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--- |
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base_model: sentence-transformers/paraphrase-MiniLM-L3-v2 |
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library_name: setfit |
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metrics: |
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- accuracy |
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pipeline_tag: text-classification |
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tags: |
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- setfit |
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- sentence-transformers |
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- text-classification |
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- generated_from_setfit_trainer |
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widget: |
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- text: 'metrics.statistics.polysyllables: 4603, 102, 339, 2604, 397, 1180, 555, 1488, |
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1226, 378, 6639, 1978, 4088, 7005, 3256, 86, 2338, 1905, 1647, 16369' |
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- text: 'company.relationship: founder, None, founder/chairman, Relation, relation, |
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CEO, chairman, investor, founder and CEO, founder/CEO, owner, chairman of management |
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committee, founder and chairman, Chairman and Chief Executive Officer, general |
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director, executive chairman, Chairman/founder, founder, chairman, ceo, former |
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chairman and CEO, relation and chairman' |
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- text: 'variety: Western, Eastern' |
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- text: 'Data.Fat.Saturated Fat: 2.009, 1.164, 1.86, 2.154, 0.568, 0.117, 1.11, 0.049, |
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0.66, 1.242, 1.899, 0.596, 2.667, 0.044, 2.554, 0.633, 4.591, 1.214, 0.121, 5.486' |
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- text: 'Date.Full: 8/26/1990, 3/24/1991, 3/31/1991, 4/7/1991, 4/14/1991, 4/21/1991, |
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4/28/1991, 5/5/1991, 5/12/1991, 5/19/1991, 5/26/1991, 6/2/1991, 6/9/1991, 6/16/1991, |
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6/23/1991, 6/30/1991, 7/7/1991, 7/14/1991, 7/21/1991, 7/28/1991' |
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inference: true |
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model-index: |
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- name: SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 |
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results: |
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- task: |
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type: text-classification |
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name: Text Classification |
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dataset: |
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name: Unknown |
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type: unknown |
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split: test |
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metrics: |
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- type: accuracy |
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value: 0.7512388503468781 |
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name: Accuracy |
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--- |
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# SetFit with sentence-transformers/paraphrase-MiniLM-L3-v2 |
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This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification. |
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The model has been trained using an efficient few-shot learning technique that involves: |
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1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning. |
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2. Training a classification head with features from the fine-tuned Sentence Transformer. |
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## Model Details |
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### Model Description |
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- **Model Type:** SetFit |
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- **Sentence Transformer body:** [sentence-transformers/paraphrase-MiniLM-L3-v2](https://huggingface.co/sentence-transformers/paraphrase-MiniLM-L3-v2) |
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- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance |
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- **Maximum Sequence Length:** 128 tokens |
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- **Number of Classes:** 39 classes |
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<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) --> |
