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JohanHeinsen
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ENO_Runaway_Advertisement_classifier_2.0

Text Classification
setfit
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Model card Files
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JohanHeinsen commited on
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Files changed (13) hide show
  1. 1_Pooling/config.json +10 -0
  2. README.md +418 -0
  3. config.json +24 -0
  4. config_sentence_transformers.json +10 -0
  5. config_setfit.json +4 -0
  6. model.safetensors +3 -0
  7. model_head.pkl +3 -0
  8. modules.json +14 -0
  9. sentence_bert_config.json +4 -0
  10. special_tokens_map.json +37 -0
  11. tokenizer.json +0 -0
  12. tokenizer_config.json +65 -0
  13. vocab.txt +0 -0
1_Pooling/config.json ADDED
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+ {
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+ "word_embedding_dimension": 768,
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+ "pooling_mode_cls_token": false,
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+ "pooling_mode_mean_tokens": true,
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+ "pooling_mode_max_tokens": false,
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+ "pooling_mode_mean_sqrt_len_tokens": false,
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+ "pooling_mode_weightedmean_tokens": false,
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+ "pooling_mode_lasttoken": false,
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+ "include_prompt": true
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+ }
README.md ADDED
@@ -0,0 +1,418 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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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: Studiosus N. Krog, Skoleholder i Ribe, haver Leilighed til at tage et par
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+ unge Mennesker i Kost og Logemente, som skulde behøve Underviisning ved ham, mod
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+ billig Betaling, da de med Omgangen og Læremaaden skal blive tiente og for nøiede.
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+ - text: Min Karl Mads Hansen, demitteret fra Landmilicesessionen for Frederichsorg
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+ Amt den 17 Marti d. A., af 25 lægd No. 77, Hagerup Sogn, 36 Aar, er mod min Vidende
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+ og Villie undvigt sin Tieneste, var iklædt hvid Vadmels Kiole, rund Hat og Skoe
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+ paa Fødderne. Da jeg haver grundet Aarsag at forfølge ham, saa advares alle og
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+ enhver ikke at modtage, huse eller hæle ham, mindre betroe ham noget enten i mit
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+ eller andres Navn, men tvertimod enten at angive hans Opholdssted, eller mod en
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+ passende Belønning foranstalte ham tilbagebragt i hans ulovlig forladte Tieneste,
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+ paa det de ved Deeltagelse ikke skal paadrage sig den Straf, som Lov og Anordninger
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+ i slig Tilfælde bestemmer. J. F. Menz, Bager, boende ved Amagerbroe.
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+ - text: En ganske nye Vand-Filtrum af Holms Fabrik i Kjøbenhavn, destillerende 50
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+ Potter Vand om Dagen er tilkjøbs i Stokkemarke Præstegaard.
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+ - text: 'Rusland. Den russiske Regjerings heftige Forbittrelse mod Engelland - hedder
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+ det i en London, ner Avis: bryder nu les i dens Journaler. Den Moskauer Avis paastaaer
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+ f. Ex., at den næste Fred slutning mellem Rusland og Storbrittanien torde blive
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+ undertegnet i Calcutta. Denne Trudsel, tilføier den engelske Avis: er intet Pralerie,
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+ men et Project, hvis Udførelse i flere Aar har beskjæftiget det Petersborgske
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+ Kabinet. Under det Paaskud at knytte Handelsforbindelser med Lande i det indre
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+ Asien, have Russerne udvidet deres militaire Rekognosceringer indtil Grændserne
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+ af det engelske Jndien, og paa en Maade iforveien gjort Udkastet til en Militairvei
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+ der hen. Flene Eventyrere have, skjulte under allehaandForklædninger, i dette
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+ Øiemed vovet sig lige til Punab, paa Hindostans Grændse. Vi ville ikke give os
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+ af med at spaae, men det troe vi, at de næste 10 Aar ville hidføre store og uventede
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+ Begivenheder i Asien. - Paa Londons Børs var det en heel almindelig Efterretning,
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+ at den russiske Regjering skal have opfordret endeel tydske Stater til at forhøie
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+ Jndførselstolden paa engelske Vare saaledes, at den kommer meget nær et Jnførselsforbud.
