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klimacoder_speedlimit_v0.1

Text Classification
setfit
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sentence-transformers
xlm-roberta
generated_from_setfit_trainer
Eval Results
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klimacoder_speedlimit_v0.1 / README.md
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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: 'Als die Studie mit dem unscheinbaren Namen "Flüssiger Verkehr für Klimaschutz
und Luftreinhaltung" im Januar veröffentlicht wird, sorgt sie schnell für Schlagzeilen:
Angeblich lasse sich durch ein allgemeines Tempolimit von 120 Kilometern pro Stunde
(km/h) auf Autobahnen und 80 km/h auf Landstraßen deutlich mehr CO2 einsparen,
als gedacht. Es ist ein Ergebnis ganz nach dem Geschmack des Auftraggebers: dem
Umweltbundesamt. Die Behörde ist dem Umweltministerium von Steffi Lemke (Grüne)
unterstellt.'
- text: Ein Tempolimit habe Wissings Haus weder in das Klimaschutzprogramm 2030 noch
in ein Sofortprogramm aufgenommen. Dabei sei das Ministerium bei seiner Bewertung
selbst davon ausgegangen, dass ein Tempolimit von 120 Kilometern pro Stunde auf
Autobahnen kurzfristige Einsparungen von jährlich 2,3 Millionen Tonnen Treibhausgasen
bringe.
- text: Die Straßen in Deutschland könnten künftig ähnlich überlastet sein wie die
Schienen, befürchtet Verkehrsminister Volker Wissing. Der FDP-Politiker will das
mit schnelleren Planungsverfahren verhindern - doch das grüne Umweltministerium
ist dagegen. Ein Tempolimit auf Autobahnen lehnt er weiterhin ab.
- text: 'Die Kosten, die sich aus einem jahrelang geführten Glaubens- und Gutachterkrieg
unter Experten und Anwälten ergeben, sind noch nicht berechnet. Denn das ist unmöglich.
Das Verkehrsministerium hätte sich jedenfalls statt um das reale Scheitern einer
von Anfang an surrealen Maut, die als europafeindliche Populismus-Idee und Wahlkampffolklore
der CSU begonnen hat und jetzt als historisch unvergleichlicher Volksschaden ohne
Vollkasko endet, um marode Brücken, den Dauerstau auf Autobahnen, das Tempolimit,
um Busse und Bahnen oder auch einfach um die bis heute nicht mal ansatzweise am
Horizont auszumachende Verkehrswende oder eine zukunftsfähige Mobilität kümmern
können: Die CSU stellte ja von 2009 bis 2021 die Verkehrsminister.'
- text: 'Denn vieles von dem, wogegen sich die Liberalen gerade so lautstark wehren,
haben sie mit ausgehandelt: Den Atomausstieg, das Verbrenner-Aus, den Vorrang
von Schiene vor Straße, die Energiewende beim Heizen, die Kindergrundsicherung
- all das steht bereits im Koalitionsvertrag. Genauso übrigens, dass es kein Tempolimit
auf der Autobahn geben soll. Und nichts davon wird durch die jüngsten Krisen ernsthaft
infrage gestellt.'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: T-Systems-onsite/cross-en-de-roberta-sentence-transformer
model-index:
- name: SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.35
name: Accuracy
---
# SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) 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.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 3 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:-----------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| supportive | <ul><li>'Die Debatte um ein nationales Tempolimit auf deutschen Autobahnen gewinnt an Fahrt, da eine wachsende Mehrheit der Bürger dies im Interesse des Umweltschutzes und der Verkehrssicherheit befürwortet. Trotz der historischen Skepsis gegenüber solchen Maßnahmen zeigt sich ein Umdenken in der Bevölkerung, wobei mehr als 60 Prozent für eine Geschwindigkeitsbegrenzung sind. Die Frage bleibt jedoch bestehen, wie lange jene, die sich vehement dagegen aussprechen, ihre Vorbehalte gegenüber einem umweltfreundlicheren und sichereren Verkehr in Deutschland aufrechterhalten können.'</li><li>'In jüngsten Umfragen zeigt sich eine bemerkenswerte Verschiebung in der öffentlichen Meinung: Mehr als 60 Prozent der Deutschen sprechen sich nun für ein nationales Tempolimit auf Autobahnen aus, getrieben von den Zielen des Umweltschutzes und der Verkehrssicherheit. Diese gesetzgeberischen Bestrebungen stoßen jedoch nach wie vor auf Widerstand bei jenen, die ihr Recht auf schnelle Mobilität über kollektive Belange stellen. Obwohl eine solche Maßnahme Herausforderungen in ihrer Umsetzung mit sich bringt, könnten sie einen wichtigen Schritt darstellen, um die ökologischen Fußabdrücke unserer Verkehrsinfrastruktur zu reduzieren und tödliche Unfälle zu verhindern.'</li><li>'Die Debatte um ein nationales Tempolimit auf deutschen Autobahnen findet zunehmend Anklang, wobei über 60 Prozent der Bevölkerung laut Umweltbundesamt dieses Maßnahme im Sinne des Klimaschutzes und der Verkehrssicherheit befürworten. Während sich Skepsis in manchen Kreisen hält, scheint die öffentliche Meinung einem Paradigmenwechsel entgegenzugehen, der den egozentrischen Widerstand gegen einen moderateren Fahrstil langsam schwinden lässt. Trotzdem bleibt abzuwarten, wie sich Gesetzesinitiativen konkret gestalten und welche Herausforderungen bei deren Umsetzung zu bewältigen sind.'</li></ul> |
| opposed | <ul><li>'Die Debatte um das nationale Tempolimit auf Autobahnen spaltet nicht nur die Länder, sondern auch die Union selbst. Während Niedersachsen einen Vorstoß unternimmt, rufen führende CDU-Vertreter aus Hessen und dem Saarland lautstark dagegen an: "Kein Tempolimit!" Ines Claus und Frank Wagner vertreten mit Nachdruck die Auffassung, dass das Fahren auf deutschen Autobahnen kein Spielball politischer Bevormundung sein sollte. Sie plädieren für Eigenverantwortung statt bürokratische Fahrverbote und sehen in der individuellen Verhaltensänderung der Autofahrerinnen und Autofahrern die bessere Lösung, um Treibstoff- und Emissionseinsparungen zu erreichen. Die Debatte bleibt hitzig – wer wird sich durchsetzen?'</li><li>'Ein nationaler Tempolimit-Wahnsinn auf deutschen Autobahnen? Immer mehr Politiker zeigen klare Kante gegen den Bremsklotz-Irrsinn und pochen auf Freiheit statt Fahrspaß-Verhüterli! Warum soll der mündige Bürger ausgebremst werden, wenn Treibstoffsparen auch anders geht?'</li><li>'Die Forderung nach einem generellen Tempolimit auf deutschen Autobahnen wirkt wie ein unnötiger Eingriff in die individuelle Freiheit der Autofahrer, zumal die Unfallstatistiken im europäischen Vergleich für sich sprechen. Während andere Länder mit Tempolimits kämpfen, zeigt sich Deutschland als Vorreiter in Sachen Verkehrssicherheit und moderner Fahrzeugtechnologie. Statt pauschaler Beschränkungen sollten wir auf innovative Lösungen setzen, die den Verkehrsfluss verbessern und gleichzeitig die Umwelt schonen.'</li></ul> |
