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quantisan
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paraphrase-MiniLM-L3-v2-93dataset-v2labels

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results
Model card Files Community
paraphrase-MiniLM-L3-v2-93dataset-v2labels / README.md
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metadata
base_model: sentence-transformers/paraphrase-MiniLM-L3-v2
library_name: setfit
metrics:
  - accuracy
pipeline_tag: text-classification
tags:
  - setfit
  - sentence-transformers
  - text-classification
  - generated_from_setfit_trainer
widget:
  - text: >-
      Category: Milk, Buttermilk, Kefir, Goat's milk, Non-dairy milk, Soy milk,
      Almond milk, Rice milk, Coconut milk, Yogurt, Chipotle dip, Dill dip,
      Onion dip, Ranch dip, Spinach dip, Tzatziki dip, Vegetable dip, Yogurt
      parfait, Frozen yogurt, Frozen yogurt sandwich
  - text: >-
      company.sector:  Software,  Finance,  Communications, pharmaceuticals,
      technology, Fashion, real estate,  software, banking and insurance,
      groceries, construction/real estate/banking,   Oil refining,  Oil
      refining, retail,  retail,  casinos, food packaging, cars, cosmetics, None
  - text: 'variety: Western, Eastern'
  - text: >-
      Data.Lycopene: 0, 1, 300, 7271, 6399, 4601, 4123, 1523, 1422, 1351, 11,
      816, 819, 812, 1001, 769, 1365, 97, 21, 34
  - text: 'Date.Month: 8, 3, 4, 5, 6, 7, 9, 10, 11, 12, 1, 2'
inference: true
model-index:
  - name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2
    results:
      - task:
          type: text-classification
          name: Text Classification
        dataset:
          name: Unknown
          type: unknown
          split: test
        metrics:
          - type: accuracy
            value: 0.7629716981132075
            name: Accuracy

SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2

This is a SetFit model that can be used for Text Classification. This SetFit model uses sentence-transformers/paraphrase-MiniLM-L3-v2 as the Sentence Transformer embedding model. A LogisticRegression instance is used for classification.

The model has been trained using an efficient few-shot learning technique that involves:

  1. Fine-tuning a Sentence Transformer with contrastive learning.
  2. Training a classification head with features from the fine-tuned Sentence Transformer.

