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---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: ' "Die Diskussion um ein nationales Tempolimit auf Autobahnen wird weiterhin
kontrovers geführt, wobei Befürworter auf die Verbesserung der Verkehrssicherheit
und Gegner auf die Einschränkung der individuellen Freiheit verweisen."'
- text: ' Diese selbsternannten Klimaretter blockieren wieder einmal die Straßen und
sorgen für Chaos, während der Rest der Welt zur Arbeit gehen muss.'
- text: ' Klima-Aktivisten wie Fridays for Future und die Letzte Generation fordern
mit ihren Aktionen eine schnellere Umsetzung von Klimaschutzmaßnahmen.'
- text: Die Blockaden von Straßen und Autobahnen durch Klima-Aktivisten haben in den
letzten Wochen sowohl Unterstützung als auch Kritik hervorgerufen, wobei einige
die Dringlichkeit des Klimanotstands betonen, während andere die Beeinträchtigung
des Alltags und die Rechte anderer Nutzer der Verkehrsinfrastruktur in den Vordergrund
stellen.
- text: ' "Ein nationales Tempolimit auf Autobahnen könnte nicht nur die Verkehrssicherheit
erhöhen, sondern auch einen wichtigen Beitrag zum Klimaschutz leisten."'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/all-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.946236559139785
name: Accuracy
---
# SetFit with sentence-transformers/all-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-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.
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:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 384 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 |
|:-----------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral | <ul><li>' Klima-Aktivisten von Fridays for Future und der Letzten Generation setzen weiterhin auf öffentliche Aktionen, um auf die Dringlichkeit des Klimawandels hinzuweisen.'</li><li>' Die Gesetzesinitiative zur flächendeckenden Einführung von Wärmepumpen wird kontrovers diskutiert, da sie sowohl ökologische Vorteile als auch erhebliche wirtschaftliche Herausforderungen mit sich bringt.'</li><li>'Ein Gesetzesentwurf der Bundesregierung sieht vor, dass zukünftig auf allen Autobahnen in Deutschland eine Höchstgeschwindigkeit von 130 Kilometern pro Stunde gelten soll, um den Ausstoß von Kohlendioxid zu reduzieren und die Verkehrssicherheit zu erhöhen.'</li></ul> |
| opposed | <ul><li>' "Diese Klima-Aktivisten blockieren wieder einmal die Straßen und bringen den Verkehr zum Erliegen – als ob das die Welt retten würde!"'</li><li>'Die Blockaden von Straßen und Autobahnen durch die Letzte Generation haben in den letzten Wochen zu massiven Behinderungen im Berufsverkehr geführt und die Nerven der Pendler strapaziert.'</li><li>'Wer hierzulande die Freiheit auf der Autobahn einschränken will, gefährdet auch die Freizeit und die Urlaubsfahrten vieler Menschen.'</li></ul> |
| supportive | <ul><li>' Die Aktionen von Klima-Aktivismus-Gruppen wie Fridays for Future oder die Letze Generation zeigen eindrucksvoll, dass junge Menschen bereit sind, für eine lebenswerte Zukunft zu kämpfen.'</li><li>'Die Bundesregierung setzt mit dem Heizungsgesetz auf eine entscheidende Weichenstellung, um den Ausstoß von Treibhausgasen in der Gebäudewärme zu reduzieren und gleichzeitig die Abhängigkeit von fossilen Energieträgern zu verringern.'</li><li>' Die Aktionen von Gruppen wie Fridays for Future und der Letzten Generation zeigen eindrucksvoll, dass die jüngere Generation bereit ist, Verantwortung für unsere Zukunft zu übernehmen.'</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9462 |
## 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/all-mpnet-base-klimacoder_v0.7")
# Run inference
preds = model(" Diese selbsternannten Klimaretter blockieren wieder einmal die Straßen und sorgen für Chaos, während der Rest der Welt zur Arbeit gehen muss.")