| neutral | <ul><li>'Der Bundestag berät derzeit über Vorschläge zur Einführung eines nationalen Tempolimits auf deutschen Autobahnen, wobei sowohl Umweltauswirkungen als auch Sicherheitsaspekte eine Rolle spielen. Befürworter argumentieren, dass ein Tempolimit den CO2-Ausstoß reduzieren und die Verkehrssicherheit erhöhen könnte, während Gegner darauf hinweisen, dass die Freiheit der individuellen Mobilität eingeschränkt würde.'</li><li>'Im politischen Diskurs um ein nationales Tempolimit auf Autobahnen gibt es erneut Debatten, die sowohl Befürworter als auch Kritiker mobilisieren. Während die aktuelle Vereinbarung im Koalitionsvertrag ein Tempolimit ausschließt, bleiben die Forderungen nach einer neuen Gesetzesinitiative angesichts von Klima- und Sicherheitsüberlegungen weiter präsent. Bislang hat sich jedoch keine maßgebliche Veränderung des bestehenden Konsenses ergeben.'</li><li>'Der jüngste Vorschlag zur Einführung eines bundesweiten Tempolimits auf deutschen Autobahnen wurde im Bundestag intensiv debattiert, wobei Befürworter die Verbesserung der Verkehrssicherheit und die mögliche Reduzierung von CO2-Emissionen betonen. Gegner sehen hingegen eine Einschränkung individueller Freiheitsrechte und den potenziellen Einfluss auf den Verkehrsfluss als Hauptpunkte der Kritik.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.35 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/klimacoder_speedlimit_v0.1")
# Run inference
preds = model("Die Straßen in Deutschland könnten künftig ähnlich überlastet sein wie die Schienen, befürchtet Verkehrsminister Volker Wissing. Der FDP-Politiker will das mit schnelleren Planungsverfahren verhindern - doch das grüne Umweltministerium ist dagegen. Ein Tempolimit auf Autobahnen lehnt er weiterhin ab.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 60.7587 | 127 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 350 |
| opposed | 403 |
| supportive | 399 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (3, 3)
- max_steps: -1
- sampling_strategy: oversampling
- body_learning_rate: (2e-05, 1e-05)
- head_learning_rate: 0.01
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- l2_weight: 0.01
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0000 | 1 | 0.2239 | - |
| 0.0018 | 50 | 0.2351 | - |
| 0.0036 | 100 | 0.2327 | - |
| 0.0054 | 150 | 0.2197 | - |
| 0.0072 | 200 | 0.2034 | - |
| 0.0091 | 250 | 0.1914 | - |
| 0.0109 | 300 | 0.1718 | - |
| 0.0127 | 350 | 0.1476 | - |
| 0.0145 | 400 | 0.1127 | - |
| 0.0163 | 450 | 0.0685 | - |
| 0.0181 | 500 | 0.039 | - |
| 0.0199 | 550 | 0.024 | - |
| 0.0217 | 600 | 0.0178 | - |
| 0.0236 | 650 | 0.01 | - |
| 0.0254 | 700 | 0.007 | - |
| 0.0272 | 750 | 0.0057 | - |
| 0.0290 | 800 | 0.0038 | - |
| 0.0308 | 850 | 0.0025 | - |
| 0.0326 | 900 | 0.0022 | - |
| 0.0344 | 950 | 0.0023 | - |
| 0.0362 | 1000 | 0.0014 | - |
| 0.0381 | 1050 | 0.0016 | - |
| 0.0399 | 1100 | 0.001 | - |
| 0.0417 | 1150 | 0.0009 | - |
| 0.0435 | 1200 | 0.0007 | - |
| 0.0453 | 1250 | 0.0007 | - |
| 0.0471 | 1300 | 0.0006 | - |
| 0.0489 | 1350 | 0.0004 | - |
| 0.0507 | 1400 | 0.0004 | - |
| 0.0525 | 1450 | 0.0003 | - |
| 0.0544 | 1500 | 0.0003 | - |
| 0.0562 | 1550 | 0.0003 | - |
| 0.0580 | 1600 | 0.0003 | - |
| 0.0598 | 1650 | 0.0002 | - |
| 0.0616 | 1700 | 0.0002 | - |
| 0.0634 | 1750 | 0.0002 | - |
| 0.0652 | 1800 | 0.0002 | - |
| 0.0670 | 1850 | 0.0002 | - |
| 0.0689 | 1900 | 0.0001 | - |
