---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"Während die Welt arbeitet, um Lösungen für die tatsächlichen Probleme zu
finden, verschwenden junge Aktivisten ihre Zeit mit Straßenblockaden und Schütteln
von Plakaten. Ihre Rhetorik ist einstudiert, ihre Wirklichkeitserfahrung jedoch
begrenzt."'
- text: Die jüngsten Gesetzesinitiativen zur flächendeckenden Einführung von Wärmepumpen
markieren einen bedeutenden Schritt in Richtung einer nachhaltigeren Energiezukunft
und könnten langfristig zur Reduzierung von CO2-Emissionen im Gebäudesektor beitragen.
Durch die Förderung dieser umweltfreundlichen Technologie wird nicht nur der Klimaschutz
gestärkt, sondern auch die Abhängigkeit von fossilen Brennstoffen verringert.
- text: Die Debatte über die Einführung eines nationalen Tempolimits auf deutschen
Autobahnen bleibt weiterhin ein umstrittenes Thema in der Politik. Befürworter
argumentieren mit positiven Auswirkungen auf die Verkehrssicherheit und den Umweltschutz,
während Gegner auf individuelle Freiheitsrechte und wirtschaftliche Faktoren verweisen.
Der Ausgang der Gesetzesinitiativen ist bisher ungewiss.
- text: 'Chaos auf den Straßen und genervte Pendler: Die Klima-Aktivisten von Fridays
for Future und der Letzten Generation sorgen erneut für Unmut in der Bevölkerung.
Während sie für ihre Sache kämpfen, wächst der Frust über ihre umstrittenen Methoden.'
- text: Inmitten wachsender Besorgnis über den Klimawandel setzen Klima-Aktivismus-Gruppen
wie Fridays for Future und die Letzte Generation mit ihren Aktionen ein starkes
Zeichen für die Dringlichkeit des Umweltschutzes. Ihre Entschlossenheit, auf die
Notwendigkeit rascher politischer und gesellschaftlicher Veränderungen hinzuweisen,
findet bei vielen Menschen Anklang und regt zum Nachdenken an.
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: nomic-ai/modernbert-embed-base
model-index:
- name: SetFit with nomic-ai/modernbert-embed-base
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.9771428571428571
name: Accuracy
---
# SetFit with nomic-ai/modernbert-embed-base
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base) 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:** [nomic-ai/modernbert-embed-base](https://huggingface.co/nomic-ai/modernbert-embed-base)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 8192 tokens
- **Number of Classes:** 3 classes
### 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 |
|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| opposed | - 'Das umstrittene "Heizungsgesetz" sorgt für hitzige Debatten und lässt viele Hausbesitzer deutschlandweit zittern: Drohen uns jetzt hohe Kosten und ein bürokratisches Chaos rund um die verpflichtende Wärmepumpen-Installation? Kritiker warnen vor überstürzten Maßnahmen, die mehr Schaden als Nutzen bringen könnten.'
- 'Die selbsternannten Retter der Welt von Fridays for Future und der Letzten Generation scheinen wieder einmal nichts Besseres zu tun zu haben, als den Alltag der hart arbeitenden Bürger mit ihren fragwürdigen Protestaktionen zu stören. Während sie sich in ihrer moralischen Überlegenheit sonnen, bleibt die Frage offen, wer die Rechnung für ihre chaotischen Stunts zahlen soll.'
- 'Die neueste Gesetzesinitiative zur Einführung eines Tempolimits auf unseren Autobahnen ist ein weiterer Schritt in Richtung Bevormundung der Bürger durch den Staat. Anstatt auf Eigenverantwortung und Freiheit zu setzen, wird mit fragwürdigen Argumenten eine Tradition der deutschen Fahrkultur aufs Spiel gesetzt.'
|
| neutral | - 'Die Bundesregierung hat vorgestern einen Entwurf für ein nationales Tempolimit auf Autobahnen vorgelegt. Demnach entspricht der Vorschlag einem von den EU-Kommissionären geforderten Schritt, um die Verkehrssicherheit zu verbessern. Der Entwurf sieht vor, dass auf ausgewählten Strecken eine Höchstgeschwindigkeit von 130 km/h festgelegt wird.'