```
<!--
### 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 | 10 | 25.6075 | 57 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 329 |
| opposed | 395 |
| supportive | 392 |
### 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 | 0.2421 | - |
| 0.0019 | 50 | 0.259 | - |
| 0.0039 | 100 | 0.2536 | - |
| 0.0058 | 150 | 0.25 | - |
| 0.0077 | 200 | 0.243 | - |
| 0.0097 | 250 | 0.2441 | - |
| 0.0116 | 300 | 0.2377 | - |
| 0.0135 | 350 | 0.2247 | - |
| 0.0155 | 400 | 0.2031 | - |
| 0.0174 | 450 | 0.1656 | - |
| 0.0193 | 500 | 0.1383 | - |
| 0.0213 | 550 | 0.1383 | - |
| 0.0232 | 600 | 0.1155 | - |
| 0.0251 | 650 | 0.1007 | - |
| 0.0271 | 700 | 0.0741 | - |
| 0.0290 | 750 | 0.063 | - |
| 0.0309 | 800 | 0.0428 | - |
| 0.0329 | 850 | 0.0304 | - |
| 0.0348 | 900 | 0.0243 | - |
| 0.0367 | 950 | 0.0189 | - |
| 0.0387 | 1000 | 0.0135 | - |
| 0.0406 | 1050 | 0.0089 | - |
| 0.0425 | 1100 | 0.0115 | - |
| 0.0445 | 1150 | 0.0071 | - |
| 0.0464 | 1200 | 0.0068 | - |
| 0.0483 | 1250 | 0.0057 | - |
| 0.0503 | 1300 | 0.0052 | - |
| 0.0522 | 1350 | 0.0063 | - |
| 0.0541 | 1400 | 0.0064 | - |
| 0.0561 | 1450 | 0.006 | - |
| 0.0580 | 1500 | 0.0035 | - |
| 0.0599 | 1550 | 0.008 | - |
| 0.0619 | 1600 | 0.0069 | - |
| 0.0638 | 1650 | 0.0021 | - |
| 0.0657 | 1700 | 0.0037 | - |
| 0.0677 | 1750 | 0.0034 | - |
| 0.0696 | 1800 | 0.0049 | - |
| 0.0715 | 1850 | 0.0024 | - |
| 0.0735 | 1900 | 0.0085 | - |
| 0.0754 | 1950 | 0.0075 | - |
| 0.0773 | 2000 | 0.0073 | - |
| 0.0793 | 2050 | 0.0031 | - |
| 0.0812 | 2100 | 0.0031 | - |
| 0.0831 | 2150 | 0.0017 | - |
| 0.0851 | 2200 | 0.0024 | - |
| 0.0870 | 2250 | 0.0026 | - |
| 0.0889 | 2300 | 0.0033 | - |
| 0.0909 | 2350 | 0.0097 | - |
| 0.0928 | 2400 | 0.0079 | - |
| 0.0947 | 2450 | 0.0028 | - |
| 0.0967 | 2500 | 0.0021 | - |
| 0.0986 | 2550 | 0.0015 | - |
| 0.1005 | 2600 | 0.0018 | - |
| 0.1025 | 2650 | 0.0028 | - |
| 0.1044 | 2700 | 0.0045 | - |
| 0.1063 | 2750 | 0.0029 | - |
| 0.1083 | 2800 | 0.0007 | - |
| 0.1102 | 2850 | 0.0 | - |
| 0.1121 | 2900 | 0.0008 | - |
| 0.1141 | 2950 | 0.0017 | - |
| 0.1160 | 3000 | 0.0018 | - |
| 0.1179 | 3050 | 0.0014 | - |
| 0.1199 | 3100 | 0.0012 | - |
| 0.1218 | 3150 | 0.001 | - |
| 0.1237 | 3200 | 0.0016 | - |
| 0.1257 | 3250 | 0.0043 | - |
| 0.1276 | 3300 | 0.0001 | - |
| 0.1295 | 3350 | 0.0017 | - |
| 0.1315 | 3400 | 0.0003 | - |
| 0.1334 | 3450 | 0.0004 | - |
| 0.1353 | 3500 | 0.0014 | - |
| 0.1373 | 3550 | 0.0001 | - |
| 0.1392 | 3600 | 0.0 | - |
| 0.1411 | 3650 | 0.0012 | - |