Model Details

Model Description

Model Sources

Model Labels

Label Examples
Integer
  • 'quality: 5, 6, 7, 4, 8, 3'
  • 'trunk: 11, 12, 16, 20, 21, 10, 17, 13, 9, 7, 8, 22, 18, 15, 23, 14, 6, 5'
  • 'Completions: 589, 112, 114, 199, 156, 239, 451, 187, 252, 395, 682, 1, 1228, 93, 315, 150, 80, 92, 233, 406'
Country Name
  • 'Nationality: Portugal, Argentina, Brazil, Uruguay, Germany, Poland, Spain, Belgium, Chile, Croatia, Wales, Italy, Slovenia, France, Gabon, Sweden, Netherlands, Denmark, Slovakia, England'
  • 'adm0_name: Afghanistan, Algeria, Angola, Argentina, Armenia, Azerbaijan, Bangladesh, Bassas da India, Belarus, Benin, Bhutan, Bolivia, Burkina Faso, Burundi, Cambodia, Cameroon, Cape Verde, Central African Republic, Chad, China'
  • 'Nation: Afghanistan, Albania, Algeria, Andorra, Angola, Antigua and Barbuda, Argentina, Armenia, Australia, Austria, Azerbaijan, The Bahamas, Bahrain, Bangladesh, Barbados, Belarus, Belgium, Belize, Benin, Bhutan'
License Plate
  • 'plate: AZIZ714, BATBOX1, BBOMBS, BEACHY1, BLK PWR5, BOT TAK, CHERIPI, CIO FTW, DAVES88, DMOBGFY, DOITFKR, EGGPUTT, F DIABDZ, FJ 666, FKK OFF, FKN BLAK, FLT ATCK, F LUPUS, HVNNHEL, H8DES'
Date
  • 'Incident.Date.Full: 2015/01/02, 2015/01/03, 2015/01/04, 2015/01/05, 2015/01/06, 2015/01/07, 2015/01/08, 2015/01/09, 2015/01/11, 2015/01/13, 2015/01/14, 2015/01/15, 2015/01/16, 2015/01/17, 2015/01/18, 2015/01/19, 2015/01/20, 2015/01/21, 2015/01/22, 2015/01/23'
  • 'end_date: 12/20/22, 12/19/22, 12/15/22, 12/14/22, 12/13/22, 12/12/22, 12/11/22, 12/7/22, 12/6/22, 12/5/22, 12/4/22, 12/2/22, 11/29/22, 11/22/22, 11/21/22, 11/20/22, 11/19/22, 11/17/22, 11/15/22, 11/14/22'
  • 'week_ended: 2021-08-28, 2021-08-21, 2021-08-14, 2021-08-07, 2021-07-31, 2021-07-24, 2021-07-17, 2021-07-10, 2021-07-03, 2021-06-26, 2021-06-19, 2021-06-12, 2021-06-05, 2021-05-29, 2021-05-22, 2021-05-15, 2021-05-08, 2021-05-01, 2021-04-24, 2021-04-17'
Latitude
  • 'Latitude: 48,87217700, 48,85543800, 48,87416100, 48,87322500, 48,87422500, 48,84189000, 48,86617200, 48,87112100, 48,86552200, 48,87623100, 48,85609000, 48,85642700, 48,86853300, 48,87465400, 48,86995000, 48,85654000, 48,87022000, 48,86962600, 48,85663200, 48,83476200'
  • 'Latitude: 50.17, 45.775, 42.17, 38.87, 43.25, 42.6, 41.73, 40.827, 40.821, 40.73, 39.48, 38.789, 38.638, 38.49, 38.404, 37.748, 37.1, 36.77, 39.284, 37.615'
  • 'lat: 83.92115933668057, 89.53277415300325, 85.37696959908148, 85.44622332365381, 84.28538158324413, 87.96664079539569, 86.11414393337242, 85.43864590316868, 87.65474214915454, 81.67725407101064, 90.47817498708324, 89.87993043195812, 81.56791356025577, 88.48808747114165, 89.3843538611984, 87.5218603199103, 83.99238693700401, 82.50195719071465, 85.84865551792468, 87.92121711225418'
Month Number
  • 'bibliography.publication.month: 6, 11, 3, 8, 1, 10, 7, 2, 4, 5, 9, 12'
  • 'Date.Month: 8, 3, 4, 5, 6, 7, 9, 10, 11, 12, 1, 2'
  • 'Incident.Date.Month: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12'
Floating Point Number
  • 'femwhite: 6262128.0, 6785226.0, 6960988.0, 6879090.0, 6388969.0, 6655571.0, 7328058.0, 8487223.0, 8634083.0, 7955003.0, 7190359.0, 5691567.0, 4653172.0, 4286302.0, 4240992.0, 3833722.0, 2764852.0'
  • 'Total Population MOE Appx: 93589.07647025918, 97743.48311569727, 102875.52676728423, 100180.67827181323, 91225.48461689966, 84927.02484353623, 74086.5329139754, 64054.081534081255, 58510.64119755533, 49482.37049442148, 50209.88701800759, 34985.37698863254, 35444.72186612619, 31511.029844047032, 25723.46796958681, 26258.43669569568, 23366.799224435217, 18833.469882408906, 17592.145498006077, 15373.85201819406'
  • 'chlorides: 0.076, 0.098, 0.092, 0.075, 0.069, 0.065, 0.073, 0.071, 0.097, 0.089, 0.114, 0.176, 0.17, 0.368, 0.086, 0.341, 0.077, 0.082, 0.106, 0.084'
Time
  • 'STOP_FRISK_TIME: 14:26:00, 11:10:00, 11:35:00, 13:20:00, 21:25:00, 20:00:00, 19:58:00, 13:15:00, 8:16:00, 18:44:00, 22:30:00, 4:45:00, 18:30:00, 0:00:00, 9:58:00, 11:15:00, 13:00:00, 8:00:00, 14:57:00, 4:15:00'
Place
  • 'Show.Theatre: Booth, Broadway, Ethel Barrymore, Palace, Belasco, Gershwin, Minskoff, Circle In The Square, Virginia, Criterion, Vivian Beaumont, Winter Garden, Plymouth, Richard Rodgers, Golden, Broadhurst, Imperial, Walter Kerr, St. James, Ambassador'
Full Name
  • 'Person.Name: Tim Elliot, Lewis Lee Lembke, John Paul Quintero, Matthew Hoffman, Michael Rodriguez, Kenneth Joe Brown, Kenneth Arnold Buck, Brock Nichols, Autumn Steele, Leslie Sapp III, Patrick Wetter, Ron Sneed, Hashim Hanif Ibn Abdul-Rasheed, Nicholas Ryan Brickman, Omarr Julian Maximillian Jackson, Loren Simpson, James Dudley Barker, Artago Damon Howard, Thomas Hamby, Jimmy Foreman'
  • 'sponsor_candidate: None, Vern Buchanan, Joyce Ann Elliott, Xochitl Torres Small, Desiree Tims, Morris Durham Davis, John Katko, Stephen Daniel, Nancy Mace, Alaina Shearer, Wesley Hunt, Scott Perry, J.D. Scholten, Jim Bognet, Angie Craig, Brynne S. Kennedy, Young Kim, Ammar Campa-Najjar, Donna E. Shalala, Jennifer T. Wexton'
  • 'candidate_name: Abigail A. Spanberger, Nicholas J. Freitas, Kara Eastman, Don Bacon, Tyler Schaeffer, Jill Schupp, Ann Wagner, Martin Schulte, Dana Balter, John Katko, Steve Williams, Christina Hale, Victoria Spartz, Kenneth Tucker, Joyce Ann Elliott, French Hill, Jared Forrest Golden, Dale John Crafts, Marie Newman, Mike Fricilone'
U.S. State Abbreviation