- 'Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden Einführung von Wärmepumpen verabschiedet, das den Übergang zu umweltfreundlicheren Heizungssystemen beschleunigen soll. Kritiker warnen vor möglichen finanziellen Belastungen für Hausbesitzer, während Befürworter die Maßnahme als notwendigen Schritt zur Erreichung der Klimaziele betrachten.'
- 'Das Bundeskabinett hat sich auf seiner jüngsten Sitzung mit der Einführung eines nationalen Tempolimits auf Autobahnen auseinandergesetzt. Die Regierung will Gesetzesinitiativen erarbeiten, um die Geschwindigkeiten auf den Hauptverkehrsstrecken in Deutschland zu beschränken. \n\n Quelle: Bundesregierung'
|
| supportive | - 'Die Bundesregierung plant den Ausbau des nationalen Tempolimits auf Autobahnen. Nach Angaben von Verkehrsministerin Wilke soll dies die Verkehrssicherheit und -effizienz erhöhen, insbesondere in Kurven und Tunneln. Die Initiative wird von Umweltverbänden begrüßt, da sie den CO2-Ausstoß reduziert und die Belastung für Autofahrer vermindert.'
- 'Die flächendeckende Einführung von Wärmepumpen durch das neue Heizungsgesetz könnte einen bedeutenden Schritt in Richtung einer nachhaltigeren Energiezukunft darstellen, indem sie den CO2-Ausstoß im Gebäudesektor erheblich reduziert. Zudem verspricht die Initiative, langfristig die Abhängigkeit von fossilen Brennstoffen zu verringern und die Energiekosten für Verbraucher zu stabilisieren.'
- 'In den letzten Tagen haben Demonstranten erneut aufgerufen, um für stärkere Maßnahmen gegen den Klimawandel zu kämpfen. Viele von ihnen gehören zu Gruppen wie Fridays for Future oder Letzte Generation, die sich mit eindrucksvollen Aktionen für ihre Forderungen einsetzen. Ihre Überzeugung und Engagement verdienen Anerkennung.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9771 |
## 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_modernbert_v0.1")
# Run inference
preds = model("Chaos auf den Straßen und genervte Pendler: Die Klima-Aktivisten von Fridays for Future und der Letzten Generation sorgen erneut für Unmut in der Bevölkerung. Während sie für ihre Sache kämpfen, wächst der Frust über ihre umstrittenen Methoden.")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 24 | 44.1632 | 73 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 503 |
| opposed | 536 |
| supportive | 536 |
### Training Hyperparameters
- batch_size: (32, 32)
- num_epochs: (1, 1)
- 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 | 0.343 | - |
| 0.0010 | 50 | 0.3042 | - |
| 0.0019 | 100 | 0.2881 | - |
| 0.0029 | 150 | 0.2698 | - |