| 0.1431 | 3700 | 0.0005 | - |
| 0.1450 | 3750 | 0.0 | - |
| 0.1469 | 3800 | 0.0 | - |
| 0.1489 | 3850 | 0.0 | - |
| 0.1508 | 3900 | 0.0 | - |
| 0.1527 | 3950 | 0.0 | - |
| 0.1547 | 4000 | 0.0061 | - |
| 0.1566 | 4050 | 0.0014 | - |
| 0.1585 | 4100 | 0.0005 | - |
| 0.1605 | 4150 | 0.0005 | - |
| 0.1624 | 4200 | 0.0001 | - |
| 0.1643 | 4250 | 0.0003 | - |
| 0.1663 | 4300 | 0.0033 | - |
| 0.1682 | 4350 | 0.0049 | - |
| 0.1701 | 4400 | 0.0012 | - |
| 0.1721 | 4450 | 0.0 | - |
| 0.1740 | 4500 | 0.0012 | - |
| 0.1759 | 4550 | 0.0006 | - |
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| 0.1914 | 4950 | 0.0 | - |
| 0.1933 | 5000 | 0.0 | - |
| 0.1953 | 5050 | 0.0 | - |
| 0.1972 | 5100 | 0.0 | - |
| 0.1991 | 5150 | 0.0 | - |
| 0.2011 | 5200 | 0.0 | - |
| 0.2030 | 5250 | 0.0 | - |
| 0.2049 | 5300 | 0.0091 | - |
| 0.2069 | 5350 | 0.0118 | - |
| 0.2088 | 5400 | 0.0032 | - |
| 0.2107 | 5450 | 0.0009 | - |
| 0.2127 | 5500 | 0.0011 | - |
| 0.2146 | 5550 | 0.0015 | - |
| 0.2165 | 5600 | 0.0026 | - |
| 0.2185 | 5650 | 0.0016 | - |
| 0.2204 | 5700 | 0.0 | - |
| 0.2223 | 5750 | 0.0019 | - |
| 0.2243 | 5800 | 0.0039 | - |
| 0.2262 | 5850 | 0.0005 | - |
| 0.2281 | 5900 | 0.0006 | - |
| 0.2301 | 5950 | 0.0015 | - |
| 0.2320 | 6000 | 0.0018 | - |
| 0.2339 | 6050 | 0.0012 | - |
| 0.2359 | 6100 | 0.0042 | - |
| 0.2378 | 6150 | 0.0016 | - |
| 0.2397 | 6200 | 0.0011 | - |
| 0.2417 | 6250 | 0.0 | - |
| 0.2436 | 6300 | 0.0 | - |
| 0.2455 | 6350 | 0.0025 | - |
| 0.2475 | 6400 | 0.0012 | - |
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| 0.2707 | 7000 | 0.0 | - |
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| 0.2745 | 7100 | 0.0 | - |
| 0.2765 | 7150 | 0.0 | - |
| 0.2784 | 7200 | 0.0 | - |
| 0.2803 | 7250 | 0.0 | - |
| 0.2823 | 7300 | 0.0 | - |
| 0.2842 | 7350 | 0.0 | - |
| 0.2861 | 7400 | 0.0 | - |
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| 0.2939 | 7600 | 0.0 | - |
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| 0.3074 | 7950 | 0.0 | - |
| 0.3093 | 8000 | 0.0 | - |
| 0.3113 | 8050 | 0.0 | - |
| 0.3132 | 8100 | 0.0 | - |
| 0.3151 | 8150 | 0.0 | - |
| 0.3171 | 8200 | 0.0 | - |
| 0.3190 | 8250 | 0.0 | - |
| 0.3209 | 8300 | 0.0 | - |
| 0.3229 | 8350 | 0.0 | - |
| 0.3248 | 8400 | 0.0 | - |
| 0.3267 | 8450 | 0.0 | - |
| 0.3287 | 8500 | 0.0 | - |
| 0.3306 | 8550 | 0.0 | - |
| 0.3325 | 8600 | 0.0 | - |
| 0.3345 | 8650 | 0.0 | - |
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| 0.3403 | 8800 | 0.0 | - |
| 0.3422 | 8850 | 0.0 | - |