  • 'Incident.Location.State: WA, OR, KS, CA, CO, OK, AZ, IA, PA, TX, OH, LA, MT, UT, AR, IL, NV, NM, MN, MO'
  • 'state2: AL, AK, AZ, AR, CA, CO, CT, DE, FL, GA, HI, ID, IL, IN, IA, KS, KY, LA, ME, MD'
  • 'State: AK, AL, AR, AZ, CA, CO, CT, DC, DE, FL, GA, HI, IA, ID, IL, IN, KS, KY, LA, MA'
Price
  • 'Income Range: $0 - $30,000, $30,001 - $48,000, $48,001 - $75,000, $75,001 - $110,000, $110,000+'
U.S. State
  • 'state: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine, Maryland'
  • 'Geography: United States, Iowa, Michigan, Minnesota, North Dakota, South Dakota, Wisconsin, Minneapolis-St. Paul-Bloomington, MN-WI'
  • 'state: None, Florida, Iowa, Pennsylvania, Nevada, Georgia, South Carolina, Nebraska CD-2, Montana, Maine, Maine CD-2, Maine CD-1, Arizona, North Carolina, Texas, Wyoming, West Virginia, Wisconsin, Washington, Vermont'
Gender
  • "Gender: Men's, Women's"
  • 'sex: Male, Female'
  • 'gender: Female, Male'
Longitude
  • 'long: -73.9561344937861, -73.9570437717691, -73.9768311751004, -73.9757249834141, -73.9593126695714, -73.9565700386162, -73.9719735582476, -73.9602609920814, -73.9770718586754, -73.9596413903948, -73.9702676472613, -73.9683613516225, -73.9541201789795, -73.9582694312289, -73.9674285955293, -73.9722500196844, -73.9695063535333, -73.9532170504865, -73.9768603630674, -73.9706105896967'
  • 'Longitude: 6.85, 2.97, 2.53, -4.02, 10.87, 11.93, 12.7, 14.139, 14.426, 13.897, 14.83, 15.213, 15.064, 14.933, 14.962, 14.999, 12.02, 14.399, 23.336, 24.439'
  • 'Longitude: 2,77228900, 2,77461100, 2,77370600, 2,77423900, 2,77654400, 2,79937600, 2,78064700, 2,77697400, 2,78928200, 2,78032200, 2,77731200, 2,77121300, 2,77167600, 2,78236500, 2,76694300, 2,77139500, 2,76872200, 2,76741500, 2,77156700, 2,82065100'
URL
Day of Week
  • 'day: Sun, Sat, Thur, Fri'
  • 'DAY2: Monday, Wednesday, Tuesday, Friday, Saturday, Thursday, Sunday'
Slug
  • 'Slug Geography: united-states, virginia'
  • 'Slug University: doctoral-universities, associates-colleges-high-transfer-high-traditional, doctoral-universities-highest-research-activity, doctoral-universities-higher-research-activity, doctoral-universities-moderate-research-activity, masters-colleges-universities-larger-programs, not-applicable-not-in-carnegie-universe-not-accredited-or-nondegree-granting'
  • 'Slug CIP: liberal-arts-sciences'
Timestamp
  • 'Modification: 26/06/2022 13:31:22, 12/04/2018 15:31:20, 26/06/2022 13:30:09, 26/06/2022 13:30:02, 26/06/2022 13:30:31, 26/06/2022 11:27:12, 26/06/2022 13:30:39, 28/10/2018 00:10:20, 12/04/2018 15:31:19, 26/06/2022 11:26:39, 12/07/2022 09:46:24, 12/04/2018 15:31:18, 21/10/2022 13:07:41, 21/10/2022 13:07:50, 16/09/2020 10:36:33, 26/06/2022 15:36:44, 24/07/2022 09:14:31, 12/04/2018 15:31:17, 26/06/2022 15:36:38, 12/07/2022 09:45:04'
  • 'date: 2001-01-02 00:00:00, 2001-01-03 00:00:00, 2001-01-04 00:00:00, 2001-01-05 00:00:00, 2001-01-08 00:00:00, 2001-01-09 00:00:00, 2001-01-10 00:00:00, 2001-01-11 00:00:00, 2001-01-12 00:00:00, 2001-01-16 00:00:00, 2001-01-17 00:00:00, 2001-01-18 00:00:00, 2001-01-19 00:00:00, 2001-01-22 00:00:00, 2001-01-23 00:00:00, 2001-01-24 00:00:00, 2001-01-25 00:00:00, 2001-01-26 00:00:00, 2001-01-29 00:00:00, 2001-01-30 00:00:00'
  • 'created_at: 12/30/20 12:29, 11/2/20 21:26, 11/2/20 22:16, 11/2/20 21:32, 11/2/20 22:01, 11/2/20 22:18, 11/2/20 22:26, 11/2/20 23:31, 11/2/20 21:49, 10/31/20 17:22, 11/1/20 14:39, 11/2/20 08:22, 10/29/20 14:16, 10/31/20 08:36, 10/29/20 11:08, 10/29/20 09:00, 10/29/20 16:13, 10/29/20 16:14, 10/30/20 15:45, 10/28/20 09:24'
Coordinate
  • 'lat_long: POINT (-73.9561344937861 40.7940823884086), POINT (-73.9570437717691 40.794850940803904), POINT (-73.9768311751004 40.76671780725581), POINT (-73.9757249834141 40.7697032606755), POINT (-73.9593126695714 40.797533370163), POINT (-73.9565700386162 40.7902561000937), POINT (-73.9719735582476 40.7693045133578), POINT (-73.9602609920814 40.79428830455661), POINT (-73.9770718586754 40.7729752391435), POINT (-73.9596413903948 40.7903128889029), POINT (-73.9702676472613 40.7762126854894), POINT (-73.9683613516225 40.7725908847499), POINT (-73.9541201789795 40.7931811701082), POINT (-73.9582694312289 40.7917367820255), POINT (-73.9674285955293 40.7829723919744), POINT (-73.9722500196844 40.7742879599026), POINT (-73.9695063535333 40.7823507678183), POINT (-73.9532170504865 40.7919669739962), POINT (-73.9768603630674 40.7702795904962), POINT (-73.9706105896967 40.7698124821507)'
Likert scale
  • 'Procedure.Hip Knee.Quality: Average, Unknown, Better, Worse'
  • 'Rating.Experience: Below, Same, None, Above'
  • 'Procedure.Heart Failure.Quality: Average, Worse, Unknown, Better'
Categorical
  • 'species: setosa, versicolor, virginica'
  • 'stage: general'
  • 'color: E, I, J, H, F, G, D'
Secondary Address
  • 'STOP_LOCATION_APARTMENT: (null), 2, 7, 4TH, 2FL, ROOF, ROOF T, BASEME, LOBBY, 17TH, 2 FLOO, 12, 1701, HALLWA, 1E, 5D, SIDEWA, FRONT, 12C, None'
Year
  • 'Year: 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979'
  • 'Year: 2020, 2019, 2018, 2017, 2016, 2015, 2014, 2013'
  • 'Date.Year: 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009'
Zip Code
  • 'zip_codes: nan, 12081.0, 10090.0, 12423.0, 12420.0'
  • 'STOP_LOCATION_ZIP_CODE: (null), 20292, AVENUE, 5 AVEN, 10019, 22768, 10035, 10026, 10128, 24231, 10030, 10039, 23874, 11213, 11233, 100652, 10451, 23543, 100745, PROSPE'