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<!-- - **Language:** Unknown --> |
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<!-- - **License:** Unknown --> |
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### Model Sources |
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- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit) |
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- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055) |
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- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit) |
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### Model Labels |
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| Label | Examples | |
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|:------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| Month Number | <ul><li>'Incident.Date.Month: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12'</li><li>'bibliography.publication.month: 6, 11, 3, 8, 1, 10, 7, 2, 4, 5, 9, 12'</li><li>'mp_month: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12'</li></ul> | |
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| Date | <ul><li>'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'</li><li>'STOP_FRISK_DATE: 1/16/2017, 2/8/2017, 2/20/2017, 2/21/2017, 2/17/2017, 2/25/2017, 3/3/2017, 3/16/2017, 3/31/2017, 4/2/2017, 4/4/2017, 3/24/2017, 4/6/2017, 4/18/2017, 5/6/2017, 5/10/2017, 5/17/2017, 5/7/2017, 5/24/2017, 6/8/2017'</li><li>'start_date: 10/31/20, 10/30/20, 10/29/20, 10/27/20, 10/23/20, 10/28/20, 10/26/20, 10/24/20, 10/8/20, 10/22/20, 10/19/20, 10/21/20, 10/11/20, 10/20/20, 10/16/20, 10/18/20, 10/15/20, 10/12/20, 10/13/20, 10/14/20'</li></ul> | |
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| Categorical | <ul><li>'STOP_LOCATION_PATROL_BORO_NAME: PBMS, (nul, 986, 5 AV, PBMN, PBBX, 238, 233, 1011, 237, PBBS, 991, 154, PBBN, PBQN, PBQS, 183, 1022, 1025, 220'</li><li>'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'</li><li>'Procedure.Heart Attack.Quality: Average, Unknown, Worse, Better'</li></ul> | |
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| Year | <ul><li>'Year: 1998, 1997, 1996, 1995, 1993, 1994'</li><li>'cycle: 2020'</li><li>'metadata.acquisition date: 2009, 1924, 2010, 1968, 1982, 1997, 2012, 1983, 1996, 1900, 1990, 1995, 1931, 1960, 1966, 1955, 1993, 1979, 2001, 2011'</li></ul> | |
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| Longitude | <ul><li>'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'</li><li>'long: 40.65531753386127, 35.52146509142811, 41.04610174058556, 37.25718863973695, 37.73038191275334, 38.78755702518432, 36.31538469187874, 38.3542649521305, 40.33741738725765, 36.831052736369664, 37.39711396680899, 38.28297641253209, 40.25037415629944, 39.12501528359793, 40.179108531876246, 38.165405118101205, 40.28234452941448, 37.1590112746327, 40.08056518798263, 38.45329795732872'</li><li>'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'</li></ul> | |
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| Floating Point Number | <ul><li>'ins_premium: 784.55, 1053.48, 899.47, 827.34, 878.41, 835.5, 1068.73, 1137.87, 1273.89, 1160.13, 913.15, 861.18, 641.96, 803.11, 710.46, 649.06, 780.45, 872.51, 1281.55, 661.88'</li><li>'Data.Vitamins.Vitamin B12: 0.05, 0.56, 0.54, 0.36, 0.61, 0.38, 0.55, 0.58, 0.22, 0.37, 0.46, 0.3, 0.07, 0.5, 0.35, 0.16, 0.24, 0.44, 0.29, 0.85'</li><li>'Average Wage Appx MOE: 103382.66673939777, 116573.18275172487, 108787.21048470394, 118363.44825256945, 100311.98088082067, 27560.471912039546, 27835.56534877041, 27020.999829170632, 100720.90656498625, 26279.88466481348, 26033.491463487928, 25918.56067562003, 25523.8518942556, 68486.51231657575, 98614.90773401306, 63965.94799270596, 59979.385203569575, 67171.5207463371, 84156.83712285856, 76854.95603440655'</li></ul> | |
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| Slug | <ul><li>'Slug Geography: united-states, iowa, michigan, minnesota, north-dakota, south-dakota, wisconsin, minneapolis-st-paul-bloomington-mn-wi'</li><li>'Slug 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'</li><li>'Slug Geography: united-states, arizona, california, nevada, oregon, los-angeles-long-beach-anaheim-ca, riverside-san-bernardino-ontario-ca, san-diego-carlsbad-ca, san-francisco-oakland-hayward-ca'</li></ul> | |
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| U.S. State Abbreviation | <ul><li>'State: AK, AL, AR, AZ, CA, CO, CT, DC, DE, FL, GA, HI, IA, ID, IL, IN, KS, KY, LA, MA'</li><li>'abbrev: AL, AK, AZ, AR, CA, CO, CT, DE, DC, FL, GA, HI, ID, IL, IN, IA, KS, KY, LA, ME'</li><li>'Facility.State: AL, AK, AZ, AR, CA, CO, CT, DE, DC, FL, GA, HI, ID, IL, IN, IA, KS, KY, LA, ME'</li></ul> | |
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| Month Name | <ul><li>'Month: JAN, FEB, MAR, APR, MAY, JUN, JUL, AUG, SEP, OCT, NOV, DEC'</li><li>'MONTH2: January, February, March, April, May, June, July, August, September, October, November, December'</li><li>'MONTH2: January, February, March, April, May, June, July, August, September, October, November, December'</li></ul> | |