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| 0.3480 | 9000 | 0.0 | - |
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| 0.3519 | 9100 | 0.0 | - |
| 0.3538 | 9150 | 0.0 | - |
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| 0.3596 | 9300 | 0.0 | - |
| 0.3615 | 9350 | 0.0 | - |
| 0.3635 | 9400 | 0.0 | - |
| 0.3654 | 9450 | 0.0081 | - |
| 0.3673 | 9500 | 0.0078 | - |
| 0.3693 | 9550 | 0.0104 | - |
| 0.3712 | 9600 | 0.0034 | - |
| 0.3731 | 9650 | 0.0009 | - |
| 0.3751 | 9700 | 0.0006 | - |
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| 0.3809 | 9850 | 0.0 | - |
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| 0.3867 | 10000 | 0.0006 | - |
| 0.3886 | 10050 | 0.0 | - |
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| 0.3944 | 10200 | 0.0 | - |
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| 0.3983 | 10300 | 0.0 | - |
| 0.4002 | 10350 | 0.0 | - |
| 0.4021 | 10400 | 0.0 | - |
| 0.4041 | 10450 | 0.0019 | - |
| 0.4060 | 10500 | 0.0035 | - |
| 0.4080 | 10550 | 0.0012 | - |
| 0.4099 | 10600 | 0.0 | - |
| 0.4118 | 10650 | 0.0 | - |
| 0.4138 | 10700 | 0.0 | - |
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| 0.4176 | 10800 | 0.0 | - |
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| 0.4234 | 10950 | 0.0006 | - |
| 0.4254 | 11000 | 0.0 | - |
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| 0.4389 | 11350 | 0.0 | - |
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| 0.4466 | 11550 | 0.0 | - |
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| 0.4505 | 11650 | 0.0 | - |
| 0.4524 | 11700 | 0.0 | - |
| 0.4544 | 11750 | 0.0 | - |
| 0.4563 | 11800 | 0.0 | - |
| 0.4582 | 11850 | 0.0 | - |
| 0.4602 | 11900 | 0.0 | - |
| 0.4621 | 11950 | 0.0 | - |
| 0.4640 | 12000 | 0.0 | - |
| 0.4660 | 12050 | 0.0 | - |
| 0.4679 | 12100 | 0.0 | - |
| 0.4698 | 12150 | 0.0 | - |
| 0.4718 | 12200 | 0.0 | - |
| 0.4737 | 12250 | 0.0 | - |
| 0.4756 | 12300 | 0.0 | - |
| 0.4776 | 12350 | 0.0 | - |
| 0.4795 | 12400 | 0.0 | - |
| 0.4814 | 12450 | 0.0 | - |
| 0.4834 | 12500 | 0.0 | - |
| 0.4853 | 12550 | 0.0 | - |
| 0.4872 | 12600 | 0.0 | - |
| 0.4892 | 12650 | 0.0 | - |
| 0.4911 | 12700 | 0.0 | - |
| 0.4930 | 12750 | 0.0 | - |
| 0.4950 | 12800 | 0.0 | - |
| 0.4969 | 12850 | 0.0 | - |
| 0.4988 | 12900 | 0.0 | - |
| 0.5008 | 12950 | 0.0 | - |
| 0.5027 | 13000 | 0.0 | - |
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| 0.5066 | 13100 | 0.0 | - |
| 0.5085 | 13150 | 0.0 | - |
| 0.5104 | 13200 | 0.0 | - |
| 0.5124 | 13250 | 0.0 | - |