  • 'recipient_zip: 995084442, 99503, 995163436, 352124572, 35216, 35976, 358021277, 352174710, 35203, 35233, 35805, 72716, 72201, 72035, 72015, 72223, 72019, 72113, 72758, 72227'
Region
  • 'Subregion: Western Europe, Italy, Greece, Turkey, Western Asia, Africa (northeastern) and Red Sea, Africa (eastern), Africa (central), Africa (western), Africa (northern), Middle East (western), Middle East (southern), Middle East (eastern), Indian Ocean (western), Indian Ocean (southern), New Zealand, Kermadec Islands, Tonga Islands, Samoan and Wallis Islands, Fiji Islands'
  • 'Region: Mediterranean and Western Asia, Africa and Red Sea, Middle East and Indian Ocean, New Zealand to Fiji, Melanesia and Australia, Indonesia, Philippines and SE Asia, Japan, Taiwan, Marianas, Kuril Islands, Kamchatka and Mainland Asia, Alaska, Canada and Western USA, Hawaii and Pacific Ocean, México and Central America, South America, West Indies, Iceland and Arctic Ocean, Atlantic Ocean, Antarctica'
  • 'region: South, West, NE, N Cntrl'
AM/PM
  • 'shift: PM, AM'
Race/Ethnicity
  • 'Person.Race: Asian, White, Hispanic, African American, Other, Unknown, Native American'
  • 'SUSPECT_RACE_DESCRIPTION: (null), WHITE, BLACK HISPANIC, BLACK, WHITE HISPANIC, ASIAN/PAC.ISL, AMER IND, MALE'
  • 'race: black, white, other'
Street Name
  • 'STOP_LOCATION_STREET_NAME: GREENWICH STREET, WALL STREET, GREENE STREET, WEST BROADWAY, WEST STREET, CHAMBERS STREET, CORTLANDT STREET, FULTON STREET, CLIFF STREET, SPRING STREET, CEDAR STREET, LIBERTY STREET, BARCLAY STREET, BATTERY PLACE, MERCER STREET, BROADWAY, SOUTH STREET, THOMPSON STREET, JAY STREET, CHURCH STREET'
  • "Adresse: Adventureland, 10 Place d'Ariane, Fantasyland, None, Disneyland Paris, Unnamed Road, Discoveryland, 3 Rue de la Galmy, Boulevard du Grand Fossé, Liaison Douce, 24 Town Square, Les Pléiades, Frontierland, 5 Cours du Danube, Rue du Bœuf Agile, Avenue René Goscinny, 1998 Rue Georges Méliès, Boulevard du Parc, Town Square, Avenue Paul Séramy"
Day of Month
  • 'bibliography.publication.day: 1, 17, 16, 20, 29, 10, 14, 11, 9, 18, 19, 22, 25, 15, 6, 28, 27, 2, 12, 21'
  • 'Date.Day: 26, 24, 31, 7, 14, 21, 28, 5, 12, 19, 2, 9, 16, 23, 30, 4, 11, 18, 25, 1'
  • 'Incident.Date.Day: 2, 3, 4, 5, 6, 7, 8, 9, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23'
Boolean
  • 'ID Workforce Status: True'
  • 'nationwide_batch: False'
  • 'PHYSICAL_FORCE_RESTRAINT_USED_FLAG: (null), Y'
Color
  • 'highlight_fur_color: None, Cinnamon, White, Gray, Cinnamon, White, Gray, White, Black, Cinnamon, White, Black, Black, White, Black, Cinnamon, Gray, Black'
  • 'color: Yellow, Black, White'
  • 'primary_fur_color: None, Gray, Cinnamon, Black'
Location
  • 'Geography: United States, Virginia'
  • "artist.birth.location: Polska, Philadelphia, United States, Springfield, United States, Leicestershire, United Kingdom, Wigan, United Kingdom, Tel Aviv-Yafo, Yisra'el, Kiel, Deutschland, New York, United States, Isleworth, United Kingdom, Leeds, United Kingdom, Kirkcaldy, United Kingdom, Worcester, United Kingdom, London, United Kingdom, Northampton, United Kingdom, Tuszyn, Polska, Reading, United Kingdom, Udine, Italia, Ayacucho, Argentina, Genve, Schweiz, Hmeenlinna, Suomi"
  • 'artist.death.location: None, London, United Kingdom, Monson, United States, Paris, France, Worcester, United Kingdom, Aldbourne, United Kingdom, Hampstead, United Kingdom, Zrich, Schweiz, New Haven, United States, Woodstock, United States, Musselburgh, United Kingdom, Maidstone, United Kingdom, Edinburgh, United Kingdom, Wallingford, United Kingdom, Barnes, United Kingdom, Wiesbaden, Deutschland, Los Angeles, Madrid, Espaa, Schweiz, Rennes, France'
Last Name
  • 'candidat: Bush, Perot, Clinton'
Company Name
  • "company.name: Microsoft, Berkshire Hathaway, Telmex, F. Hoffmann-La Roche, Zara, Henderson Land Development, Oracle, Lin Yuan Group, Aldi, Sun Hung Kai Properties, Kingdom Holding Company, Koch industries, Cheung king, Walmart, Seibu Corporation, Las Vegas Sands, Aldi Nord, Tetra Pak, BMW, L'Oreal"
Street Address
  • 'STOP_LOCATION_FULL_ADDRESS: 180 GREENWICH STREET, WALL STREET && BROADWAY, 75 GREENE STREET, 429 WEST BROADWAY, WEST STREET && CHAMBERS STREET, CHAMBERS STREET && WEST BROADWAY, CORTLANDT STREET && CHURCH STREET, 111 FULTON STREET, 25 CLIFF STREET, SPRING STREET && AVENUE OF THE AMERICAS, 130 CEDAR STREET, 225 LIBERTY STREET, BARCLAY STREET && WEST STREET, 153 GREENWICH STREET, BATTERY PLACE && STATE STREET, MERCER STREET && BROOME STREET, WEST STREET && CANAL STREET, BROADWAY && PRINCE STREET, WEST BROADWAY && AVENUE OF THE AMERICAS, 3 SOUTH STREET'
Short text
  • 'make: AMC Concord, AMC Pacer, AMC Spirit, Buick Century, Buick Electra, Buick LeSabre, Buick Opel, Buick Regal, Buick Riviera, Buick Skylark, Cad. Deville, Cad. Eldorado, Cad. Seville, Chev. Chevette, Chev. Impala, Chev. Malibu, Chev. Monte Carlo, Chev. Monza, Chev. Nova, Dodge Colt'
  • 'Club: Real Madrid CF, FC Barcelona, Paris Saint-Germain, FC Bayern Munich, Manchester United, Chelsea, Juventus, Manchester City, Arsenal, Atlético Madrid, Borussia Dortmund, Milan, Tottenham Hotspur, Napoli, Inter, Liverpool, Roma, Beşiktaş JK, AS Monaco, Bayer 04 Leverkusen'