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+ Den brittiske Handelstand er naturligviis bleven meget forbittret herover. Ligeledes
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+ har den Efterretning gjort Opsigt i Wien, at 6 wallakiske Regimenter ere paa Keiser
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+ Nicolais Befaling blevne indlemmede i den russiske Armee. J St. Petersborg hed
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+ det, at Don Carlos af Spaniens ældste Søn skal forloves med en nordisk Prindsesse.'
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+ - text: Hos Undertegnede erholdes i Commission heftede Exemplarer a 2 Rbdlr. i Sedler
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+ af den nys i Kiøbenhavn udkomne Jule= og Nytaarsgave Lyra ved Jørgen Henrich,
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+ Berner Rottbøll Sadolin, Boghandler og Bogbinder.
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+ metrics:
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+ - accuracy
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+ - f1
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+ - precision
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+ - recall
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+ pipeline_tag: text-classification
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+ library_name: setfit
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+ inference: true
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+ base_model: JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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+ model-index:
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+ - name: SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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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: 0.998960498960499
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+ name: Accuracy
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+ - type: f1
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+ value: 0.9915966386554622
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+ name: F1
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+ - type: precision
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+ value: 0.9833333333333333
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+ name: Precision
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+ - type: recall
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+ value: 1.0
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+ name: Recall
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+ ---
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+
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+ # SetFit with JohanHeinsen/Old_News_Segmentation_SBERT_V0.1
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+
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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 [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1) 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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+
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+ The model has been trained using an efficient few-shot learning technique that involves:
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+ - **Model Type:** SetFit
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+ - **Sentence Transformer body:** [JohanHeinsen/Old_News_Segmentation_SBERT_V0.1](https://huggingface.co/JohanHeinsen/Old_News_Segmentation_SBERT_V0.1)
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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:** 512 tokens
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+ - **Number of Classes:** 2 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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+
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+ ### Model Sources
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+
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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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+
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+ ### Model Labels
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+ | Label | Examples |
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+ |:------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
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+ | 0 | <ul><li>'En meget brav gammel adelig Dame i Augsburg, har legeret 600,000 Gylden til et Pigeinstitues Oprettelse.'</li><li>'Efter indkommen Anmeldelse fra vedkommende Strandtoldbetjent er der løst af Havet inddrevet: paa Østeragger Strand 1 Oxhoved Viin mkt. J Feene paa Tolbøl Strand et Ditto Dito med samme Mærke, paa Hvidberg v. A. Strand 1 Ditto Dito mkt. DL. R. paa Ørum Strand 1 Ditto Dito mkt. 1 Pupaa Steenberg Strand 1 Ditto Dito mkt. NeEieren eller Eierne til fornævnte Oxhoveder Vine indkaldes herved sub poena præclusi et perpetui silentii med Aar og Dags Varsel at indfinde sig ved Amtet for at legitimere Eiendomsretten, hvorefter det indkommende Auctionsbeløb, med Fradrag af alle lovlige Udgifter, skal vorde Vedkommende udbetalt. Thisted Amthuus, den 24de August 1833. Faye.'</li><li>'Ved Tallotteriets 1212te Trækning i Altona den 12te April udkom følgende Nummere:'</li></ul> |
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+ | 1 | <ul><li>'En Pige 15 Aar gammel, liden af Vext, navnlig Anne Marie, er den 25 May 1761. fra sine Forældre undvigt, og da hende en Arv er tilfalden, saa ombedes hun, eller hvo hende skulde forekomme, at formode hende at indfinde sig hos mig, boende i Nyeboder i Kiøbenhavn paa Elsdyrs-Længden i No. 18, som er hendes Fader, Christen Matros ved 4de Divisions 8de Compagnie.'</li><li>'At fra Kronborg Fæstnings Arbeide den 2 Oct. Sidst er undvigt uærlige Slave Hans Hansen, fød i Roeskilde, 42 Aar gl., liden af Vext, maadelig af Lemmer, blaae af Øine og bruun af Haar, det bekiendtgiøres herved til alle og enhvers Efterretning ligesom man og tillige vil have enhver anmodet at anholde denne for den offentlige Sikkerhed farlige Person, hvor som helst han skulde antræffes, og derefter henbringe ham til nærmeste Arresthuus til Bevaring, hvorfra han, naar saadant Commandant-skabet paa Kronborg tilmeldes, strax skal vorde afhentet, og de paa hans Anholdelse, Arrest og Forplegning anvendte Bekostninger, samt de sædvanlige Opbringerpenge bliver betalt, og tiener tillige til Underretning, at fornævnte Slave ved sin Undvigelse ei havde andet end bare Skiorte paa Kroppen, men Slave Buxer, Strømper og Skoe paa Benene, og en rund Hat paa Hovedet, og har desuden et stort Ar paa det ene Been fra en langvarig Beenskade.'</li><li>'Af Kongens Regiment har Mousqueteer Carl Sverling absenteret sig, samme var klæd i en graa Frakke, rød Manchesters Vest og Buxer, koparret af Ansigt, 23 Aar gl. 65, Tom. Høy; den som tager ham op, levere ham til Casernene imod Douceur efter Forordningen.'</li></ul> |