| 0.5143 | 13300 | 0.0 | - |
| 0.5162 | 13350 | 0.0 | - |
| 0.5182 | 13400 | 0.0 | - |
| 0.5201 | 13450 | 0.0 | - |
| 0.5220 | 13500 | 0.0 | - |
| 0.5240 | 13550 | 0.0 | - |
| 0.5259 | 13600 | 0.0 | - |
| 0.5278 | 13650 | 0.0 | - |
| 0.5298 | 13700 | 0.0 | - |
| 0.5317 | 13750 | 0.0 | - |
| 0.5336 | 13800 | 0.0 | - |
| 0.5356 | 13850 | 0.0 | - |
| 0.5375 | 13900 | 0.0 | - |
| 0.5394 | 13950 | 0.0 | - |
| 0.5414 | 14000 | 0.0 | - |
| 0.5433 | 14050 | 0.0 | - |
| 0.5452 | 14100 | 0.0 | - |
| 0.5472 | 14150 | 0.0 | - |
| 0.5491 | 14200 | 0.0 | - |
| 0.5510 | 14250 | 0.0 | - |
| 0.5530 | 14300 | 0.0 | - |
| 0.5549 | 14350 | 0.0 | - |
| 0.5568 | 14400 | 0.0 | - |
| 0.5588 | 14450 | 0.0 | - |
| 0.5607 | 14500 | 0.0 | - |
| 0.5626 | 14550 | 0.0 | - |
| 0.5646 | 14600 | 0.0 | - |
| 0.5665 | 14650 | 0.0 | - |
| 0.5684 | 14700 | 0.0 | - |
| 0.5704 | 14750 | 0.0 | - |
| 0.5723 | 14800 | 0.0 | - |
| 0.5742 | 14850 | 0.0 | - |
| 0.5762 | 14900 | 0.0 | - |
| 0.5781 | 14950 | 0.0 | - |
| 0.5800 | 15000 | 0.0 | - |
| 0.5820 | 15050 | 0.0 | - |
| 0.5839 | 15100 | 0.0 | - |
| 0.5858 | 15150 | 0.0 | - |
| 0.5878 | 15200 | 0.0 | - |
| 0.5897 | 15250 | 0.0 | - |
| 0.5916 | 15300 | 0.0 | - |
| 0.5936 | 15350 | 0.0 | - |
| 0.5955 | 15400 | 0.0 | - |
| 0.5974 | 15450 | 0.0 | - |
| 0.5994 | 15500 | 0.0 | - |
| 0.6013 | 15550 | 0.0 | - |
| 0.6032 | 15600 | 0.0 | - |
| 0.6052 | 15650 | 0.0 | - |
| 0.6071 | 15700 | 0.0 | - |
| 0.6090 | 15750 | 0.0 | - |
| 0.6110 | 15800 | 0.0 | - |
| 0.6129 | 15850 | 0.0 | - |
| 0.6148 | 15900 | 0.0 | - |
| 0.6168 | 15950 | 0.0 | - |
| 0.6187 | 16000 | 0.0 | - |
| 0.6206 | 16050 | 0.0 | - |
| 0.6226 | 16100 | 0.0 | - |
| 0.6245 | 16150 | 0.0 | - |
| 0.6264 | 16200 | 0.0 | - |
| 0.6284 | 16250 | 0.0 | - |
| 0.6303 | 16300 | 0.0 | - |
| 0.6322 | 16350 | 0.0 | - |
| 0.6342 | 16400 | 0.0 | - |
| 0.6361 | 16450 | 0.0 | - |
| 0.6380 | 16500 | 0.0 | - |
| 0.6400 | 16550 | 0.0 | - |
| 0.6419 | 16600 | 0.0 | - |
| 0.6438 | 16650 | 0.0 | - |
| 0.6458 | 16700 | 0.0 | - |
| 0.6477 | 16750 | 0.0 | - |
| 0.6496 | 16800 | 0.0 | - |
| 0.6516 | 16850 | 0.0 | - |
| 0.6535 | 16900 | 0.0 | - |
| 0.6554 | 16950 | 0.0 | - |
| 0.6574 | 17000 | 0.0 | - |
| 0.6593 | 17050 | 0.0 | - |
| 0.6612 | 17100 | 0.0 | - |
| 0.6632 | 17150 | 0.0 | - |
| 0.6651 | 17200 | 0.0 | - |
| 0.6670 | 17250 | 0.0 | - |
| 0.6690 | 17300 | 0.0 | - |
| 0.6709 | 17350 | 0.0 | - |
| 0.6728 | 17400 | 0.0 | - |
| 0.6748 | 17450 | 0.0 | - |
| 0.6767 | 17500 | 0.0 | - |
| 0.6786 | 17550 | 0.0 | - |