  • 'memo_text: IN KIND: FACILITY RENTAL, None, IN KIND: BUMPER STICKERS SIGNS AND BUTTONS, IN KIND: BILLBOARD ADVERTISING, IN KIND: CATERING, IN KIND: PHOTOGRAPHY, IN KIND: AIR TRAVEL, IN KIND: PHOTOGRAPHY SERVICES, IN KIND: FACILITY RENTAL/CATERING, IN KIND: CAR RENTAL PARADE TICKET BANNER, IN KIND: CAMPAIGN SIGNS, IN KIND: OFFICE SPACE, IN KIND: BOOTH SPACE AT INDIANA STATE FAIR, IN KIND: TRAVEL, IN KIND: CHARTER BUS, IN KIND: SIGNAGE, IN KIND: EMAIL LIST, IN KIND: AIRFARE, IN KIND: TABLES, IN KIND: LODGING'
Occupation
  • 'Detailed Occupation: Physicians, Physicians & surgeons, Lawyers, & judges, magistrates, & other judicial workers, Medical & health services managers, Chief executives & legislators, Veterinarians, Social & community service managers, Securities, commodities, & financial services sales agents, Petroleum, mining & geological engineers, including mining safety engineers, Economists, Miscellaneous social scientists, including survey researchers & sociologists, Natural sciences managers, Geoscientists and hydrologists, except geographers, Detectives & criminal investigators, Judicial law clerks, Other psychologists, Architectural & engineering managers, Education administrators, Astronomers & physicists, Public relations and fundraising managers'
  • 'occupation: Operatives, Craftsmen, Sales, Other, Managers/admin, Professional/technical, Clerical/unskilled, Laborers, Transport, Service, nan, Household workers, Farm laborers, Farmers'
  • 'Detailed Occupation: Other managers, Cashiers, Retail salespersons, Driver/sales workers & truck drivers, Registered nurses'
Very short text
  • 'above_ground_sighter_measurement: None, FALSE, 4, 3, 30, 10, 6, 24, 8, 25, 5, 50, 70, 12, 2, 20, 7, 13, 15, 28'
  • 'review_reason_code: 2, 1, 4, None, 5, 3, 7, 3?, 8, D, ?, 3, 1, 1 or 2, D or 1, 7B, 1, 2, 1 OR 2, D OR 2, B, 4?'
  • 'status: N, Y, REMOVE, None, 1, ?, H, R, M, T'
Numeric
  • 'cat_idx: 1, 2, 3'
  • 'solutions: 1, 2, 3'
  • 'metadata.formats.total: 8, 7, 6, 9, 5, 11, 10, 12, 3, 4'
URI
Letter grade
  • 'fte_grade: B+, B, B/C, A, A-, None, C, A/B, B-, A+, C/D'
  • 'fte_grade: B+, B, B/C, A, A-, None, C, A/B, B-, A+, C/D'
  • 'fte_grade: B/C, B-, A-, A, B+, B, C/D, A/B, A+, C+, None, F'
Month Name
  • 'month: Jan, Feb, Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec'
  • 'Month: JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC'
  • 'bibliography.publication.month name: June, November, March, August, January, October, July, February, April, May, September, December'
Age
  • 'Person.Age: 53, 47, 23, 32, 39, 18, 22, 35, 34, 25, 31, 41, 30, 37, 28, 42, 36, 49, 71, 33'
  • 'Age: Under 5 Years, 5 to 17 Years, 18 to 24 Years, 25 to 34 Years, 35 to 44 Years, 45 to 54 Years, 55 to 59 Years, 60 & 61 Years, 62 to 64 Years, 65 to 74 Years, 75 Years & Over'
  • 'demographics.age: 40, 45, 58, 65, 70, 74, 0, 48, 77, 68, 56, 83, 71, 69, 44, 78, 73, 67, 53, 61'
Partial timestamp
  • 'Last Known Eruption: 8300 BCE, 4040 BCE, Unknown, 3600 BCE, 1282 CE, 104 BCE, 1538 CE, 1944 CE, 1302 CE, 8040 BCE, 2019 CE, 1230 CE, 1890 CE, 1867 CE, 1891 CE, 1050 BCE, 258 BCE, 140 CE, 1950 CE, 1888 CE'
  • 'created_at: 12/17/20 21:39, 6/14/21 15:36, 11/2/20 09:02, 11/2/20 12:49, 11/2/20 19:02, 11/2/20 14:04, 11/2/20 17:37, 11/2/20 18:39, 11/2/20 18:40, 11/4/20 09:17, 11/4/20 10:29, 11/4/20 10:32, 11/4/20 10:38, 11/4/20 10:39, 11/28/20 21:14, 11/2/20 21:25, 11/2/20 21:32, 11/2/20 22:12, 11/2/20 23:30, 11/2/20 23:33'
  • 'bibliography.publication.full: June, 1998, November, 1999, March, 1994, June 17, 2008, August 16, 2005, August 20, 2006, August 29, 2006, January 10, 2006, March, 2001, June, 2001, October 14, 1892, July, 1998, July, 2003, January, 1994, October 1997, August 16, 2013, February 11, 2006, June 9, 2008, January 1, 1870, April, 2001'
Abbreviation
  • 'bibliography.congress classifications: PR, PS, PZ,PR, PT, PZ,PS, E300, JC, PG, HQ, PQ, PR,PZ, PA,JC, PA, B, TJ, BS, HT, JK, PE, E011'
Country ISO Code
  • "Runner-up Nationality (Men's): None, USA, BRA, AUS, RSA, FRA, CND, RUS, GBR, BEL, GER, ESP, NED, POL, ARG, CZE, YUG, TCH, URS"
  • 'Runner-up Nationality: AUS, GBR, NZL, FRA, USA, RSA, CZE, ARG, GER, SUI, ESP, CRO, ROM, DEN, TCH, URS, CZ, SRB, CND, SWE'
  • 'Champion Nationality: AUS, FRA, GBR, NZL, USA, SRB, SUI, SWE, CZE, ESP, GER, NED, CRO, BRA, RUS'
City Name
  • 'recipient_city: ANCHORAGE, BIRMINGHAM, GUNTERSVILLE, HUNTSVILLE, BENTONVILLE, LITTLE ROCK, CONWAY, BENTON, MAUMELLE, ROGERS, JONESBORO, PHOENIX, TEMPE, SCOTTSDALE, CAVE CREEK, PHEONIX, CHANDLER, FLAGSTAFF, PARADISE VALLEY, SAFFORD'
  • 'Incident.Location.City: Shelton, Aloha, Wichita, San Francisco, Evans, Guthrie, Chandler, Assaria, Burlington, Knoxville, Stockton, Freeport, Columbus, Des Moines, New Orleans, Huntley, Salt Lake City, Strong, Syracuse, England'
  • 'Facility.City: Dothan, Boaz, Florence, Opp, Luverne, Birmingham, Fort Payne, Alabaster, Sheffield, Ozark, Centre, Montgomery, Opelika, Wedowee, Tallassee, Cullman, Andalusia, Anniston, Huntsville, Gadsden'
Continents
  • 'Continent: Africa, South America, Asia, North America, Australia'
Postal Code
  • 'Code postal: 77700.0, nan'
Marital status
  • 'married: single, married'
  • 'never_married: 0, 1'
First Name
  • 'Top Name: Mary, Linda, Debra, Lisa, Michelle, Jennifer, Jessica, Samantha, Ashley, Hannah, Emily, Madison, Emma, Isabella, Sophia, Olivia, John, Robert, James, David'
Currency Code
  • 'cur_name: AFN, DZD, AOA, ARS, AMD, AZN, BDT, INR, BYR, XOF, BTN, BOB, BIF, KHR, XAF, CVE, CNY, COP, USD, CDF'