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| Day of Month | <ul><li>'bibliography.publication.day: 1, 17, 16, 20, 29, 10, 14, 11, 9, 18, 19, 22, 25, 15, 6, 28, 27, 2, 12, 21'</li><li>'Date.Day: 26, 24, 31, 7, 14, 21, 28, 5, 12, 19, 2, 9, 16, 23, 30, 4, 11, 18, 25, 1'</li><li>'bibliography.publication.day: 1, 17, 16, 20, 29, 10, 14, 11, 9, 18, 19, 22, 25, 15, 6, 28, 27, 2, 12, 21'</li></ul> | |
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| Currency Code | <ul><li>'cur_name: AFN, DZD, AOA, ARS, AMD, AZN, BDT, INR, BYR, XOF, BTN, BOB, BIF, KHR, XAF, CVE, CNY, COP, USD, CDF'</li></ul> | |
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| Last Name | <ul><li>'answer: Spanberger, Freitas, Eastman, Bacon, Schaeffer, Schupp, Wagner, Schulte, Balter, Katko, Williams, Hale, Spartz, Tucker, Elliott, Hill, Golden, Crafts, Newman, Fricilone'</li><li>'candidat: Bush, Perot, Clinton'</li></ul> | |
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| Timestamp | <ul><li>'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'</li><li>'created_at: 12/21/22 09:28, 12/21/22 12:52, 12/16/22 18:27, 12/16/22 21:10, 12/14/22 10:39, 12/14/22 08:22, 12/15/22 18:31, 12/14/22 14:13, 12/13/22 09:36, 12/14/22 08:23, 12/14/22 15:40, 12/15/22 09:40, 12/7/22 10:47, 12/7/22 08:17, 12/7/22 17:56, 12/15/22 09:50, 11/30/22 09:25, 11/23/22 08:46, 12/1/22 09:39, 12/5/22 08:29'</li><li>'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'</li></ul> | |
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| Day of Week | <ul><li>'DAY2: Monday, Wednesday, Tuesday, Friday, Saturday, Thursday, Sunday'</li><li>'DAY2: Monday, Wednesday, Tuesday, Friday, Saturday, Thursday, Sunday'</li><li>'day: Sun, Sat, Thur, Fri'</li></ul> | |
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| Integer | <ul><li>'RF: 91.0, 92.0, 88.0, nan, 87.0, 81.0, 84.0, 70.0, 85.0, 83.0, 55.0, 86.0, 66.0, 62.0, 69.0, 82.0, 76.0, 77.0, 79.0, 68.0'</li><li>'GK diving: 7.0, 6.0, 9.0, 27.0, 91.0, 15.0, 90.0, 11.0, 10.0, 5.0, 85.0, 13.0, 3.0, 89.0, 84.0, 14.0, 2.0, 88.0, 12.0, 4.0'</li><li>'Household Income by Race Moe: 128.0, 286.0, 270.0, 445.0, 390.0, 315.0, 496.0, 734.0, 791.0, 135.0, 266.0, 231.99999999999997, 409.0, 304.0, 326.0, 488.0, 723.0, 531.0, 140.0, 275.0'</li></ul> | |
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| Street Address | <ul><li>'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'</li><li>'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'</li><li>'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'</li></ul> | |
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| URL | <ul><li>'Flag: https://cdn.sofifa.org/flags/38.png, https://cdn.sofifa.org/flags/52.png, https://cdn.sofifa.org/flags/54.png, https://cdn.sofifa.org/flags/60.png, https://cdn.sofifa.org/flags/21.png, https://cdn.sofifa.org/flags/37.png, https://cdn.sofifa.org/flags/45.png, https://cdn.sofifa.org/flags/7.png, https://cdn.sofifa.org/flags/55.png, https://cdn.sofifa.org/flags/10.png, https://cdn.sofifa.org/flags/50.png, https://cdn.sofifa.org/flags/27.png, https://cdn.sofifa.org/flags/44.png, https://cdn.sofifa.org/flags/18.png, https://cdn.sofifa.org/flags/115.png, https://cdn.sofifa.org/flags/46.png, https://cdn.sofifa.org/flags/34.png, https://cdn.sofifa.org/flags/13.png, https://cdn.sofifa.org/flags/43.png, https://cdn.sofifa.org/flags/14.png'</li><li>'Club Logo: https://cdn.sofifa.org/24/18/teams/243.png, https://cdn.sofifa.org/24/18/teams/241.png, https://cdn.sofifa.org/24/18/teams/73.png, https://cdn.sofifa.org/24/18/teams/21.png, https://cdn.sofifa.org/24/18/teams/11.png, https://cdn.sofifa.org/24/18/teams/5.png, https://cdn.sofifa.org/24/18/teams/45.png, https://cdn.sofifa.org/24/18/teams/10.png, https://cdn.sofifa.org/24/18/teams/1.png, https://cdn.sofifa.org/24/18/teams/240.png, https://cdn.sofifa.org/24/18/teams/22.png, https://cdn.sofifa.org/24/18/teams/47.png, https://cdn.sofifa.org/24/18/teams/18.png, https://cdn.sofifa.org/24/18/teams/48.png, https://cdn.sofifa.org/24/18/teams/44.png, https://cdn.sofifa.org/24/18/teams/9.png, https://cdn.sofifa.org/24/18/teams/52.png, https://cdn.sofifa.org/24/18/teams/327.png, https://cdn.sofifa.org/24/18/teams/69.png, https://cdn.sofifa.org/24/18/teams/32.png'</li><li>'url: https://github.com/optimus-forecasting-and-polling/0ptimus-SC-VA7-November-2020/blob/main/virginia_cd_7_poll_toplines_tl_31_october_2020.pdf, https://projects.fivethirtyeight.com/polls/20201102_NE_UNLV.pdf, https://docs.google.com/spreadsheets/d/157HttGCHSc-6qx_hAWLg_KCrggbAUCbatOXH3WipShI/edit#gid=0, https://docs.google.com/spreadsheets/d/1RQaEpV6kT3qV2b5P2B_AQgnS2CAJdgJaioH_10hO4qU/edit#gid=1652054673, https://docs.google.com/spreadsheets/d/1sSzvXk5e3hpqSJiYtKN95Bm3VInlB_XtlWV8JQ57780/edit#gid=0, https://docs.google.com/spreadsheets/d/1iWSrZDo1yUpPUkqXffKUsO4w-lGKqhxb5aQGG-fU1Dw/edit#gid=1692435149, https://docs.google.com/spreadsheets/d/19LDCjGkae7_DJHp2KMN79Uq0aukFrQRibJLDjriTMqY/edit#gid=1236548106, https://docs.google.com/spreadsheets/d/1eBWQtr-cx1ECGu-dsFcF_VdhnFfyRPd3qz3zcTmhdFY/edit#gid=1166077921, https://projects.fivethirtyeight.com/polls/20201104_IL3_Victory.pdf, https://projects.fivethirtyeight.com/polls/20201104_IL6_Victory.pdf, https://projects.fivethirtyeight.com/polls/20201104_IL17_Victory.pdf, https://docs.google.com/spreadsheets/d/1C_aw1txi2S5CeUze-vC6TR28M5k0n0RcBGGRUlQEpDQ/edit#gid=2044611796, https://docs.google.com/spreadsheets/d/1RLsioeQZMNIHBeH1xo3nCGherCVpenMxoUxXGKaHUqw/edit#gid=1882933718, https://emersonpolling.reportablenews.com/pr/super-poll-sunday-pregame-polls-show-midwest-shift-for-biden-in-wisconsin-nebraska-s-2nd-district-and-vigo-county-indiana, https://outlook.office.com/mail/inbox/id/AAQkADg2MDVmMmNjLTRhYmUtNDA2NC04NzUzLWZlNWIxZGRkYmM3NQAQAO1Uc%2BZbJvFIsCShNRkrNvQ%3D, https://www.telemundopr.com/noticias/puerto-rico/https-www-telemundopr-com-noticias-puerto-rico-voto-2020-la-encuesta-final-elecciones-2020-puerto-rico-2143435-2/2143435/, https://314action.org/wp-content/uploads/2020/10/NCCD9Results1-1.pdf, https://www.abqjournal.com/1512949/extremely-close-race-for-2nd-congressional-district-ex-pollster-notes-race-between-torres-small-and-herrell-comes-down-to-turnout.html, http://stpetepolls.org/files/StPetePolls_2020_CD13GEN_October28_EH39F.pdf, https://www.scribd.com/document/482078454/Alaska-October-28-2020#from_embed'</li></ul> | |
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| U.S. State | <ul><li>'Geography: Arizona, California, Nevada, Oregon'</li><li>'State: Alabama, Alaska, Arizona, Arkansas, California, Colorado, Connecticut, Delaware, District of Columbia, Florida, Georgia, Hawaii, Idaho, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Maine'</li><li>'Slug Geography: california'</li></ul> | |
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| Zip Code | <ul><li>'recipient_zip: 995084442, 99503, 995163436, 352124572, 35216, 35976, 358021277, 352174710, 35203, 35233, 35805, 72716, 72201, 72035, 72015, 72223, 72019, 72113, 72758, 72227'</li><li>'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'</li><li>'zip_codes: nan, 12081.0, 10090.0, 12423.0, 12420.0'</li></ul> | |
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| Country Name | <ul><li>'Nationality: Portugal, Argentina, Brazil, Uruguay, Germany, Poland, Spain, Belgium, Chile, Croatia, Wales, Italy, Slovenia, France, Gabon, Sweden, Netherlands, Denmark, Slovakia, England'</li><li>'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'</li><li>'Geography: United States'</li></ul> | |
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| Boolean | <ul><li>'ranked_choice_reallocated: False, True'</li><li>'wealth.how.was founder: True'</li><li>'internal: False'</li></ul> | |
|
| Short text | <ul><li>'agestr: Under 5, 5 to 9, 10 to 14, 15 to 19, 20 to 24, 25 to 29, 30 to 34, 35 to 39, 40 to 44, 45 to 49, 50 to 54, 55 to 59, 60 to 64, 65 to 69, 70 to 74, 75 to 79, 80 to 84'</li><li>"Show.Name: Tru, Miss Saigon, A Streetcar Named Desire 92, The Sisters Rosensweig, Beauty And The Beast, A Little More Magic, Broken Glass, Show Boat, Sunset Boulevard, The Shadow Box, Uncle Vanya, Smokey Joe'S Cafe, Having Our Say, Hamlet 95, The Rose Tattoo, A Month In The Country, Arcadia, Cats, Chronicle Of A Death Foretold, How To Succeed In Business Without Really Trying"</li><li>'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'</li></ul> | |
|
| Occupation | <ul><li>'Detailed Occupation: Other managers, Cashiers, Retail salespersons, Driver/sales workers & truck drivers, Registered nurses'</li><li>'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'</li><li>'occupation: Operatives, Craftsmen, Sales, Other, Managers/admin, Professional/technical, Clerical/unskilled, Laborers, Transport, Service, nan, Household workers, Farm laborers, Farmers'</li></ul> | |
|
| Partial timestamp | <ul><li>'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'</li><li>'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'</li><li>'Rating.Experience: Below, Same, None, Above'</li></ul> | |
|
| Street Name | <ul><li>'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'</li><li>'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'</li><li>'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'</li></ul> | |
|
| Full Name | <ul><li>"cand_nm: Rubio, Marco, Santorum, Richard J., Perry, James R. (Rick), Carson, Benjamin S., Cruz, Rafael Edward 'Ted', Paul, Rand, Clinton, Hillary Rodham, Sanders, Bernard, Fiorina, Carly, Huckabee, Mike, Pataki, George E., O'Malley, Martin Joseph, Graham, Lindsey O., Bush, Jeb, Trump, Donald J., Jindal, Bobby, Christie, Christopher J., Walker, Scott, Stein, Jill, Webb, James Henry Jr."</li><li>'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'</li><li>'bibliography.author.name: Austen, Jane, Gilman, Charlotte Perkins, Carroll, Lewis, Shelley, Mary Wollstonecraft, Kafka, Franz, Twain, Mark, Wilde, Oscar, Douglass, Frederick, Ibsen, Henrik, Melville, Herman, Doyle, Arthur Conan, Dickens, Charles, Joyce, James, Swift, Jonathan, Stoker, Bram, Machiavelli, Niccolo, Tolstoy, Leo, graf, Grimm, Wilhelm, Vatsyayana, Unknown'</li></ul> | |
|
| Very short text | <ul><li>'above_ground_sighter_measurement: None, FALSE, 4, 3, 30, 10, 6, 24, 8, 25, 5, 50, 70, 12, 2, 20, 7, 13, 15, 28'</li><li>'status: N, Y, REMOVE, None, 1, ?, H, R, M, T'</li><li>'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?'</li></ul> | |
|