| 0.0039 | 200 | 0.2463 | - |
| 0.0048 | 250 | 0.2377 | - |
| 0.0058 | 300 | 0.2319 | - |
| 0.0068 | 350 | 0.2074 | - |
| 0.0077 | 400 | 0.1729 | - |
| 0.0087 | 450 | 0.1458 | - |
| 0.0097 | 500 | 0.1004 | - |
| 0.0106 | 550 | 0.0714 | - |
| 0.0116 | 600 | 0.0452 | - |
| 0.0126 | 650 | 0.028 | - |
| 0.0136 | 700 | 0.0149 | - |
| 0.0145 | 750 | 0.0101 | - |
| 0.0155 | 800 | 0.0067 | - |
| 0.0165 | 850 | 0.0037 | - |
| 0.0174 | 900 | 0.0032 | - |
| 0.0184 | 950 | 0.0023 | - |
| 0.0194 | 1000 | 0.0017 | - |
| 0.0203 | 1050 | 0.0011 | - |
| 0.0213 | 1100 | 0.0006 | - |
| 0.0223 | 1150 | 0.0011 | - |
| 0.0232 | 1200 | 0.0016 | - |
| 0.0242 | 1250 | 0.0021 | - |
| 0.0252 | 1300 | 0.0004 | - |
| 0.0261 | 1350 | 0.0003 | - |
| 0.0271 | 1400 | 0.0009 | - |
| 0.0281 | 1450 | 0.002 | - |
| 0.0290 | 1500 | 0.0008 | - |
| 0.0300 | 1550 | 0.0012 | - |
| 0.0310 | 1600 | 0.0003 | - |
| 0.0319 | 1650 | 0.0002 | - |
| 0.0329 | 1700 | 0.0003 | - |
| 0.0339 | 1750 | 0.0002 | - |
| 0.0348 | 1800 | 0.0001 | - |
| 0.0358 | 1850 | 0.0001 | - |
| 0.0368 | 1900 | 0.0001 | - |
| 0.0377 | 1950 | 0.0001 | - |
| 0.0387 | 2000 | 0.0001 | - |
| 0.0397 | 2050 | 0.0001 | - |
| 0.0407 | 2100 | 0.0001 | - |
| 0.0416 | 2150 | 0.0001 | - |
| 0.0426 | 2200 | 0.0001 | - |
| 0.0436 | 2250 | 0.0001 | - |
| 0.0445 | 2300 | 0.0001 | - |
| 0.0455 | 2350 | 0.0001 | - |
| 0.0465 | 2400 | 0.0001 | - |
| 0.0474 | 2450 | 0.0001 | - |
| 0.0484 | 2500 | 0.0001 | - |
| 0.0494 | 2550 | 0.0 | - |
| 0.0503 | 2600 | 0.0 | - |
| 0.0513 | 2650 | 0.0 | - |
| 0.0523 | 2700 | 0.0 | - |
| 0.0532 | 2750 | 0.0 | - |
| 0.0542 | 2800 | 0.0 | - |
| 0.0552 | 2850 | 0.0 | - |
| 0.0561 | 2900 | 0.0 | - |
| 0.0571 | 2950 | 0.0 | - |
| 0.0581 | 3000 | 0.0 | - |
| 0.0590 | 3050 | 0.0 | - |
| 0.0600 | 3100 | 0.0 | - |
| 0.0610 | 3150 | 0.0 | - |
| 0.0619 | 3200 | 0.0 | - |
| 0.0629 | 3250 | 0.0 | - |
| 0.0639 | 3300 | 0.0 | - |
| 0.0649 | 3350 | 0.0 | - |
| 0.0658 | 3400 | 0.0 | - |
| 0.0668 | 3450 | 0.0 | - |
| 0.0678 | 3500 | 0.0 | - |
| 0.0687 | 3550 | 0.0 | - |
| 0.0697 | 3600 | 0.0 | - |
| 0.0707 | 3650 | 0.0 | - |
| 0.0716 | 3700 | 0.0 | - |
| 0.0726 | 3750 | 0.0 | - |
| 0.0736 | 3800 | 0.0 | - |
| 0.0745 | 3850 | 0.0 | - |
| 0.0755 | 3900 | 0.0 | - |
| 0.0765 | 3950 | 0.0 | - |
| 0.0774 | 4000 | 0.0 | - |
| 0.0784 | 4050 | 0.0 | - |
| 0.0794 | 4100 | 0.0 | - |
| 0.0803 | 4150 | 0.0 | - |
| 0.0813 | 4200 | 0.0 | - |
| 0.0823 | 4250 | 0.0 | - |
| 0.0832 | 4300 | 0.0 | - |
| 0.0842 | 4350 | 0.0 | - |
| 0.0852 | 4400 | 0.0 | - |
| 0.0861 | 4450 | 0.0 | - |
| 0.0871 | 4500 | 0.0 | - |
| 0.0881 | 4550 | 0.0 | - |