Evaluation

Metrics

Label Accuracy
all 0.7630

Uses

Direct Use for Inference

First install the SetFit library:

pip install setfit

Then you can load this model and run inference.

from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("quantisan/paraphrase-MiniLM-L3-v2-93dataset-v2labels")
# Run inference
preds = model("variety: Western, Eastern")

Training Details

Training Set Metrics

Training set Min Median Max
Word count 2 22.1604 378
Label Training Sample Count
Categorical 8
Numeric 8
Timestamp 5
Date 8
Integer 8
Partial timestamp 3
Short text 8
Very short text 3
AM/PM 1
Boolean 8
City Name 4
Color 3
Company Name 1
Coordinate 1
Country ISO Code 3
Country Name 8
Currency Code 1
Day of Month 3
Day of Week 2
First Name 1
Floating Point Number 8
Full Name 8
Last Name 1
Latitude 4
License Plate 1
Longitude 4
Month Name 4
Month Number 4
Occupation 3
Postal Code 1
Price 1
Secondary Address 1
Slug 8
Street Address 1
Street Name 2
Time 1
U.S. State 8
U.S. State Abbreviation 6
URI 1
URL 8
Year 8
Zip Code 3
Likert scale 8
Gender 8
Letter grade 4
Race/Ethnicity 3
Marital status 2
Continents 1
Region 5
Age 3
Place 1
Abbreviation 1
Location 3

Training Hyperparameters

  • batch_size: (8, 8)
  • num_epochs: (4, 4)
  • max_steps: -1
  • sampling_strategy: oversampling
  • body_learning_rate: (2e-05, 1e-05)
  • head_learning_rate: 0.01
  • loss: CosineSimilarityLoss
  • distance_metric: cosine_distance
  • margin: 0.25
  • end_to_end: False
  • use_amp: False
  • warmup_proportion: 0.1
  • l2_weight: 0.01
  • seed: 42
  • eval_max_steps: -1
  • load_best_model_at_end: True