| URI | <ul><li>'url: https://docs.cdn.yougov.com/c5o6xiw8t9/econtoplines.pdf, https://docs.cdn.yougov.com/by8wjw1hur/econTabReport.pdf, https://subscriber.politicopro.com/newsletter/2022/12/who-supports-the-payment-pause-00072673, https://news.yahoo.com/new-poll-shows-stark-partisan-divide-when-it-comes-to-americans-view-of-schools-132510314.html, https://harvardharrispoll.com/wp-content/uploads/2022/12/HHP_Dec2022_KeyResults.pdf, https://echeloninsights.com/in-the-news/december-2022-omnibus-2/, https://docs.cdn.yougov.com/b94ttrxy3v/econtoplines.pdf, https://docs.cdn.yougov.com/urts2xadfd/econTabReport.pdf, https://www.foxnews.com/politics/fox-news-poll-americans-show-little-enthusiasm-biden-trump-rematch-2024, https://poll.qu.edu/poll-release?releaseid=3863, https://www.usatoday.com/story/news/politics/2022/12/13/trump-support-gop-2024-presidential-race-poll/10882346002/, https://www.wsj.com/articles/ron-desantis-holds-early-lead-over-donald-trump-among-gop-primary-voters-wsj-poll-shows-11670989311?mod=hp_lead_pos5, https://www.cnn.com/2022/12/14/politics/biden-trump-2024-poll/index.html, https://docs.cdn.yougov.com/dshfq4wqyr/econtoplines.pdf, https://docs.cdn.yougov.com/qsanp37uhh/econTabReport.pdf, https://www.yahoo.com/news/poll-trump-loses-ground-with-republicans-after-kanye-west-nick-fuentes-dinner-100204385.html, https://docs.cdn.yougov.com/regijt79ge/November_Georgia_Ads_joined_earlier.pdf, https://docs.cdn.yougov.com/xcvx6iu6em/November_Georgia_Ads_joined.pdf, https://news.gallup.com/poll/406892/party-images-stable-midterm-elections.aspx, https://docs.cdn.yougov.com/2zebcvjoec/econtoplines.pdf'</li></ul> | |
|
| Latitude | <ul><li>'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'</li><li>'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'</li><li>'lat: 40.7940823884086, 40.7948509408039, 40.7667178072558, 40.7697032606755, 40.797533370163, 40.7902561000937, 40.7693045133578, 40.7942883045566, 40.7729752391435, 40.7903128889029, 40.7762126854894, 40.7725908847499, 40.7931811701082, 40.7917367820255, 40.7829723919744, 40.7742879599026, 40.7823507678183, 40.7919669739962, 40.7702795904962, 40.7698124821507'</li></ul> | |
|
| Time | <ul><li>'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'</li><li>'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'</li><li>'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'</li></ul> | |
|
| Postal Code | <ul><li>'Code postal: 77700.0, nan'</li></ul> | |
|
| Country ISO Code | <ul><li>'Runner-up Nationality: AUS, GBR, NZL, FRA, USA, RSA, CZE, ARG, GER, SUI, ESP, CRO, ROM, DEN, TCH, URS, CZ, SRB, CND, SWE'</li><li>'Champion Nationality: AUS, FRA, GBR, NZL, USA, SRB, SUI, SWE, CZE, ESP, GER, NED, CRO, BRA, RUS'</li></ul> | |
|
| First Name | <ul><li>'Top Name: Mary, Linda, Debra, Lisa, Michelle, Jennifer, Jessica, Samantha, Ashley, Hannah, Emily, Madison, Emma, Isabella, Sophia, Olivia, John, Robert, James, David'</li></ul> | |
|
| City Name | <ul><li>'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'</li></ul> | |
|
| Color | <ul><li>'color: Yellow, Black, White'</li><li>'highlight_fur_color: None, Cinnamon, White, Gray, Cinnamon, White, Gray, White, Black, Cinnamon, White, Black, Black, White, Black, Cinnamon, Gray, Black'</li><li>'primary_fur_color: None, Gray, Cinnamon, Black'</li></ul> | |
|
| License Plate | <ul><li>'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'</li></ul> | |
|
| AM/PM | <ul><li>'shift: PM, AM'</li></ul> | |
|
| Company Name | <ul><li>"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"</li></ul> | |
|
| Secondary Address | <ul><li>'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'</li></ul> | |
|
|
|
## Evaluation |
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### Metrics |
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| Label | Accuracy | |
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|:--------|:---------| |
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| **all** | 0.7512 | |
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|
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## Uses |
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|
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### Direct Use for Inference |
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|
|
First install the SetFit library: |
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|
|
```bash |
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pip install setfit |
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``` |
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|
|
Then you can load this model and run inference. |
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|
|
```python |
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from setfit import SetFitModel |
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|
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# Download from the 🤗 Hub |
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model = SetFitModel.from_pretrained("quantisan/paraphrase-MiniLM-L3-v2-93dataset") |
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# Run inference |
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preds = model("variety: Western, Eastern") |
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``` |
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<!-- |