| 0.0890 | 4600 | 0.0 | - |
| 0.0900 | 4650 | 0.0 | - |
| 0.0910 | 4700 | 0.0 | - |
| 0.0920 | 4750 | 0.0 | - |
| 0.0929 | 4800 | 0.0 | - |
| 0.0939 | 4850 | 0.0 | - |
| 0.0949 | 4900 | 0.0 | - |
| 0.0958 | 4950 | 0.0 | - |
| 0.0968 | 5000 | 0.0 | - |
| 0.0978 | 5050 | 0.0 | - |
| 0.0987 | 5100 | 0.0 | - |
| 0.0997 | 5150 | 0.0 | - |
| 0.1007 | 5200 | 0.0 | - |
| 0.1016 | 5250 | 0.0 | - |
| 0.1026 | 5300 | 0.0 | - |
| 0.1036 | 5350 | 0.0 | - |
| 0.1045 | 5400 | 0.0 | - |
| 0.1055 | 5450 | 0.0 | - |
| 0.1065 | 5500 | 0.0 | - |
| 0.1074 | 5550 | 0.0 | - |
| 0.1084 | 5600 | 0.0 | - |
| 0.1094 | 5650 | 0.0 | - |
| 0.1103 | 5700 | 0.0 | - |
| 0.1113 | 5750 | 0.0 | - |
| 0.1123 | 5800 | 0.0 | - |
| 0.1132 | 5850 | 0.0 | - |
| 0.1142 | 5900 | 0.0 | - |
| 0.1152 | 5950 | 0.0 | - |
| 0.1162 | 6000 | 0.0 | - |
| 0.1171 | 6050 | 0.0 | - |
| 0.1181 | 6100 | 0.0 | - |
| 0.1191 | 6150 | 0.0 | - |
| 0.1200 | 6200 | 0.0 | - |
| 0.1210 | 6250 | 0.0 | - |
| 0.1220 | 6300 | 0.0 | - |
| 0.1229 | 6350 | 0.0 | - |
| 0.1239 | 6400 | 0.0 | - |
| 0.1249 | 6450 | 0.0 | - |
| 0.1258 | 6500 | 0.0 | - |
| 0.1268 | 6550 | 0.0 | - |
| 0.1278 | 6600 | 0.0 | - |
| 0.1287 | 6650 | 0.0 | - |
| 0.1297 | 6700 | 0.0 | - |
| 0.1307 | 6750 | 0.0 | - |
| 0.1316 | 6800 | 0.0 | - |
| 0.1326 | 6850 | 0.0 | - |
| 0.1336 | 6900 | 0.0 | - |
| 0.1345 | 6950 | 0.0 | - |
| 0.1355 | 7000 | 0.0 | - |
| 0.1365 | 7050 | 0.0 | - |
| 0.1374 | 7100 | 0.0 | - |
| 0.1384 | 7150 | 0.0 | - |
| 0.1394 | 7200 | 0.0 | - |
| 0.1403 | 7250 | 0.0 | - |
| 0.1413 | 7300 | 0.0 | - |
| 0.1423 | 7350 | 0.0 | - |
| 0.1433 | 7400 | 0.0 | - |
| 0.1442 | 7450 | 0.0 | - |
| 0.1452 | 7500 | 0.0 | - |
| 0.1462 | 7550 | 0.0 | - |
| 0.1471 | 7600 | 0.0 | - |
| 0.1481 | 7650 | 0.0 | - |
| 0.1491 | 7700 | 0.0 | - |
| 0.1500 | 7750 | 0.0 | - |
| 0.1510 | 7800 | 0.0 | - |
| 0.1520 | 7850 | 0.0 | - |
| 0.1529 | 7900 | 0.0 | - |
| 0.1539 | 7950 | 0.0 | - |
| 0.1549 | 8000 | 0.0 | - |
| 0.1558 | 8050 | 0.0 | - |
| 0.1568 | 8100 | 0.0 | - |
| 0.1578 | 8150 | 0.0 | - |
| 0.1587 | 8200 | 0.0 | - |
| 0.1597 | 8250 | 0.0 | - |
| 0.1607 | 8300 | 0.0 | - |
| 0.1616 | 8350 | 0.0 | - |
| 0.1626 | 8400 | 0.0 | - |
| 0.1636 | 8450 | 0.0 | - |
| 0.1645 | 8500 | 0.0 | - |
| 0.1655 | 8550 | 0.0 | - |
| 0.1665 | 8600 | 0.0 | - |
| 0.1675 | 8650 | 0.0 | - |
| 0.1684 | 8700 | 0.0 | - |
| 0.1694 | 8750 | 0.0 | - |
| 0.1704 | 8800 | 0.0 | - |
| 0.1713 | 8850 | 0.0 | - |
| 0.1723 | 8900 | 0.0 | - |
| 0.1733 | 8950 | 0.0 | - |
| 0.1742 | 9000 | 0.0 | - |
| 0.1752 | 9050 | 0.0 | - |
| 0.1762 | 9100 | 0.0 | - |
| 0.1771 | 9150 | 0.0 | - |