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+
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+ ## Evaluation
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+
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+ ### Metrics
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+ | Label | Accuracy | F1 | Precision | Recall |
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+ |:--------|:---------|:-------|:----------|:-------|
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+ | **all** | 0.9990 | 0.9916 | 0.9833 | 1.0 |
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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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+
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+ First install the SetFit library:
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+
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+ ```bash
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+ pip install setfit
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+ ```
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+
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+ Then you can load this model and run inference.
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+
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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("setfit_model_id")
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+ # Run inference
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+ preds = model("En ganske nye Vand-Filtrum af Holms Fabrik i Kjøbenhavn, destillerende 50 Potter Vand om Dagen er tilkjøbs i Stokkemarke Præstegaard.")
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+ ```
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+
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+ <!--
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+ ### Downstream Use
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+
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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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+ <!--
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+ ### Out-of-Scope Use
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+
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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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+ <!--
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+ ## Bias, Risks and Limitations
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+
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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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+ <!--
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+ ### Recommendations
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+
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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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+
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+ ## Training Details
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+
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+ ### Training Set Metrics
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+ | Training set | Min | Median | Max |
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+ |:-------------|:----|:--------|:-----|
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+ | Word count | 5 | 88.9318 | 1999 |
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+
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+ | Label | Training Sample Count |
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+ |:------|:----------------------|
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+ | 0 | 2093 |
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+ | 1 | 149 |
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+
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+ ### Training Hyperparameters
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+ - batch_size: (12, 12)
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+ - num_epochs: (2, 2)
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+ - max_steps: -1
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+ - sampling_strategy: oversampling
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+ - num_iterations: 12
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+ - body_learning_rate: (2e-05, 2e-05)
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+ - head_learning_rate: 2e-05
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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: False
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+
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+ ### Training Results
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+ | Epoch | Step | Training Loss | Validation Loss |
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+ |:------:|:----:|:-------------:|:---------------:|
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+ | 0.0002 | 1 | 0.5665 | - |
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+ | 0.0112 | 50 | 0.4302 | - |
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+ | 0.0223 | 100 | 0.3677 | - |
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+ | 0.0335 | 150 | 0.1981 | - |
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+ | 0.0446 | 200 | 0.0642 | - |
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+ | 0.0558 | 250 | 0.0272 | - |
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+ | 0.0669 | 300 | 0.0083 | - |
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+ | 0.0781 | 350 | 0.0114 | - |
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+ | 0.0892 | 400 | 0.0038 | - |