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### Downstream Use |
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*List how someone could finetune this model on their own dataset.* |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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--> |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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|
|
### Training Set Metrics |
|
| Training set | Min | Median | Max | |
|
|:-------------|:----|:--------|:----| |
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| Word count | 2 | 22.3314 | 85 | |
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|
|
| Label | Training Sample Count | |
|
|:------------------------|:----------------------| |
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| Categorical | 8 | |
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| Timestamp | 5 | |
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| Date | 8 | |
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| Integer | 8 | |
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| Partial timestamp | 4 | |
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| Short text | 8 | |
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| Very short text | 3 | |
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| AM/PM | 1 | |
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| Boolean | 8 | |
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| City Name | 1 | |
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| Color | 3 | |
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| Company Name | 1 | |
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| Country ISO Code | 2 | |
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| Country Name | 8 | |
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| Currency Code | 1 | |
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| Day of Month | 4 | |
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| Day of Week | 4 | |
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| First Name | 1 | |
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| Floating Point Number | 8 | |
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| Full Name | 8 | |
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| Last Name | 2 | |
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| Latitude | 4 | |
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| License Plate | 1 | |
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| Longitude | 4 | |
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| Month Name | 6 | |
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| Month Number | 4 | |
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| Occupation | 3 | |
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| Postal Code | 1 | |
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| Secondary Address | 1 | |
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| Slug | 8 | |
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| Street Address | 3 | |
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| Street Name | 3 | |
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| Time | 3 | |
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| U.S. State | 8 | |
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| U.S. State Abbreviation | 6 | |
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| URI | 1 | |
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| URL | 8 | |
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| Year | 8 | |
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| Zip Code | 4 | |
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|
|
### Training Hyperparameters |
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- batch_size: (8, 8) |
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- num_epochs: (4, 4) |
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- max_steps: -1 |
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- sampling_strategy: oversampling |
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- body_learning_rate: (2e-05, 1e-05) |
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- head_learning_rate: 0.01 |
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- loss: CosineSimilarityLoss |
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- distance_metric: cosine_distance |
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- margin: 0.25 |
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- end_to_end: False |
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- use_amp: False |
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- warmup_proportion: 0.1 |
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- l2_weight: 0.01 |
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- seed: 42 |
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- eval_max_steps: -1 |
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- load_best_model_at_end: True |
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|
|
### Training Results |
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| Epoch | Step | Training Loss | Validation Loss | |
|
|:------:|:-----:|:-------------:|:---------------:| |
|