Training Results

Epoch Step Training Loss Validation Loss
0.0002 1 0.1497 -
0.0092 50 0.1834 -
0.0183 100 0.1917 -
0.0275 150 0.1712 -
0.0366 200 0.1505 -
0.0458 250 0.146 -
0.0549 300 0.1465 -
0.0641 350 0.1297 -
0.0732 400 0.1238 -
0.0824 450 0.111 -
0.0916 500 0.1035 -
0.1007 550 0.1008 -
0.1099 600 0.0914 -
0.1190 650 0.0869 -
0.1282 700 0.0792 -
0.1373 750 0.0712 -
0.1465 800 0.0709 -
0.1556 850 0.0808 -
0.1648 900 0.0659 -
0.1740 950 0.0611 -
0.1831 1000 0.0611 -
0.1923 1050 0.0607 -
0.2014 1100 0.0611 -
0.2106 1150 0.0507 -
0.2197 1200 0.0577 -
0.2289 1250 0.0508 -
0.2381 1300 0.0399 -
0.2472 1350 0.0442 -
0.2564 1400 0.0516 -
0.2655 1450 0.0441 -
0.2747 1500 0.0472 -
0.2838 1550 0.0284 -
0.2930 1600 0.0492 -
0.3021 1650 0.035 -
0.3113 1700 0.0338 -
0.3205 1750 0.0286 -
0.3296 1800 0.0296 -
0.3388 1850 0.0328 -
0.3479 1900 0.0277 -
0.3571 1950 0.0269 -
0.3662 2000 0.0262 -
0.3754 2050 0.0311 -
0.3845 2100 0.0277 -
0.3937 2150 0.022 -
0.4029 2200 0.0216 -
0.4120 2250 0.0213 -
0.4212 2300 0.0231 -
0.4303 2350 0.0255 -
0.4395 2400 0.02 -
0.4486 2450 0.0181 -
0.4578 2500 0.0196 -
0.4669 2550 0.0182 -
0.4761 2600 0.0199 -
0.4853 2650 0.0171 -
0.4944 2700 0.0171 -
0.5036 2750 0.0169 -
0.5127 2800 0.0161 -
0.5219 2850 0.0104 -
0.5310 2900 0.0133 -
0.5402 2950 0.0137 -
0.5493 3000 0.0241 -
0.5585 3050 0.0156 -
0.5677 3100 0.0155 -
0.5768 3150 0.0158 -
0.5860 3200 0.0165 -
0.5951 3250 0.0141 -
0.6043 3300 0.0129 -
0.6134 3350 0.0129 -
0.6226 3400 0.0103 -
0.6318 3450 0.011 -
0.6409 3500 0.0117 -
0.6501 3550 0.0128 -
0.6592 3600 0.0125 -
0.6684 3650 0.0138 -
0.6775 3700 0.0101 -
0.6867 3750 0.0123 -
0.6958 3800 0.0127 -
0.7050 3850 0.0088 -
0.7142 3900 0.0097 -
0.7233 3950 0.0078 -
0.7325 4000 0.0056 -
0.7416 4050 0.0096 -
0.7508 4100 0.0114 -
0.7599 4150 0.0105 -
0.7691 4200 0.0101 -
0.7782 4250 0.0077 -
0.7874 4300 0.0104 -
0.7966 4350 0.007 -
0.8057 4400 0.0112 -
0.8149 4450 0.008 -
0.8240 4500 0.0063 -
0.8332 4550 0.0153 -
0.8423 4600 0.0081 -
0.8515 4650 0.007 -
0.8606 4700 0.0052 -
0.8698 4750 0.0054 -
0.8790 4800 0.0063 -
0.8881 4850 0.0131 -
0.8973 4900 0.0086 -
0.9064 4950 0.0086 -
0.9156 5000 0.008 -
0.9247 5050 0.0097 -
0.9339 5100 0.0081 -
0.9431 5150 0.0052 -
0.9522 5200 0.008 -
0.9614 5250 0.0055 -
0.9705 5300 0.0048 -
0.9797 5350 0.0055 -
0.9888 5400 0.0064 -
0.9980 5450 0.0043 -
1.0 5461 - 0.0926
1.0071 5500 0.0064 -
1.0163 5550 0.0079 -
1.0255 5600 0.0037 -
1.0346 5650 0.0045 -
1.0438 5700 0.0072 -
1.0529 5750 0.0055 -
1.0621 5800 0.0046 -
1.0712 5850 0.0039 -
1.0804 5900 0.0063 -
1.0895 5950 0.0071 -
1.0987 6000 0.005 -
1.1079 6050 0.0066 -
1.1170 6100 0.0041 -
1.1262 6150 0.0056 -
1.1353 6200 0.0063 -
1.1445 6250 0.0057 -
1.1536 6300 0.004 -
1.1628 6350 0.0058 -
1.1719 6400 0.0067 -
1.1811 6450 0.0058 -
1.1903 6500 0.0081 -
1.1994 6550 0.0062 -
1.2086 6600 0.0062 -
1.2177 6650 0.0034 -
1.2269 6700 0.0031 -
1.2360 6750 0.0048 -
1.2452 6800 0.006 -
1.2543 6850 0.0054 -
1.2635 6900 0.007 -
1.2727 6950 0.0064 -
1.2818 7000 0.0055 -
1.2910 7050 0.0049 -
1.3001 7100 0.0063 -
1.3093 7150 0.0044 -
1.3184 7200 0.0063 -
1.3276 7250 0.003 -
1.3368 7300 0.0049 -
1.3459 7350 0.0047 -
1.3551 7400 0.0043 -
1.3642 7450 0.0023 -
1.3734 7500 0.0025 -
1.3825 7550 0.0047 -
1.3917 7600 0.0027 -
1.4008 7650 0.0036 -
1.4100 7700 0.0026 -
1.4192 7750 0.0019 -
1.4283 7800 0.0048 -
1.4375 7850 0.0047 -
1.4466 7900 0.0041 -
1.4558 7950 0.0073 -
1.4649 8000 0.0023 -
1.4741 8050 0.0054 -
1.4832 8100 0.0042 -
1.4924 8150 0.0078 -
1.5016 8200 0.0063 -
1.5107 8250 0.0033 -
1.5199 8300 0.0055 -
1.5290 8350 0.0043 -
1.5382 8400 0.0027 -
1.5473 8450 0.0021 -
1.5565 8500 0.0022 -
1.5656 8550 0.0063 -
1.5748 8600 0.0049 -
1.5840 8650 0.0049 -
1.5931 8700 0.0057 -
1.6023 8750 0.0035 -
1.6114 8800 0.0022 -
1.6206 8850 0.0029 -
1.6297 8900 0.0062 -
1.6389 8950 0.0022 -
1.6480 9000 0.0047 -
1.6572 9050 0.0024 -
1.6664 9100 0.0053 -
1.6755 9150 0.0021 -
1.6847 9200 0.0029 -
1.6938 9250 0.0031 -
1.7030 9300 0.0024 -
1.7121 9350 0.0034 -
1.7213 9400 0.0021 -
1.7305 9450 0.0025 -
1.7396 9500 0.0023 -
1.7488 9550 0.0029 -
1.7579 9600 0.0025 -
1.7671 9650 0.0021 -
1.7762 9700 0.0019 -
1.7854 9750 0.0034 -
1.7945 9800 0.0016 -
1.8037 9850 0.0019 -
1.8129 9900 0.0024 -
1.8220 9950 0.002 -
1.8312 10000 0.0021 -
1.8403 10050 0.0061 -
1.8495 10100 0.0019 -
1.8586 10150 0.0014 -
1.8678 10200 0.0021 -
1.8769 10250 0.0031 -
1.8861 10300 0.002 -
1.8953 10350 0.0014 -
1.9044 10400 0.0015 -
1.9136 10450 0.0014 -
1.9227 10500 0.0018 -
1.9319 10550 0.0014 -
1.9410 10600 0.0015 -
1.9502 10650 0.0014 -
1.9593 10700 0.0013 -
1.9685 10750 0.0032 -
1.9777 10800 0.0017 -
1.9868 10850 0.0015 -
1.9960 10900 0.0012 -
2.0 10922 - 0.1071
2.0051 10950 0.0013 -
2.0143 11000 0.0013 -
2.0234 11050 0.0015 -
2.0326 11100 0.0013 -
2.0418 11150 0.0013 -
2.0509 11200 0.0011 -
2.0601 11250 0.0013 -
2.0692 11300 0.0013 -
2.0784 11350 0.0034 -
2.0875 11400 0.0012 -
2.0967 11450 0.0012 -
2.1058 11500 0.0025 -
2.1150 11550 0.0026 -
2.1242 11600 0.0031 -
2.1333 11650 0.0012 -
2.1425 11700 0.0011 -