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+ | 0.1004 | 450 | 0.0036 | - |
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+ | 0.1115 | 500 | 0.0023 | - |
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+ | 0.1227 | 550 | 0.005 | - |
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+ | 0.1338 | 600 | 0.0031 | - |
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+ | 0.1450 | 650 | 0.0011 | - |
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+ | 0.1561 | 700 | 0.0038 | - |
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+ | 0.1673 | 750 | 0.0001 | - |
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+ | 0.1784 | 800 | 0.0005 | - |
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+ | 0.1896 | 850 | 0.0019 | - |
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+ | 0.2007 | 900 | 0.0016 | - |
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+ | 0.2119 | 950 | 0.0001 | - |
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+ | 0.2230 | 1000 | 0.0014 | - |
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+ | 0.2342 | 1050 | 0.0022 | - |
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+ | 0.2453 | 1100 | 0.0021 | - |
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+ | 0.2565 | 1150 | 0.0018 | - |
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+ | 0.2676 | 1200 | 0.0002 | - |
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+ | 0.2788 | 1250 | 0.0 | - |
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+ | 0.2899 | 1300 | 0.0019 | - |
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+ | 0.3011 | 1350 | 0.0 | - |
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+ | 0.3122 | 1400 | 0.0 | - |
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+ | 0.3234 | 1450 | 0.0036 | - |
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+ | 0.3345 | 1500 | 0.0 | - |
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+ | 0.3457 | 1550 | 0.0 | - |
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+ | 0.3568 | 1600 | 0.0 | - |
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+ | 0.3680 | 1650 | 0.0 | - |
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+ | 0.3791 | 1700 | 0.0 | - |
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+ | 0.3903 | 1750 | 0.0018 | - |
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+ | 0.4014 | 1800 | 0.0001 | - |
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+ | 0.4126 | 1850 | 0.0017 | - |
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+ | 0.4237 | 1900 | 0.0 | - |
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+ | 0.4349 | 1950 | 0.0 | - |
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+ | 0.4460 | 2000 | 0.0 | - |
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+ | 0.4572 | 2050 | 0.0035 | - |
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+ | 0.4683 | 2100 | 0.0034 | - |
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+ | 0.4795 | 2150 | 0.0036 | - |
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+ | 0.4906 | 2200 | 0.0017 | - |
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+ | 0.5018 | 2250 | 0.0056 | - |
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+ | 0.5129 | 2300 | 0.0006 | - |
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+ | 0.5241 | 2350 | 0.0 | - |
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+ | 0.5352 | 2400 | 0.0 | - |
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+ | 0.5464 | 2450 | 0.0 | - |
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+ | 0.5575 | 2500 | 0.0016 | - |
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+ | 0.5687 | 2550 | 0.0014 | - |
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+ | 0.5798 | 2600 | 0.0 | - |
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+ | 0.5910 | 2650 | 0.0012 | - |
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+ | 0.6021 | 2700 | 0.0001 | - |
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+ | 0.6133 | 2750 | 0.0 | - |
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+ | 0.6244 | 2800 | 0.0 | - |
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+ | 0.6356 | 2850 | 0.0 | - |
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+ | 0.6467 | 2900 | 0.0 | - |
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+ | 0.6579 | 2950 | 0.0 | - |
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+ | 0.6690 | 3000 | 0.0016 | - |
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+ | 0.6802 | 3050 | 0.0 | - |
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+ | 0.6913 | 3100 | 0.0 | - |
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+ | 0.7025 | 3150 | 0.0 | - |
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+ | 0.7136 | 3200 | 0.0017 | - |
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+ | 0.7248 | 3250 | 0.0012 | - |
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+ | 0.7360 | 3300 | 0.0002 | - |
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+ | 0.7471 | 3350 | 0.0 | - |
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+ | 0.7583 | 3400 | 0.0 | - |
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+ | 0.7694 | 3450 | 0.0 | - |
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+ | 0.7806 | 3500 | 0.0 | - |
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+ | 0.7917 | 3550 | 0.0 | - |
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+ | 0.8029 | 3600 | 0.0 | - |
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+ | 0.8140 | 3650 | 0.0 | - |
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+ | 0.8252 | 3700 | 0.0 | - |
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+ | 0.8363 | 3750 | 0.0 | - |
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+ | 0.8475 | 3800 | 0.0 | - |
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+ | 0.8586 | 3850 | 0.0 | - |
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+ | 0.8698 | 3900 | 0.0 | - |
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+ | 0.8809 | 3950 | 0.0 | - |
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+ | 0.8921 | 4000 | 0.0 | - |
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+ | 0.9032 | 4050 | 0.0 | - |
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+ | 0.9144 | 4100 | 0.0 | - |
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+ | 0.9255 | 4150 | 0.0 | - |
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+ | 0.9367 | 4200 | 0.0 | - |