| 0.1781 | 9200 | 0.0 | - |
| 0.1791 | 9250 | 0.0 | - |
| 0.1800 | 9300 | 0.0 | - |
| 0.1810 | 9350 | 0.0 | - |
| 0.1820 | 9400 | 0.0 | - |
| 0.1829 | 9450 | 0.0 | - |
| 0.1839 | 9500 | 0.0 | - |
| 0.1849 | 9550 | 0.0 | - |
| 0.1858 | 9600 | 0.0 | - |
| 0.1868 | 9650 | 0.0 | - |
| 0.1878 | 9700 | 0.0 | - |
| 0.1887 | 9750 | 0.0 | - |
| 0.1897 | 9800 | 0.0 | - |
| 0.1907 | 9850 | 0.0 | - |
| 0.1916 | 9900 | 0.0 | - |
| 0.1926 | 9950 | 0.0 | - |
| 0.1936 | 10000 | 0.0 | - |
| 0.1946 | 10050 | 0.0 | - |
| 0.1955 | 10100 | 0.0 | - |
| 0.1965 | 10150 | 0.0 | - |
| 0.1975 | 10200 | 0.0 | - |
| 0.1984 | 10250 | 0.0 | - |
| 0.1994 | 10300 | 0.0 | - |
| 0.2004 | 10350 | 0.0 | - |
| 0.2013 | 10400 | 0.0 | - |
| 0.2023 | 10450 | 0.0 | - |
| 0.2033 | 10500 | 0.0 | - |
| 0.2042 | 10550 | 0.0 | - |
| 0.2052 | 10600 | 0.0 | - |
| 0.2062 | 10650 | 0.0 | - |
| 0.2071 | 10700 | 0.1864 | - |
| 0.2081 | 10750 | 0.0643 | - |
| 0.2091 | 10800 | 0.0257 | - |
| 0.2100 | 10850 | 0.0125 | - |
| 0.2110 | 10900 | 0.0097 | - |
| 0.2120 | 10950 | 0.0072 | - |
| 0.2129 | 11000 | 0.0032 | - |
| 0.2139 | 11050 | 0.001 | - |
| 0.2149 | 11100 | 0.0001 | - |
| 0.2158 | 11150 | 0.0001 | - |
| 0.2168 | 11200 | 0.0001 | - |
| 0.2178 | 11250 | 0.0 | - |
| 0.2188 | 11300 | 0.0001 | - |
| 0.2197 | 11350 | 0.0 | - |
| 0.2207 | 11400 | 0.0 | - |
| 0.2217 | 11450 | 0.0 | - |
| 0.2226 | 11500 | 0.0 | - |
| 0.2236 | 11550 | 0.0 | - |
| 0.2246 | 11600 | 0.0 | - |
| 0.2255 | 11650 | 0.0 | - |
| 0.2265 | 11700 | 0.0 | - |
| 0.2275 | 11750 | 0.0 | - |
| 0.2284 | 11800 | 0.0 | - |
| 0.2294 | 11850 | 0.0 | - |
| 0.2304 | 11900 | 0.0 | - |
| 0.2313 | 11950 | 0.0 | - |
| 0.2323 | 12000 | 0.0 | - |
| 0.2333 | 12050 | 0.0 | - |
| 0.2342 | 12100 | 0.0 | - |
| 0.2352 | 12150 | 0.0 | - |
| 0.2362 | 12200 | 0.0 | - |
| 0.2371 | 12250 | 0.0 | - |
| 0.2381 | 12300 | 0.0 | - |
| 0.2391 | 12350 | 0.0 | - |
| 0.2400 | 12400 | 0.0 | - |
| 0.2410 | 12450 | 0.0 | - |
| 0.2420 | 12500 | 0.0 | - |
| 0.2429 | 12550 | 0.0 | - |
| 0.2439 | 12600 | 0.0 | - |
| 0.2449 | 12650 | 0.0 | - |
| 0.2459 | 12700 | 0.0 | - |
| 0.2468 | 12750 | 0.0 | - |
| 0.2478 | 12800 | 0.0 | - |
| 0.2488 | 12850 | 0.0 | - |
| 0.2497 | 12900 | 0.0 | - |
| 0.2507 | 12950 | 0.0 | - |
| 0.2517 | 13000 | 0.0 | - |
| 0.2526 | 13050 | 0.0 | - |
| 0.2536 | 13100 | 0.0 | - |
| 0.2546 | 13150 | 0.0 | - |
| 0.2555 | 13200 | 0.0 | - |
| 0.2565 | 13250 | 0.0 | - |
| 0.2575 | 13300 | 0.0 | - |
| 0.2584 | 13350 | 0.0 | - |
| 0.2594 | 13400 | 0.0 | - |
| 0.2604 | 13450 | 0.0 | - |
| 0.2613 | 13500 | 0.0 | - |
| 0.2623 | 13550 | 0.0 | - |