| 0.6806 | 17600 | 0.0 | - |
| 0.6825 | 17650 | 0.0 | - |
| 0.6844 | 17700 | 0.0 | - |
| 0.6864 | 17750 | 0.0 | - |
| 0.6883 | 17800 | 0.0 | - |
| 0.6902 | 17850 | 0.0 | - |
| 0.6922 | 17900 | 0.0 | - |
| 0.6941 | 17950 | 0.0 | - |
| 0.6960 | 18000 | 0.0 | - |
| 0.6980 | 18050 | 0.0 | - |
| 0.6999 | 18100 | 0.0 | - |
| 0.7018 | 18150 | 0.0 | - |
| 0.7038 | 18200 | 0.0 | - |
| 0.7057 | 18250 | 0.0 | - |
| 0.7076 | 18300 | 0.0 | - |
| 0.7096 | 18350 | 0.0 | - |
| 0.7115 | 18400 | 0.0 | - |
| 0.7134 | 18450 | 0.0 | - |
| 0.7154 | 18500 | 0.0 | - |
| 0.7173 | 18550 | 0.0 | - |
| 0.7192 | 18600 | 0.0 | - |
| 0.7212 | 18650 | 0.0 | - |
| 0.7231 | 18700 | 0.0 | - |
| 0.7250 | 18750 | 0.0 | - |
| 0.7270 | 18800 | 0.0 | - |
| 0.7289 | 18850 | 0.0 | - |
| 0.7308 | 18900 | 0.0 | - |
| 0.7328 | 18950 | 0.0 | - |
| 0.7347 | 19000 | 0.0 | - |
| 0.7366 | 19050 | 0.0 | - |
| 0.7386 | 19100 | 0.0 | - |
| 0.7405 | 19150 | 0.0 | - |
| 0.7424 | 19200 | 0.0 | - |
| 0.7444 | 19250 | 0.0 | - |
| 0.7463 | 19300 | 0.0 | - |
| 0.7482 | 19350 | 0.0 | - |
| 0.7502 | 19400 | 0.0 | - |
| 0.7521 | 19450 | 0.0 | - |
| 0.7540 | 19500 | 0.0 | - |
| 0.7560 | 19550 | 0.0 | - |
| 0.7579 | 19600 | 0.0 | - |
| 0.7598 | 19650 | 0.0 | - |
| 0.7618 | 19700 | 0.0 | - |
| 0.7637 | 19750 | 0.0 | - |
| 0.7656 | 19800 | 0.0 | - |
| 0.7676 | 19850 | 0.0 | - |
| 0.7695 | 19900 | 0.0 | - |
| 0.7714 | 19950 | 0.0 | - |
| 0.7734 | 20000 | 0.0 | - |
| 0.7753 | 20050 | 0.0 | - |
| 0.7772 | 20100 | 0.0 | - |
| 0.7792 | 20150 | 0.0 | - |
| 0.7811 | 20200 | 0.0 | - |
| 0.7830 | 20250 | 0.0 | - |
| 0.7850 | 20300 | 0.0 | - |
| 0.7869 | 20350 | 0.0 | - |
| 0.7888 | 20400 | 0.0 | - |
| 0.7908 | 20450 | 0.0 | - |
| 0.7927 | 20500 | 0.0 | - |
| 0.7946 | 20550 | 0.0 | - |
| 0.7966 | 20600 | 0.0 | - |
| 0.7985 | 20650 | 0.0 | - |
| 0.8004 | 20700 | 0.0 | - |
| 0.8024 | 20750 | 0.0 | - |
| 0.8043 | 20800 | 0.0 | - |
| 0.8062 | 20850 | 0.0 | - |
| 0.8082 | 20900 | 0.0 | - |
| 0.8101 | 20950 | 0.0 | - |
| 0.8120 | 21000 | 0.0 | - |
| 0.8140 | 21050 | 0.0 | - |
| 0.8159 | 21100 | 0.0 | - |
| 0.8178 | 21150 | 0.0 | - |
| 0.8198 | 21200 | 0.0002 | - |
| 0.8217 | 21250 | 0.0027 | - |
| 0.8236 | 21300 | 0.0019 | - |
| 0.8256 | 21350 | 0.0 | - |
| 0.8275 | 21400 | 0.0 | - |
| 0.8294 | 21450 | 0.0 | - |
| 0.8314 | 21500 | 0.0 | - |
| 0.8333 | 21550 | 0.0011 | - |
| 0.8352 | 21600 | 0.0 | - |
| 0.8372 | 21650 | 0.0 | - |
| 0.8391 | 21700 | 0.0 | - |
| 0.8410 | 21750 | 0.0 | - |
| 0.8430 | 21800 | 0.0 | - |
| 0.8449 | 21850 | 0.0 | - |