2.1516 11750 0.0013 -
2.1608 11800 0.0012 -
2.1699 11850 0.0013 -
2.1791 11900 0.0011 -
2.1882 11950 0.0011 -
2.1974 12000 0.0012 -
2.2066 12050 0.0014 -
2.2157 12100 0.003 -
2.2249 12150 0.001 -
2.2340 12200 0.0011 -
2.2432 12250 0.0028 -
2.2523 12300 0.0027 -
2.2615 12350 0.0013 -
2.2706 12400 0.0024 -
2.2798 12450 0.0011 -
2.2890 12500 0.001 -
2.2981 12550 0.0011 -
2.3073 12600 0.0011 -
2.3164 12650 0.0029 -
2.3256 12700 0.0029 -
2.3347 12750 0.0009 -
2.3439 12800 0.0013 -
2.3530 12850 0.0009 -
2.3622 12900 0.001 -
2.3714 12950 0.0011 -
2.3805 13000 0.0027 -
2.3897 13050 0.0009 -
2.3988 13100 0.0011 -
2.4080 13150 0.0012 -
2.4171 13200 0.0024 -
2.4263 13250 0.0039 -
2.4355 13300 0.001 -
2.4446 13350 0.0017 -
2.4538 13400 0.0012 -
2.4629 13450 0.0021 -
2.4721 13500 0.0021 -
2.4812 13550 0.0032 -
2.4904 13600 0.0012 -
2.4995 13650 0.0012 -
2.5087 13700 0.0014 -
2.5179 13750 0.001 -
2.5270 13800 0.0011 -
2.5362 13850 0.0009 -
2.5453 13900 0.0034 -
2.5545 13950 0.0015 -
2.5636 14000 0.0013 -
2.5728 14050 0.0069 -
2.5819 14100 0.001 -
2.5911 14150 0.0034 -
2.6003 14200 0.0028 -
2.6094 14250 0.001 -
2.6186 14300 0.0012 -
2.6277 14350 0.0013 -
2.6369 14400 0.0011 -
2.6460 14450 0.0009 -
2.6552 14500 0.001 -
2.6643 14550 0.0009 -
2.6735 14600 0.0012 -
2.6827 14650 0.0041 -
2.6918 14700 0.0008 -
2.7010 14750 0.0019 -
2.7101 14800 0.001 -
2.7193 14850 0.0012 -
2.7284 14900 0.0013 -
2.7376 14950 0.0012 -
2.7467 15000 0.0019 -
2.7559 15050 0.0009 -
2.7651 15100 0.0009 -
2.7742 15150 0.0008 -
2.7834 15200 0.0028 -
2.7925 15250 0.0009 -
2.8017 15300 0.0011 -
2.8108 15350 0.0029 -
2.8200 15400 0.0008 -
2.8292 15450 0.001 -
2.8383 15500 0.0019 -
2.8475 15550 0.0011 -
2.8566 15600 0.0022 -
2.8658 15650 0.0011 -
2.8749 15700 0.0009 -
2.8841 15750 0.0008 -
2.8932 15800 0.0009 -
2.9024 15850 0.0009 -
2.9116 15900 0.0011 -
2.9207 15950 0.0011 -
2.9299 16000 0.0017 -
2.9390 16050 0.001 -
2.9482 16100 0.0008 -
2.9573 16150 0.0009 -
2.9665 16200 0.0008 -
2.9756 16250 0.0009 -
2.9848 16300 0.0007 -
2.9940 16350 0.0011 -
3.0 16383 - 0.0990
3.0031 16400 0.0008 -
3.0123 16450 0.0008 -
3.0214 16500 0.0008 -
3.0306 16550 0.0008 -
3.0397 16600 0.0015 -
3.0489 16650 0.0007 -
3.0580 16700 0.0008 -
3.0672 16750 0.0009 -
3.0764 16800 0.0008 -
3.0855 16850 0.0008 -
3.0947 16900 0.0023 -
3.1038 16950 0.0007 -
3.1130 17000 0.0006 -
3.1221 17050 0.0024 -
3.1313 17100 0.0008 -
3.1405 17150 0.0017 -
3.1496 17200 0.0011 -
3.1588 17250 0.0008 -
3.1679 17300 0.0008 -
3.1771 17350 0.0007 -
3.1862 17400 0.0014 -
3.1954 17450 0.0008 -
3.2045 17500 0.0007 -
3.2137 17550 0.0007 -
3.2229 17600 0.0006 -
3.2320 17650 0.0007 -
3.2412 17700 0.0021 -
3.2503 17750 0.0006 -
3.2595 17800 0.0006 -
3.2686 17850 0.0007 -
3.2778 17900 0.0006 -
3.2869 17950 0.0008 -
3.2961 18000 0.0008 -
3.3053 18050 0.0008 -
3.3144 18100 0.0027 -
3.3236 18150 0.0008 -
3.3327 18200 0.0007 -
3.3419 18250 0.0007 -
3.3510 18300 0.0008 -
3.3602 18350 0.0007 -
3.3693 18400 0.0022 -
3.3785 18450 0.0007 -
3.3877 18500 0.0014 -
3.3968 18550 0.0006 -
3.4060 18600 0.0016 -
3.4151 18650 0.0007 -
3.4243 18700 0.0015 -
3.4334 18750 0.0006 -
3.4426 18800 0.001 -
3.4517 18850 0.0008 -
3.4609 18900 0.0008 -
3.4701 18950 0.0007 -
3.4792 19000 0.0015 -
3.4884 19050 0.0007 -
3.4975 19100 0.0006 -
3.5067 19150 0.0007 -
3.5158 19200 0.0014 -
3.5250 19250 0.0006 -
3.5342 19300 0.0011 -
3.5433 19350 0.0008 -
3.5525 19400 0.0007 -
3.5616 19450 0.0008 -
3.5708 19500 0.0021 -
3.5799 19550 0.0007 -
3.5891 19600 0.0007 -
3.5982 19650 0.0006 -
3.6074 19700 0.0007 -
3.6166 19750 0.0007 -
3.6257 19800 0.0007 -
3.6349 19850 0.001 -
3.6440 19900 0.0011 -
3.6532 19950 0.0007 -
3.6623 20000 0.0006 -
3.6715 20050 0.0022 -
3.6806 20100 0.0011 -
3.6898 20150 0.0007 -
3.6990 20200 0.0006 -
3.7081 20250 0.0007 -
3.7173 20300 0.0006 -
3.7264 20350 0.0006 -
3.7356 20400 0.0013 -
3.7447 20450 0.0009 -
3.7539 20500 0.0006 -
3.7630 20550 0.001 -
3.7722 20600 0.0007 -
3.7814 20650 0.0007 -
3.7905 20700 0.0006 -
3.7997 20750 0.0006 -
3.8088 20800 0.0015 -
3.8180 20850 0.0009 -
3.8271 20900 0.0009 -
3.8363 20950 0.0005 -
3.8454 21000 0.0008 -
3.8546 21050 0.0006 -
3.8638 21100 0.0008 -
3.8729 21150 0.0006 -
3.8821 21200 0.0006 -
3.8912 21250 0.0005 -
3.9004 21300 0.0006 -
3.9095 21350 0.0015 -
3.9187 21400 0.0017 -
3.9279 21450 0.0006 -
3.9370 21500 0.0007 -
3.9462 21550 0.0014 -
3.9553 21600 0.0012 -
3.9645 21650 0.0017 -
3.9736 21700 0.0008 -
3.9828 21750 0.0006 -
3.9919 21800 0.0006 -
4.0 21844 - 0.1004

Framework Versions

  • Python: 3.11.10
  • SetFit: 1.1.0
  • Sentence Transformers: 3.2.0
  • Transformers: 4.45.2
  • PyTorch: 2.4.1+cu124
  • Datasets: 3.0.1
  • Tokenizers: 0.20.1

Citation

BibTeX 

@article{https://doi.org/10.48550/arxiv.2209.11055,
    doi = {10.48550/ARXIV.2209.11055},
    url = {https://arxiv.org/abs/2209.11055},
    author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    title = {Efficient Few-Shot Learning Without Prompts},
    publisher = {arXiv},
    year = {2022},
    copyright = {Creative Commons Attribution 4.0 International}
}