| 0.2633 | 13600 | 0.0 | - |
| 0.2642 | 13650 | 0.0 | - |
| 0.2652 | 13700 | 0.0 | - |
| 0.2662 | 13750 | 0.0 | - |
| 0.2671 | 13800 | 0.0 | - |
| 0.2681 | 13850 | 0.0 | - |
| 0.2691 | 13900 | 0.0 | - |
| 0.2701 | 13950 | 0.0 | - |
| 0.2710 | 14000 | 0.0 | - |
| 0.2720 | 14050 | 0.0 | - |
| 0.2730 | 14100 | 0.0 | - |
| 0.2739 | 14150 | 0.0 | - |
| 0.2749 | 14200 | 0.0 | - |
| 0.2759 | 14250 | 0.0 | - |
| 0.2768 | 14300 | 0.0 | - |
| 0.2778 | 14350 | 0.0 | - |
| 0.2788 | 14400 | 0.0 | - |
| 0.2797 | 14450 | 0.0 | - |
| 0.2807 | 14500 | 0.0 | - |
| 0.2817 | 14550 | 0.0 | - |
| 0.2826 | 14600 | 0.0 | - |
| 0.2836 | 14650 | 0.0 | - |
| 0.2846 | 14700 | 0.0 | - |
| 0.2855 | 14750 | 0.0 | - |
| 0.2865 | 14800 | 0.0 | - |
| 0.2875 | 14850 | 0.0 | - |
| 0.2884 | 14900 | 0.0 | - |
| 0.2894 | 14950 | 0.0 | - |
| 0.2904 | 15000 | 0.0 | - |
| 0.2913 | 15050 | 0.0 | - |
| 0.2923 | 15100 | 0.0 | - |
| 0.2933 | 15150 | 0.0 | - |
| 0.2942 | 15200 | 0.0 | - |
| 0.2952 | 15250 | 0.0 | - |
| 0.2962 | 15300 | 0.0 | - |
| 0.2972 | 15350 | 0.0 | - |
| 0.2981 | 15400 | 0.0 | - |
| 0.2991 | 15450 | 0.0 | - |
| 0.3001 | 15500 | 0.0 | - |
| 0.3010 | 15550 | 0.0 | - |
| 0.3020 | 15600 | 0.0 | - |
| 0.3030 | 15650 | 0.0 | - |
| 0.3039 | 15700 | 0.0 | - |
| 0.3049 | 15750 | 0.0 | - |
| 0.3059 | 15800 | 0.0 | - |
| 0.3068 | 15850 | 0.0 | - |
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| 0.7695 | 39750 | 0.0 | - |
| 0.7705 | 39800 | 0.0 | - |
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| 0.7753 | 40050 | 0.0 | - |
| 0.7763 | 40100 | 0.0 | - |
| 0.7772 | 40150 | 0.0 | - |
| 0.7782 | 40200 | 0.0 | - |
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| 0.7801 | 40300 | 0.0 | - |
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| 0.7937 | 41000 | 0.0 | - |
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| 0.7976 | 41200 | 0.0 | - |
| 0.7985 | 41250 | 0.0 | - |
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| 0.8034 | 41500 | 0.0 | - |
| 0.8043 | 41550 | 0.0 | - |
| 0.8053 | 41600 | 0.0 | - |
| 0.8063 | 41650 | 0.0 | - |
| 0.8072 | 41700 | 0.0 | - |
| 0.8082 | 41750 | 0.0 | - |
| 0.8092 | 41800 | 0.0 | - |
| 0.8102 | 41850 | 0.0 | - |
| 0.8111 | 41900 | 0.0 | - |
| 0.8121 | 41950 | 0.0 | - |
| 0.8131 | 42000 | 0.0 | - |
| 0.8140 | 42050 | 0.0 | - |
| 0.8150 | 42100 | 0.0 | - |
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| 0.8169 | 42200 | 0.0 | - |
| 0.8179 | 42250 | 0.0 | - |
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| 0.8198 | 42350 | 0.0 | - |
| 0.8208 | 42400 | 0.0 | - |
| 0.8218 | 42450 | 0.0 | - |
| 0.8227 | 42500 | 0.0 | - |
