5932 | 280 | 1.0433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6144 | 290 | 0.98 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6356 | 300 | 1.0272 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6568 | 310 | 1.054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6780 | 320 | 1.0213 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.6992 | 330 | 1.0111 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7203 | 340 | 0.9849 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7415 | 350 | 1.0054 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7627 | 360 | 0.9998 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.7839 | 370 | 0.9871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8051 | 380 | 1.0223 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8263 | 390 | 0.9592 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8475 | 400 | 0.9736 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8686 | 410 | 0.9653 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.8898 | 420 | 0.9856 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9110 | 430 | 1.0445 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9322 | 440 | 0.9818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9534 | 450 | 0.9937 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9746 | 460 | 0.9818 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.9958 | 470 | 0.9799 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0169 | 480 | 0.908 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0381 | 490 | 0.9568 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0593 | 500 | 0.9887 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.0805 | 510 | 0.9401 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1017 | 520 | 0.934 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1229 | 530 | 0.9245 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1441 | 540 | 0.9329 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1653 | 550 | 0.9985 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.1864 | 560 | 0.9591 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2076 | 570 | 0.9433 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2288 | 580 | 0.9645 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.25 | 590 | 0.9682 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2712 | 600 | 0.9385 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.2924 | 610 | 0.8819 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3136 | 620 | 0.9471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3347 | 630 | 0.919 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3559 | 640 | 0.9523 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3771 | 650 | 0.9248 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.3983 | 660 | 0.9784 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4195 | 670 | 0.9003 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4407 | 680 | 0.9652 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4619 | 690 | 0.9286 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.4831 | 700 | 0.8873 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5042 | 710 | 0.9252 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5254 | 720 | 0.938 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5466 | 730 | 0.9394 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5678 | 740 | 0.9224 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.5890 | 750 | 0.9128 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6102 | 760 | 0.9367 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6314 | 770 | 0.9664 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6525 | 780 | 0.9307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6737 | 790 | 0.8823 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.6949 | 800 | 0.9306 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7161 | 810 | 0.8754 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7373 | 820 | 0.9376 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7585 | 830 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.7797 | 840 | 0.9254 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8008 | 850 | 0.9282 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8220 | 860 | 0.9175 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8432 | 870 | 0.9482 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8644 | 880 | 0.9289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.8856 | 890 | 0.9354 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9068 | 900 | 0.9253 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9280 | 910 | 0.9363 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9492 | 920 | 1.0037 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9703 | 930 | 0.8552 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 1.9915 | 940 | 0.9267 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0127 | 950 | 0.9043 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0339 | 960 | 0.8859 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0551 | 970 | 0.9149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0763 | 980 | 0.917 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.0975 | 990 | 0.8839 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1186 | 1000 | 0.9502 | 0.2921 | 0.5202 | 0.7847 | 0.2791 | 0.6314 | 0.5328 | 0.2699 | 0.4650 | 0.9204 | 0.2720 | 0.4849 | 0.6789 | 0.3691 | 0.5000 |
| 2.1398 | 1010 | 0.9131 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1610 | 1020 | 0.9191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.1822 | 1030 | 0.8992 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2034 | 1040 | 0.913 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2246 | 1050 | 0.871 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2458 | 1060 | 0.9336 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2669 | 1070 | 0.903 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.2881 | 1080 | 0.8995 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3093 | 1090 | 0.9018 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3305 | 1100 | 0.861 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3517 | 1110 | 0.8548 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3729 | 1120 | 0.8928 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.3941 | 1130 | 0.9606 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4153 | 1140 | 0.8921 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4364 | 1150 | 0.8511 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4576 | 1160 | 0.8977 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.4788 | 1170 | 0.8894 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5 | 1180 | 0.8647 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5212 | 1190 | 0.8421 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5424 | 1200 | 0.8654 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5636 | 1210 | 0.926 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.5847 | 1220 | 0.8911 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6059 | 1230 | 0.9191 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6271 | 1240 | 0.8731 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6483 | 1250 | 0.8757 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6695 | 1260 | 0.8825 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.6907 | 1270 | 0.8881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7119 | 1280 | 0.8745 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7331 | 1290 | 0.8404 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7542 | 1300 | 0.9377 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7754 | 1310 | 0.9149 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.7966 | 1320 | 0.8881 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8178 | 1330 | 0.8889 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8390 | 1340 | 0.9289 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8602 | 1350 | 0.9169 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.8814 | 1360 | 0.8803 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9025 | 1370 | 0.8398 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9237 | 1380 | 0.8716 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9449 | 1390 | 0.8912 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9661 | 1400 | 0.8471 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 2.9873 | 1410 | 0.9158 | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
</details>
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.7.0+cu126
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## 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}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->