| 0.0003 | 1 | 0.3882 | - | |
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| 0.0140 | 50 | 0.1864 | - | |
|
| 0.0280 | 100 | 0.1588 | - | |
|
| 0.0421 | 150 | 0.15 | - | |
|
| 0.0561 | 200 | 0.1537 | - | |
|
| 0.0701 | 250 | 0.1325 | - | |
|
| 0.0841 | 300 | 0.132 | - | |
|
| 0.0981 | 350 | 0.1149 | - | |
|
| 0.1121 | 400 | 0.1198 | - | |
|
| 0.1262 | 450 | 0.1035 | - | |
|
| 0.1402 | 500 | 0.0907 | - | |
|
| 0.1542 | 550 | 0.0917 | - | |
|
| 0.1682 | 600 | 0.0875 | - | |
|
| 0.1822 | 650 | 0.0803 | - | |
|
| 0.1962 | 700 | 0.0669 | - | |
|
| 0.2103 | 750 | 0.0671 | - | |
|
| 0.2243 | 800 | 0.0614 | - | |
|
| 0.2383 | 850 | 0.0642 | - | |
|
| 0.2523 | 900 | 0.0481 | - | |
|
| 0.2663 | 950 | 0.0548 | - | |
|
| 0.2803 | 1000 | 0.0346 | - | |
|
| 0.2944 | 1050 | 0.0406 | - | |
|
| 0.3084 | 1100 | 0.0403 | - | |
|
| 0.3224 | 1150 | 0.0349 | - | |
|
| 0.3364 | 1200 | 0.0312 | - | |
|
| 0.3504 | 1250 | 0.0378 | - | |
|
| 0.3645 | 1300 | 0.0335 | - | |
|
| 0.3785 | 1350 | 0.0323 | - | |
|
| 0.3925 | 1400 | 0.0234 | - | |
|
| 0.4065 | 1450 | 0.0313 | - | |
|
| 0.4205 | 1500 | 0.022 | - | |
|
| 0.4345 | 1550 | 0.0326 | - | |
|
| 0.4486 | 1600 | 0.0233 | - | |
|
| 0.4626 | 1650 | 0.0195 | - | |
|
| 0.4766 | 1700 | 0.0254 | - | |
|
| 0.4906 | 1750 | 0.0211 | - | |
|
| 0.5046 | 1800 | 0.0198 | - | |
|
| 0.5186 | 1850 | 0.0201 | - | |
|
| 0.5327 | 1900 | 0.0216 | - | |
|
| 0.5467 | 1950 | 0.0174 | - | |
|
| 0.5607 | 2000 | 0.0176 | - | |
|
| 0.5747 | 2050 | 0.0234 | - | |
|
| 0.5887 | 2100 | 0.0172 | - | |
|
| 0.6027 | 2150 | 0.0129 | - | |
|
| 0.6168 | 2200 | 0.0151 | - | |
|
| 0.6308 | 2250 | 0.015 | - | |
|
| 0.6448 | 2300 | 0.0164 | - | |
|
| 0.6588 | 2350 | 0.0137 | - | |
|
| 0.6728 | 2400 | 0.014 | - | |
|
| 0.6869 | 2450 | 0.0154 | - | |
|
| 0.7009 | 2500 | 0.0135 | - | |
|
| 0.7149 | 2550 | 0.0164 | - | |
|
| 0.7289 | 2600 | 0.0139 | - | |
|
| 0.7429 | 2650 | 0.0164 | - | |
|
| 0.7569 | 2700 | 0.0106 | - | |
|
| 0.7710 | 2750 | 0.0084 | - | |
|
| 0.7850 | 2800 | 0.0133 | - | |
|
| 0.7990 | 2850 | 0.0114 | - | |
|
| 0.8130 | 2900 | 0.0066 | - | |
|
| 0.8270 | 2950 | 0.0091 | - | |
|
| 0.8410 | 3000 | 0.0126 | - | |
|
| 0.8551 | 3050 | 0.0107 | - | |
|
| 0.8691 | 3100 | 0.0068 | - | |
|
| 0.8831 | 3150 | 0.006 | - | |
|
| 0.8971 | 3200 | 0.007 | - | |
|
| 0.9111 | 3250 | 0.0155 | - | |
|
| 0.9251 | 3300 | 0.0111 | - | |
|
| 0.9392 | 3350 | 0.0049 | - | |
|
| 0.9532 | 3400 | 0.0076 | - | |
|
| 0.9672 | 3450 | 0.0092 | - | |
|
| 0.9812 | 3500 | 0.0086 | - | |
|
| 0.9952 | 3550 | 0.0061 | - | |
|
| 1.0 | 3567 | - | 0.1341 | |
|
| 1.0093 | 3600 | 0.0073 | - | |
|
| 1.0233 | 3650 | 0.0065 | - | |
|
| 1.0373 | 3700 | 0.0063 | - | |
|
| 1.0513 | 3750 | 0.0094 | - | |
|
| 1.0653 | 3800 | 0.0114 | - | |
|
| 1.0793 | 3850 | 0.0084 | - | |
|
| 1.0934 | 3900 | 0.0098 | - | |
|
| 1.1074 | 3950 | 0.0058 | - | |
|
| 1.1214 | 4000 | 0.0045 | - | |
|
| 1.1354 | 4050 | 0.018 | - | |
|
| 1.1494 | 4100 | 0.0077 | - | |
|
| 1.1634 | 4150 | 0.0067 | - | |
|
| 1.1775 | 4200 | 0.0061 | - | |
|
| 1.1915 | 4250 | 0.0037 | - | |
|
| 1.2055 | 4300 | 0.0045 | - | |
|
| 1.2195 | 4350 | 0.0033 | - | |
|
| 1.2335 | 4400 | 0.0067 | - | |
|
| 1.2475 | 4450 | 0.0054 | - | |
|
| 1.2616 | 4500 | 0.0057 | - | |
|
| 1.2756 | 4550 | 0.004 | - | |
|
| 1.2896 | 4600 | 0.0033 | - | |
|
| 1.3036 | 4650 | 0.0076 | - | |
|
| 1.3176 | 4700 | 0.0045 | - | |
|
| 1.3317 | 4750 | 0.0068 | - | |
|
| 1.3457 | 4800 | 0.0043 | - | |
|
| 1.3597 | 4850 | 0.0049 | - | |
|
| 1.3737 | 4900 | 0.0045 | - | |
|
| 1.3877 | 4950 | 0.0055 | - | |
|
| 1.4017 | 5000 | 0.0065 | - | |
|
| 1.4158 | 5050 | 0.0029 | - | |
|
| 1.4298 | 5100 | 0.0041 | - | |
|
| 1.4438 | 5150 | 0.0064 | - | |
|
| 1.4578 | 5200 | 0.0031 | - | |
|
| 1.4718 | 5250 | 0.0078 | - | |
|
| 1.4858 | 5300 | 0.0031 | - | |
|
| 1.4999 | 5350 | 0.004 | - | |
|
| 1.5139 | 5400 | 0.0035 | - | |
|
| 1.5279 | 5450 | 0.0062 | - | |
|
| 1.5419 | 5500 | 0.0062 | - | |
|
| 1.5559 | 5550 | 0.0065 | - | |
|
| 1.5699 | 5600 | 0.0036 | - | |
|
| 1.5840 | 5650 | 0.0037 | - | |
|
| 1.5980 | 5700 | 0.0047 | - | |
|
| 1.6120 | 5750 | 0.0037 | - | |
|
| 1.6260 | 5800 | 0.0028 | - | |
|
| 1.6400 | 5850 | 0.0052 | - | |
|
| 1.6541 | 5900 | 0.0043 | - | |
|
| 1.6681 | 5950 | 0.0029 | - | |
|
| 1.6821 | 6000 | 0.0064 | - | |
|
| 1.6961 | 6050 | 0.0031 | - | |
|
| 1.7101 | 6100 | 0.0023 | - | |
|
| 1.7241 | 6150 | 0.002 | - | |
|
| 1.7382 | 6200 | 0.0041 | - | |
|
| 1.7522 | 6250 | 0.0033 | - | |
|
| 1.7662 | 6300 | 0.0043 | - | |
|
| 1.7802 | 6350 | 0.0023 | - | |
|
| 1.7942 | 6400 | 0.0036 | - | |
|
| 1.8082 | 6450 | 0.0024 | - | |
|
| 1.8223 | 6500 | 0.0016 | - | |
|
| 1.8363 | 6550 | 0.003 | - | |
|
| 1.8503 | 6600 | 0.0043 | - | |
|
| 1.8643 | 6650 | 0.0043 | - | |
|
| 1.8783 | 6700 | 0.0017 | - | |
|
| 1.8923 | 6750 | 0.0018 | - | |
|
| 1.9064 | 6800 | 0.0029 | - | |
|
| 1.9204 | 6850 | 0.0026 | - | |
|
| 1.9344 | 6900 | 0.0039 | - | |
|
| 1.9484 | 6950 | 0.0019 | - | |
|
| 1.9624 | 7000 | 0.0041 | - | |
|
| 1.9765 | 7050 | 0.0019 | - | |
|
| 1.9905 | 7100 | 0.0023 | - | |
|
| 2.0 | 7134 | - | 0.1286 | |
|
| 2.0045 | 7150 | 0.0016 | - | |
|
| 2.0185 | 7200 | 0.0017 | - | |
|
| 2.0325 | 7250 | 0.0016 | - | |
|
| 2.0465 | 7300 | 0.0019 | - | |
|
| 2.0606 | 7350 | 0.0015 | - | |