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| 0.8247 | 42600 | 0.0 | - |
| 0.8256 | 42650 | 0.0 | - |
| 0.8266 | 42700 | 0.0 | - |
| 0.8276 | 42750 | 0.0 | - |
| 0.8285 | 42800 | 0.0 | - |
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| 0.8324 | 43000 | 0.0 | - |
| 0.8334 | 43050 | 0.0 | - |
| 0.8343 | 43100 | 0.0 | - |
| 0.8353 | 43150 | 0.0 | - |
| 0.8363 | 43200 | 0.0 | - |
| 0.8373 | 43250 | 0.0 | - |
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| 0.8392 | 43350 | 0.0 | - |
| 0.8402 | 43400 | 0.0 | - |
| 0.8411 | 43450 | 0.0 | - |
| 0.8421 | 43500 | 0.0 | - |
| 0.8431 | 43550 | 0.0 | - |
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| 0.8469 | 43750 | 0.0 | - |
| 0.8479 | 43800 | 0.0 | - |
| 0.8489 | 43850 | 0.0 | - |
| 0.8498 | 43900 | 0.0 | - |
| 0.8508 | 43950 | 0.0 | - |
| 0.8518 | 44000 | 0.0 | - |
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| 0.8537 | 44100 | 0.0 | - |
| 0.8547 | 44150 | 0.0 | - |
| 0.8556 | 44200 | 0.0 | - |
| 0.8566 | 44250 | 0.0 | - |
| 0.8576 | 44300 | 0.0 | - |
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| 0.8663 | 44750 | 0.0 | - |
| 0.8673 | 44800 | 0.0 | - |
| 0.8682 | 44850 | 0.0 | - |
| 0.8692 | 44900 | 0.0 | - |
| 0.8702 | 44950 | 0.0 | - |
| 0.8711 | 45000 | 0.0 | - |
| 0.8721 | 45050 | 0.0 | - |
| 0.8731 | 45100 | 0.0 | - |
| 0.8740 | 45150 | 0.0 | - |
| 0.8750 | 45200 | 0.0 | - |
| 0.8760 | 45250 | 0.0 | - |
| 0.8769 | 45300 | 0.0 | - |
| 0.8779 | 45350 | 0.0 | - |
| 0.8789 | 45400 | 0.0 | - |
| 0.8798 | 45450 | 0.0 | - |
| 0.8808 | 45500 | 0.0 | - |
| 0.8818 | 45550 | 0.0 | - |
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| 0.8837 | 45650 | 0.0 | - |
| 0.8847 | 45700 | 0.0 | - |
| 0.8856 | 45750 | 0.0 | - |
| 0.8866 | 45800 | 0.0 | - |
| 0.8876 | 45850 | 0.0 | - |
| 0.8886 | 45900 | 0.0 | - |
| 0.8895 | 45950 | 0.0 | - |
| 0.8905 | 46000 | 0.0 | - |
| 0.8915 | 46050 | 0.0 | - |
| 0.8924 | 46100 | 0.0 | - |
| 0.8934 | 46150 | 0.0 | - |
| 0.8944 | 46200 | 0.0 | - |
| 0.8953 | 46250 | 0.0 | - |
| 0.8963 | 46300 | 0.0 | - |
| 0.8973 | 46350 | 0.0 | - |
| 0.8982 | 46400 | 0.0 | - |
| 0.8992 | 46450 | 0.0 | - |
| 0.9002 | 46500 | 0.0 | - |
| 0.9011 | 46550 | 0.0 | - |
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| 0.9031 | 46650 | 0.0 | - |
| 0.9040 | 46700 | 0.0 | - |
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| 0.9060 | 46800 | 0.0 | - |
| 0.9069 | 46850 | 0.0 | - |
| 0.9079 | 46900 | 0.0 | - |
| 0.9089 | 46950 | 0.0 | - |
| 0.9098 | 47000 | 0.0 | - |
| 0.9108 | 47050 | 0.0 | - |
| 0.9118 | 47100 | 0.0 | - |