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+ | 0.9478 | 4250 | 0.0 | - |
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+ | 0.9590 | 4300 | 0.0 | - |
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+ | 0.9701 | 4350 | 0.0 | - |
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+ | 0.9813 | 4400 | 0.0 | - |
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+ | 0.9924 | 4450 | 0.0 | - |
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+ | 1.0036 | 4500 | 0.0 | - |
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+ | 1.0147 | 4550 | 0.0 | - |
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+ | 1.0259 | 4600 | 0.0 | - |
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+ | 1.0370 | 4650 | 0.0 | - |
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+ | 1.0482 | 4700 | 0.0 | - |
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+ | 1.0593 | 4750 | 0.0 | - |
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+ | 1.0705 | 4800 | 0.0 | - |
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+ | 1.0816 | 4850 | 0.0 | - |
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+ | 1.0928 | 4900 | 0.0 | - |
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+ | 1.1039 | 4950 | 0.0 | - |
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+ | 1.1151 | 5000 | 0.0 | - |
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+ | 1.1262 | 5050 | 0.0 | - |
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+ | 1.1374 | 5100 | 0.0 | - |
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+ | 1.1485 | 5150 | 0.0 | - |
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+ | 1.1597 | 5200 | 0.0 | - |
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+ | 1.1708 | 5250 | 0.0 | - |
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+ | 1.1820 | 5300 | 0.0 | - |
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+ | 1.1931 | 5350 | 0.0 | - |
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+ | 1.2043 | 5400 | 0.0 | - |
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+ | 1.2154 | 5450 | 0.0 | - |
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+ | 1.2266 | 5500 | 0.0 | - |
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+ | 1.2377 | 5550 | 0.0 | - |
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+ | 1.2489 | 5600 | 0.0 | - |
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+ | 1.2600 | 5650 | 0.0 | - |
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+ | 1.2712 | 5700 | 0.0 | - |
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+ | 1.2823 | 5750 | 0.0 | - |
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+ | 1.2935 | 5800 | 0.0 | - |
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+ | 1.3046 | 5850 | 0.0 | - |
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+ | 1.3158 | 5900 | 0.0 | - |
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+ | 1.3269 | 5950 | 0.0 | - |
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+ | 1.3381 | 6000 | 0.0 | - |
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+ | 1.3492 | 6050 | 0.0 | - |
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+ | 1.3604 | 6100 | 0.0 | - |
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+ | 1.3715 | 6150 | 0.0 | - |
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+ | 1.3827 | 6200 | 0.0 | - |
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+ | 1.3938 | 6250 | 0.0 | - |
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+ | 1.4050 | 6300 | 0.0 | - |
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+ | 1.4161 | 6350 | 0.0 | - |
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+ | 1.4273 | 6400 | 0.0 | - |
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+ | 1.4384 | 6450 | 0.0 | - |
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+ | 1.4496 | 6500 | 0.0 | - |
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+ | 1.4607 | 6550 | 0.0 | - |
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+ | 1.4719 | 6600 | 0.0 | - |
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+ | 1.4831 | 6650 | 0.0 | - |
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+ | 1.4942 | 6700 | 0.0 | - |
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+ | 1.5054 | 6750 | 0.0 | - |
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+
377
+ ### Framework Versions
378
+ - Python: 3.11.12
379
+ - SetFit: 1.1.3
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+ - Sentence Transformers: 4.1.0
381
+ - Transformers: 4.51.3
382
+ - PyTorch: 2.7.0
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+ - Datasets: 2.19.2
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+ - Tokenizers: 0.21.1
385
+
386
+ ## Citation
387
+
388
+ ### BibTeX
389
+ ```bibtex
390
+ @article{https://doi.org/10.48550/arxiv.2209.11055,
391
+ doi = {10.48550/ARXIV.2209.11055},
392
+ url = {https://arxiv.org/abs/2209.11055},
393
+ author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
394
+ keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
395
+ title = {Efficient Few-Shot Learning Without Prompts},
396
+ publisher = {arXiv},
397
+ year = {2022},
398
+ copyright = {Creative Commons Attribution 4.0 International}
399
+ }
400
+ ```
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+
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+ <!--
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+ ## Glossary
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+
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+ *Clearly define terms in order to be accessible across audiences.*
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+ -->
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+
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+ <!--
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+ ## Model Card Authors
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+
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+ *Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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+ -->
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+
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+ <!--
415
+ ## Model Card Contact
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+
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+ *Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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+ -->
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