| 0.0707 | 1950 | 0.0001 | - |
| 0.0725 | 2000 | 0.0001 | - |
| 0.0743 | 2050 | 0.0001 | - |
| 0.0761 | 2100 | 0.0001 | - |
| 0.0779 | 2150 | 0.0001 | - |
| 0.0797 | 2200 | 0.0001 | - |
| 0.0815 | 2250 | 0.0001 | - |
| 0.0834 | 2300 | 0.0001 | - |
| 0.0852 | 2350 | 0.0001 | - |
| 0.0870 | 2400 | 0.0001 | - |
| 0.0888 | 2450 | 0.0001 | - |
| 0.0906 | 2500 | 0.0001 | - |
| 0.0924 | 2550 | 0.0001 | - |
| 0.0942 | 2600 | 0.0001 | - |
| 0.0960 | 2650 | 0.0001 | - |
| 0.0978 | 2700 | 0.0 | - |
| 0.0997 | 2750 | 0.0 | - |
| 0.1015 | 2800 | 0.0 | - |
| 0.1033 | 2850 | 0.0 | - |
| 0.1051 | 2900 | 0.0 | - |
| 0.1069 | 2950 | 0.0 | - |
| 0.1087 | 3000 | 0.0 | - |
| 0.1105 | 3050 | 0.0 | - |
| 0.1123 | 3100 | 0.0 | - |
| 0.1142 | 3150 | 0.0 | - |
| 0.1160 | 3200 | 0.0 | - |
| 0.1178 | 3250 | 0.0 | - |
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| 0.1214 | 3350 | 0.0 | - |
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| 0.1450 | 4000 | 0.0 | - |
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| 0.1903 | 5250 | 0.0 | - |
| 0.1921 | 5300 | 0.0081 | - |
| 0.1939 | 5350 | 0.0103 | - |
| 0.1957 | 5400 | 0.001 | - |
| 0.1975 | 5450 | 0.002 | - |
| 0.1993 | 5500 | 0.0001 | - |
| 0.2011 | 5550 | 0.0 | - |
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| 0.2899 | 8000 | 0.0 | - |
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| 0.2935 | 8100 | 0.0 | - |
| 0.2954 | 8150 | 0.0 | - |
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| 0.2990 | 8250 | 0.0 | - |
| 0.3008 | 8300 | 0.0 | - |
| 0.3026 | 8350 | 0.0 | - |
| 0.3044 | 8400 | 0.0 | - |
| 0.3062 | 8450 | 0.0 | - |
| 0.3080 | 8500 | 0.0 | - |
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| 0.4457 | 12300 | 0.0 | - |
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| 0.9151 | 25250 | 0.0 | - |
| 0.9169 | 25300 | 0.0 | - |
| 0.9187 | 25350 | 0.0 | - |
| 0.9205 | 25400 | 0.0 | - |
| 0.9223 | 25450 | 0.0 | - |
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| 0.9259 | 25550 | 0.0 | - |
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| 0.9839 | 27150 | 0.0 | - |
| 0.9857 | 27200 | 0.0 | - |
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| 0.9893 | 27300 | 0.0 | - |
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| 1.0002 | 27600 | 0.0 | - |
| 1.0020 | 27650 | 0.0 | - |
| 1.0038 | 27700 | 0.0 | - |
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| 1.0075 | 27800 | 0.0 | - |
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| 1.0111 | 27900 | 0.0 | - |
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| 1.0147 | 28000 | 0.0 | - |
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| 1.0183 | 28100 | 0.0 | - |
| 1.0201 | 28150 | 0.0 | - |
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| 1.0510 | 29000 | 0.0 | - |
| 1.0528 | 29050 | 0.0 | - |
| 1.0546 | 29100 | 0.0 | - |
| 1.0564 | 29150 | 0.0 | - |
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| 1.0600 | 29250 | 0.0 | - |
| 1.0618 | 29300 | 0.0 | - |
| 1.0636 | 29350 | 0.0 | - |
| 1.0654 | 29400 | 0.0 | - |
| 1.0673 | 29450 | 0.0 | - |
| 1.0691 | 29500 | 0.0 | - |
| 1.0709 | 29550 | 0.0 | - |
| 1.0727 | 29600 | 0.0 | - |
| 1.0745 | 29650 | 0.0 | - |
| 1.0763 | 29700 | 0.0 | - |
| 1.0781 | 29750 | 0.0 | - |
| 1.0799 | 29800 | 0.0 | - |
| 1.0818 | 29850 | 0.0 | - |
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| 1.0872 | 30000 | 0.0 | - |