|
| 2.0746 | 7400 | 0.0016 | - | |
|
| 2.0886 | 7450 | 0.0015 | - | |
|
| 2.1026 | 7500 | 0.0015 | - | |
|
| 2.1166 | 7550 | 0.0034 | - | |
|
| 2.1306 | 7600 | 0.0043 | - | |
|
| 2.1447 | 7650 | 0.0016 | - | |
|
| 2.1587 | 7700 | 0.0016 | - | |
|
| 2.1727 | 7750 | 0.0015 | - | |
|
| 2.1867 | 7800 | 0.0015 | - | |
|
| 2.2007 | 7850 | 0.0017 | - | |
|
| 2.2147 | 7900 | 0.0013 | - | |
|
| 2.2288 | 7950 | 0.0016 | - | |
|
| 2.2428 | 8000 | 0.0013 | - | |
|
| 2.2568 | 8050 | 0.0039 | - | |
|
| 2.2708 | 8100 | 0.0053 | - | |
|
| 2.2848 | 8150 | 0.0025 | - | |
|
| 2.2989 | 8200 | 0.0015 | - | |
|
| 2.3129 | 8250 | 0.0012 | - | |
|
| 2.3269 | 8300 | 0.006 | - | |
|
| 2.3409 | 8350 | 0.0014 | - | |
|
| 2.3549 | 8400 | 0.0014 | - | |
|
| 2.3689 | 8450 | 0.0028 | - | |
|
| 2.3830 | 8500 | 0.0015 | - | |
|
| 2.3970 | 8550 | 0.0019 | - | |
|
| 2.4110 | 8600 | 0.0017 | - | |
|
| 2.4250 | 8650 | 0.002 | - | |
|
| 2.4390 | 8700 | 0.0016 | - | |
|
| 2.4530 | 8750 | 0.0014 | - | |
|
| 2.4671 | 8800 | 0.0021 | - | |
|
| 2.4811 | 8850 | 0.0012 | - | |
|
| 2.4951 | 8900 | 0.0015 | - | |
|
| 2.5091 | 8950 | 0.0012 | - | |
|
| 2.5231 | 9000 | 0.0012 | - | |
|
| 2.5371 | 9050 | 0.0016 | - | |
|
| 2.5512 | 9100 | 0.0016 | - | |
|
| 2.5652 | 9150 | 0.0013 | - | |
|
| 2.5792 | 9200 | 0.0028 | - | |
|
| 2.5932 | 9250 | 0.0013 | - | |
|
| 2.6072 | 9300 | 0.0011 | - | |
|
| 2.6213 | 9350 | 0.0035 | - | |
|
| 2.6353 | 9400 | 0.0013 | - | |
|
| 2.6493 | 9450 | 0.0012 | - | |
|
| 2.6633 | 9500 | 0.0037 | - | |
|
| 2.6773 | 9550 | 0.0012 | - | |
|
| 2.6913 | 9600 | 0.0011 | - | |
|
| 2.7054 | 9650 | 0.0037 | - | |
|
| 2.7194 | 9700 | 0.0012 | - | |
|
| 2.7334 | 9750 | 0.0013 | - | |
|
| 2.7474 | 9800 | 0.0013 | - | |
|
| 2.7614 | 9850 | 0.001 | - | |
|
| 2.7754 | 9900 | 0.0011 | - | |
|
| 2.7895 | 9950 | 0.0012 | - | |
|
| 2.8035 | 10000 | 0.0012 | - | |
|
| 2.8175 | 10050 | 0.001 | - | |
|
| 2.8315 | 10100 | 0.001 | - | |
|
| 2.8455 | 10150 | 0.0011 | - | |
|
| 2.8595 | 10200 | 0.0009 | - | |
|
| 2.8736 | 10250 | 0.0018 | - | |
|
| 2.8876 | 10300 | 0.0013 | - | |
|
| 2.9016 | 10350 | 0.0009 | - | |
|
| 2.9156 | 10400 | 0.0033 | - | |
|
| 2.9296 | 10450 | 0.0034 | - | |
|
| 2.9437 | 10500 | 0.0011 | - | |
|
| 2.9577 | 10550 | 0.0013 | - | |
|
| 2.9717 | 10600 | 0.0009 | - | |
|
| 2.9857 | 10650 | 0.0009 | - | |
|
| 2.9997 | 10700 | 0.0011 | - | |
|
| 3.0 | 10701 | - | 0.1205 | |
|
| 3.0137 | 10750 | 0.0009 | - | |
|
| 3.0278 | 10800 | 0.0009 | - | |
|
| 3.0418 | 10850 | 0.0032 | - | |
|
| 3.0558 | 10900 | 0.0008 | - | |
|
| 3.0698 | 10950 | 0.0013 | - | |
|
| 3.0838 | 11000 | 0.0033 | - | |
|
| 3.0978 | 11050 | 0.0011 | - | |
|
| 3.1119 | 11100 | 0.0008 | - | |
|
| 3.1259 | 11150 | 0.0009 | - | |
|
| 3.1399 | 11200 | 0.0008 | - | |
|
| 3.1539 | 11250 | 0.0033 | - | |
|
| 3.1679 | 11300 | 0.0032 | - | |
|
| 3.1819 | 11350 | 0.0008 | - | |
|
| 3.1960 | 11400 | 0.0008 | - | |
|
| 3.2100 | 11450 | 0.001 | - | |
|
| 3.2240 | 11500 | 0.0009 | - | |
|
| 3.2380 | 11550 | 0.0008 | - | |
|
| 3.2520 | 11600 | 0.0008 | - | |
|
| 3.2660 | 11650 | 0.0008 | - | |
|
| 3.2801 | 11700 | 0.0009 | - | |
|
| 3.2941 | 11750 | 0.0008 | - | |
|
| 3.3081 | 11800 | 0.0007 | - | |
|
| 3.3221 | 11850 | 0.0008 | - | |
|
| 3.3361 | 11900 | 0.0008 | - | |
|
| 3.3502 | 11950 | 0.0009 | - | |
|
| 3.3642 | 12000 | 0.0008 | - | |
|
| 3.3782 | 12050 | 0.0007 | - | |
|
| 3.3922 | 12100 | 0.0009 | - | |
|
| 3.4062 | 12150 | 0.0008 | - | |
|
| 3.4202 | 12200 | 0.0008 | - | |
|
| 3.4343 | 12250 | 0.0009 | - | |
|
| 3.4483 | 12300 | 0.0008 | - | |
|
| 3.4623 | 12350 | 0.0008 | - | |
|
| 3.4763 | 12400 | 0.0008 | - | |
|
| 3.4903 | 12450 | 0.0009 | - | |
|
| 3.5043 | 12500 | 0.0007 | - | |
|
| 3.5184 | 12550 | 0.0008 | - | |
|
| 3.5324 | 12600 | 0.0009 | - | |
|
| 3.5464 | 12650 | 0.0031 | - | |
|
| 3.5604 | 12700 | 0.0009 | - | |
|
| 3.5744 | 12750 | 0.0008 | - | |
|
| 3.5884 | 12800 | 0.0007 | - | |
|
| 3.6025 | 12850 | 0.0007 | - | |
|
| 3.6165 | 12900 | 0.0007 | - | |
|
| 3.6305 | 12950 | 0.0008 | - | |
|
| 3.6445 | 13000 | 0.0007 | - | |
|
| 3.6585 | 13050 | 0.0008 | - | |
|
| 3.6726 | 13100 | 0.0007 | - | |
|
| 3.6866 | 13150 | 0.0007 | - | |
|
| 3.7006 | 13200 | 0.0008 | - | |
|
| 3.7146 | 13250 | 0.0007 | - | |
|
| 3.7286 | 13300 | 0.0031 | - | |
|
| 3.7426 | 13350 | 0.0006 | - | |
|
| 3.7567 | 13400 | 0.0008 | - | |
|
| 3.7707 | 13450 | 0.0007 | - | |
|
| 3.7847 | 13500 | 0.0006 | - | |
|
| 3.7987 | 13550 | 0.0007 | - | |
|
| 3.8127 | 13600 | 0.0008 | - | |
|
| 3.8267 | 13650 | 0.0007 | - | |
|
| 3.8408 | 13700 | 0.0008 | - | |
|
| 3.8548 | 13750 | 0.0007 | - | |
|
| 3.8688 | 13800 | 0.0007 | - | |
|
| 3.8828 | 13850 | 0.0007 | - | |
|
| 3.8968 | 13900 | 0.0007 | - | |
|
| 3.9108 | 13950 | 0.0007 | - | |
|
| 3.9249 | 14000 | 0.0031 | - | |
|
| 3.9389 | 14050 | 0.003 | - | |
|
| 3.9529 | 14100 | 0.0007 | - | |
|
| 3.9669 | 14150 | 0.0007 | - | |
|
| 3.9809 | 14200 | 0.0007 | - | |
|
| 3.9950 | 14250 | 0.0007 | - | |
|
| 4.0 | 14268 | - | 0.1155 | |
|
|
|
### 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 |
|
```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} |
|
} |
|
``` |
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|
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