| 0.9128 | 47150 | 0.0 | - |
| 0.9137 | 47200 | 0.0 | - |
| 0.9147 | 47250 | 0.0 | - |
| 0.9157 | 47300 | 0.0 | - |
| 0.9166 | 47350 | 0.0 | - |
| 0.9176 | 47400 | 0.0 | - |
| 0.9186 | 47450 | 0.0 | - |
| 0.9195 | 47500 | 0.0 | - |
| 0.9205 | 47550 | 0.0 | - |
| 0.9215 | 47600 | 0.0 | - |
| 0.9224 | 47650 | 0.0 | - |
| 0.9234 | 47700 | 0.0 | - |
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| 0.9263 | 47850 | 0.0 | - |
| 0.9273 | 47900 | 0.0 | - |
| 0.9282 | 47950 | 0.0 | - |
| 0.9292 | 48000 | 0.0 | - |
| 0.9302 | 48050 | 0.0 | - |
| 0.9311 | 48100 | 0.0 | - |
| 0.9321 | 48150 | 0.0 | - |
| 0.9331 | 48200 | 0.0 | - |
| 0.9340 | 48250 | 0.0 | - |
| 0.9350 | 48300 | 0.0 | - |
| 0.9360 | 48350 | 0.0 | - |
| 0.9369 | 48400 | 0.0 | - |
| 0.9379 | 48450 | 0.0 | - |
| 0.9389 | 48500 | 0.0 | - |
| 0.9399 | 48550 | 0.0 | - |
| 0.9408 | 48600 | 0.0 | - |
| 0.9418 | 48650 | 0.0 | - |
| 0.9428 | 48700 | 0.0 | - |
| 0.9437 | 48750 | 0.0 | - |
| 0.9447 | 48800 | 0.0 | - |
| 0.9457 | 48850 | 0.0 | - |
| 0.9466 | 48900 | 0.0 | - |
| 0.9476 | 48950 | 0.0 | - |
| 0.9486 | 49000 | 0.0 | - |
| 0.9495 | 49050 | 0.0 | - |
| 0.9505 | 49100 | 0.0 | - |
| 0.9515 | 49150 | 0.0 | - |
| 0.9524 | 49200 | 0.0 | - |
| 0.9534 | 49250 | 0.0 | - |
| 0.9544 | 49300 | 0.0 | - |
| 0.9553 | 49350 | 0.0 | - |
| 0.9563 | 49400 | 0.0 | - |
| 0.9573 | 49450 | 0.0 | - |
| 0.9582 | 49500 | 0.0 | - |
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| 0.9602 | 49600 | 0.0 | - |
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| 0.9621 | 49700 | 0.0 | - |
| 0.9631 | 49750 | 0.0 | - |
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| 0.9679 | 50000 | 0.0 | - |
| 0.9689 | 50050 | 0.0 | - |
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| 0.9708 | 50150 | 0.0 | - |
| 0.9718 | 50200 | 0.0 | - |
| 0.9728 | 50250 | 0.0 | - |
| 0.9737 | 50300 | 0.0 | - |
| 0.9747 | 50350 | 0.0 | - |
| 0.9757 | 50400 | 0.0 | - |
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| 0.9795 | 50600 | 0.0 | - |
| 0.9805 | 50650 | 0.0 | - |
| 0.9815 | 50700 | 0.0 | - |
| 0.9824 | 50750 | 0.0 | - |
| 0.9834 | 50800 | 0.0 | - |
| 0.9844 | 50850 | 0.0 | - |
| 0.9853 | 50900 | 0.0 | - |
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| 0.9970 | 51500 | 0.0 | - |
| 0.9979 | 51550 | 0.0 | - |
| 0.9989 | 51600 | 0.0 | - |
| 0.9999 | 51650 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.2.0.dev0
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0.dev0
- PyTorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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}
}
```