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| 1.0908 | 30100 | 0.0 | - |
| 1.0926 | 30150 | 0.0 | - |
| 1.0944 | 30200 | 0.0 | - |
| 1.0963 | 30250 | 0.0 | - |
| 1.0981 | 30300 | 0.0 | - |
| 1.0999 | 30350 | 0.0 | - |
| 1.1017 | 30400 | 0.0 | - |
| 1.1035 | 30450 | 0.0 | - |
| 1.1053 | 30500 | 0.0 | - |
| 1.1071 | 30550 | 0.0 | - |
| 1.1089 | 30600 | 0.0 | - |
| 1.1107 | 30650 | 0.0 | - |
| 1.1126 | 30700 | 0.0 | - |
| 1.1144 | 30750 | 0.0 | - |
| 1.1162 | 30800 | 0.0 | - |
| 1.1180 | 30850 | 0.0 | - |
| 1.1198 | 30900 | 0.0 | - |
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| 1.1234 | 31000 | 0.0 | - |
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| 1.1289 | 31150 | 0.0 | - |
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| 1.1343 | 31300 | 0.0 | - |
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| 1.1542 | 31850 | 0.0 | - |
| 1.1560 | 31900 | 0.0 | - |
| 1.1579 | 31950 | 0.0 | - |
| 1.1597 | 32000 | 0.0 | - |
| 1.1615 | 32050 | 0.0 | - |
| 1.1633 | 32100 | 0.0 | - |
| 1.1651 | 32150 | 0.0 | - |
| 1.1669 | 32200 | 0.0 | - |
| 1.1687 | 32250 | 0.0 | - |
| 1.1705 | 32300 | 0.0 | - |
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| 1.1850 | 32700 | 0.0 | - |
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| 1.1887 | 32800 | 0.0 | - |
| 1.1905 | 32850 | 0.0 | - |
| 1.1923 | 32900 | 0.0 | - |
| 1.1941 | 32950 | 0.0 | - |
| 1.1959 | 33000 | 0.0 | - |
| 1.1977 | 33050 | 0.0 | - |
| 1.1995 | 33100 | 0.0 | - |
| 1.2013 | 33150 | 0.0 | - |
| 1.2032 | 33200 | 0.0 | - |
| 1.2050 | 33250 | 0.0 | - |
| 1.2068 | 33300 | 0.0 | - |
| 1.2086 | 33350 | 0.0 | - |
| 1.2104 | 33400 | 0.0 | - |
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| 1.2140 | 33500 | 0.0 | - |
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| 1.2177 | 33600 | 0.0 | - |
| 1.2195 | 33650 | 0.0 | - |
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| 1.2322 | 34000 | 0.0 | - |
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| 1.2376 | 34150 | 0.0 | - |
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| 1.2412 | 34250 | 0.0 | - |
| 1.2430 | 34300 | 0.0 | - |
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| 1.2684 | 35000 | 0.0 | - |
| 1.2702 | 35050 | 0.0 | - |
| 1.2720 | 35100 | 0.0 | - |
| 1.2738 | 35150 | 0.0 | - |
| 1.2756 | 35200 | 0.0 | - |
| 1.2775 | 35250 | 0.0 | - |
| 1.2793 | 35300 | 0.0 | - |
| 1.2811 | 35350 | 0.0 | - |
| 1.2829 | 35400 | 0.0 | - |
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| 1.2865 | 35500 | 0.0 | - |
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| 1.2901 | 35600 | 0.0 | - |
| 1.2919 | 35650 | 0.0 | - |
| 1.2938 | 35700 | 0.0 | - |
| 1.2956 | 35750 | 0.0 | - |
| 1.2974 | 35800 | 0.0 | - |
| 1.2992 | 35850 | 0.0 | - |
| 1.3010 | 35900 | 0.0 | - |
| 1.3028 | 35950 | 0.0 | - |
| 1.3046 | 36000 | 0.0 | - |
| 1.3064 | 36050 | 0.0 | - |
| 1.3083 | 36100 | 0.0 | - |
| 1.3101 | 36150 | 0.0 | - |
| 1.3119 | 36200 | 0.0 | - |
| 1.3137 | 36250 | 0.0 | - |
| 1.3155 | 36300 | 0.0 | - |
| 1.3173 | 36350 | 0.0 | - |
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### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- Sentence Transformers: 3.3.1
- Transformers: 4.42.2
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.19.1
## 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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