| 0.8468 | 21900 | 0.0 | - |
| 0.8488 | 21950 | 0.0006 | - |
| 0.8507 | 22000 | 0.0 | - |
| 0.8526 | 22050 | 0.0 | - |
| 0.8546 | 22100 | 0.0002 | - |
| 0.8565 | 22150 | 0.0 | - |
| 0.8584 | 22200 | 0.0011 | - |
| 0.8604 | 22250 | 0.0 | - |
| 0.8623 | 22300 | 0.0 | - |
| 0.8642 | 22350 | 0.0 | - |
| 0.8662 | 22400 | 0.0 | - |
| 0.8681 | 22450 | 0.0 | - |
| 0.8700 | 22500 | 0.0 | - |
| 0.8720 | 22550 | 0.0 | - |
| 0.8739 | 22600 | 0.0 | - |
| 0.8758 | 22650 | 0.0 | - |
| 0.8778 | 22700 | 0.0 | - |
| 0.8797 | 22750 | 0.0 | - |
| 0.8816 | 22800 | 0.0 | - |
| 0.8836 | 22850 | 0.0 | - |
| 0.8855 | 22900 | 0.0 | - |
| 0.8874 | 22950 | 0.0 | - |
| 0.8894 | 23000 | 0.0 | - |
| 0.8913 | 23050 | 0.0 | - |
| 0.8932 | 23100 | 0.0 | - |
| 0.8952 | 23150 | 0.0 | - |
| 0.8971 | 23200 | 0.0 | - |
| 0.8990 | 23250 | 0.0 | - |
| 0.9010 | 23300 | 0.0 | - |
| 0.9029 | 23350 | 0.0 | - |
| 0.9048 | 23400 | 0.0 | - |
| 0.9068 | 23450 | 0.0 | - |
| 0.9087 | 23500 | 0.0 | - |
| 0.9106 | 23550 | 0.0 | - |
| 0.9126 | 23600 | 0.0 | - |
| 0.9145 | 23650 | 0.0 | - |
| 0.9164 | 23700 | 0.0 | - |
| 0.9184 | 23750 | 0.0 | - |
| 0.9203 | 23800 | 0.0 | - |
| 0.9222 | 23850 | 0.0 | - |
| 0.9242 | 23900 | 0.0 | - |
| 0.9261 | 23950 | 0.0 | - |
| 0.9280 | 24000 | 0.0 | - |
| 0.9300 | 24050 | 0.0 | - |
| 0.9319 | 24100 | 0.0 | - |
| 0.9338 | 24150 | 0.0 | - |
| 0.9358 | 24200 | 0.0 | - |
| 0.9377 | 24250 | 0.0 | - |
| 0.9396 | 24300 | 0.0 | - |
| 0.9416 | 24350 | 0.0 | - |
| 0.9435 | 24400 | 0.0 | - |
| 0.9454 | 24450 | 0.0 | - |
| 0.9474 | 24500 | 0.0 | - |
| 0.9493 | 24550 | 0.0 | - |
| 0.9512 | 24600 | 0.0 | - |
| 0.9532 | 24650 | 0.0 | - |
| 0.9551 | 24700 | 0.0 | - |
| 0.9570 | 24750 | 0.0 | - |
| 0.9590 | 24800 | 0.0 | - |
| 0.9609 | 24850 | 0.0 | - |
| 0.9628 | 24900 | 0.0 | - |
| 0.9648 | 24950 | 0.0 | - |
| 0.9667 | 25000 | 0.0 | - |
| 0.9686 | 25050 | 0.0 | - |
| 0.9706 | 25100 | 0.0 | - |
| 0.9725 | 25150 | 0.0 | - |
| 0.9744 | 25200 | 0.0 | - |
| 0.9764 | 25250 | 0.0 | - |
| 0.9783 | 25300 | 0.0 | - |
| 0.9802 | 25350 | 0.0 | - |
| 0.9822 | 25400 | 0.0 | - |
| 0.9841 | 25450 | 0.0 | - |
| 0.9860 | 25500 | 0.0 | - |
| 0.9880 | 25550 | 0.0 | - |
| 0.9899 | 25600 | 0.0 | - |
| 0.9918 | 25650 | 0.0 | - |
| 0.9938 | 25700 | 0.0 | - |
| 0.9957 | 25750 | 0.0 | - |
| 0.9976 | 25800 | 0.0 | - |
| 0.9996 | 25850 | 0.0 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.1.0
- 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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