---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: ' "Ein Tempolimit auf deutschen Autobahnen wäre ein Schlag ins Gesicht aller
Autofahrer, die Freiheit und Unabhängigkeit schätzen."'
- text: Die Bundesregierung prüft derzeit mehrere Gesetzesinitiativen, die ein generelles
Tempolimit auf deutschen Autobahnen vorsehen.
- text: ' Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!'
- text: '"Während sich unsere Politiker auf ihren Klimakonferenzen über die Notwendigkeit
neuer Heizungssysteme unterhalten, vergessen sie dabei geflissentlich, dass die
einfache Frau Schmidt oder der einfache Herr Müller bald jeden zweiten Lohnscheck
direkt in die Kasse des Heizungsexperten oder des Energiekonzerns überweisen werden."'
- text: ' "Das geplante Heizungsgesetz ist ein weiterer Schritt in Richtung staatlicher
Bevormundung und wird die Bürger in die Armut treiben."'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/all-MiniLM-L6-v2
model-index:
- name: SetFit with sentence-transformers/all-MiniLM-L6-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.931899641577061
name: Accuracy
---
# SetFit with sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-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-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 256 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 |
|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| neutral | - ' Die Aktionen von Klima-Aktivismus-Gruppen wie Fridays for Future oder die Letzte Generation polarisieren die Öffentlichkeit, während sie gleichzeitig wichtige Diskussionen über den Klimawandel anstoßen.'
- 'Die Diskussion um ein generelles Tempolimit auf Autobahnen hat in den vergangenen Wochen an Fahrt gewonnen und sowohl Befürworter als auch Gegner haben ihre Positionen deutlich gemacht.'
- ' "Das geplante Heizungsgesetz sieht vor, dass ab 2024 in Neubauten und bei der Sanierung von Bestandsgebäuden verstärkt auf Wärmepumpen gesetzt werden soll."'
|
| supportive | - 'Die Einführung eines generellen Tempolimits auf deutschen Autobahnen würde nicht nur zu einer Senkung des Kraftstoffverbrauchs und der Treibhausgasemissionen führen, sondern auch die Verkehrssicherheit erhöhen.'
- ' "Ein nationales Tempolimit auf Autobahnen könnte laut Experten die Verkehrssicherheit erheblich verbessern und gleichzeitig den CO2-Ausstoß reduzieren."'
- ' "Das geplante Heizungsgesetz könnte einen wichtigen Beitrag zur Reduzierung von CO2-Emissionen leisten und somit einen bedeutenden Schritt in Richtung Klimaneutralität darstellen."'
|
| opposed | - 'Die Freiheit der Straße, ein Stück deutscher Identität, das in Gefahr geraten könnte, wenn die politischen Tempolimit-Fanatiker ihren Willen durchsetzen.'
- ' "Es reicht! Wann hören diese Klima-Aktivisten endlich auf, unsere Straßen zu blockieren und den Alltag der hart arbeitenden Bürger zu stören?"'
- '„Die Blockaden von Straßen und Autobahnen durch die Letzte Generation sorgen für tägliche Nervosität bei Pendler und Anwohner, die sich fragen, wann diese ständigen Behinderungen endlich ein Ende haben werden.“'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9319 |
## 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/MiniLM-klimacoder_v0.5")
# Run inference
preds = model(" Das Tempolimit auf Autobahnen würde die Freiheit der Autofahrer massiv einschränken!")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 11 | 25.5421 | 57 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 326 |
| opposed | 394 |
| supportive | 396 |
### 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 | 0.2393 | - |
| 0.0019 | 50 | 0.2748 | - |
| 0.0039 | 100 | 0.2607 | - |
| 0.0058 | 150 | 0.2486 | - |
| 0.0077 | 200 | 0.2465 | - |
| 0.0097 | 250 | 0.246 | - |
| 0.0116 | 300 | 0.2454 | - |
| 0.0135 | 350 | 0.2406 | - |
| 0.0155 | 400 | 0.235 | - |
| 0.0174 | 450 | 0.2269 | - |
| 0.0193 | 500 | 0.2184 | - |
| 0.0213 | 550 | 0.2095 | - |
| 0.0232 | 600 | 0.1833 | - |
| 0.0251 | 650 | 0.1777 | - |
| 0.0271 | 700 | 0.1548 | - |
| 0.0290 | 750 | 0.1464 | - |
| 0.0310 | 800 | 0.1326 | - |
| 0.0329 | 850 | 0.1304 | - |
| 0.0348 | 900 | 0.1237 | - |
| 0.0368 | 950 | 0.1163 | - |
| 0.0387 | 1000 | 0.1129 | - |
| 0.0406 | 1050 | 0.1017 | - |
| 0.0426 | 1100 | 0.0907 | - |
| 0.0445 | 1150 | 0.0857 | - |
| 0.0464 | 1200 | 0.0645 | - |
| 0.0484 | 1250 | 0.0641 | - |
| 0.0503 | 1300 | 0.0514 | - |
| 0.0522 | 1350 | 0.0442 | - |
| 0.0542 | 1400 | 0.0342 | - |
| 0.0561 | 1450 | 0.0291 | - |
| 0.0580 | 1500 | 0.0243 | - |
| 0.0600 | 1550 | 0.0185 | - |
| 0.0619 | 1600 | 0.0142 | - |
| 0.0638 | 1650 | 0.0092 | - |
| 0.0658 | 1700 | 0.0112 | - |
| 0.0677 | 1750 | 0.0076 | - |
| 0.0696 | 1800 | 0.0046 | - |
| 0.0716 | 1850 | 0.0038 | - |
| 0.0735 | 1900 | 0.0025 | - |
| 0.0754 | 1950 | 0.0028 | - |
| 0.0774 | 2000 | 0.0034 | - |
| 0.0793 | 2050 | 0.0022 | - |
| 0.0812 | 2100 | 0.0028 | - |
| 0.0832 | 2150 | 0.0025 | - |
| 0.0851 | 2200 | 0.0025 | - |
| 0.0870 | 2250 | 0.0011 | - |
| 0.0890 | 2300 | 0.0013 | - |
| 0.0909 | 2350 | 0.0019 | - |
| 0.0929 | 2400 | 0.0006 | - |
| 0.0948 | 2450 | 0.0013 | - |
| 0.0967 | 2500 | 0.0005 | - |
| 0.0987 | 2550 | 0.0006 | - |
| 0.1006 | 2600 | 0.0012 | - |
| 0.1025 | 2650 | 0.0016 | - |
| 0.1045 | 2700 | 0.0005 | - |
| 0.1064 | 2750 | 0.0004 | - |
| 0.1083 | 2800 | 0.0003 | - |
| 0.1103 | 2850 | 0.0008 | - |
| 0.1122 | 2900 | 0.001 | - |
| 0.1141 | 2950 | 0.0018 | - |
| 0.1161 | 3000 | 0.0005 | - |
| 0.1180 | 3050 | 0.0002 | - |
| 0.1199 | 3100 | 0.0005 | - |
| 0.1219 | 3150 | 0.0006 | - |
| 0.1238 | 3200 | 0.0017 | - |
| 0.1257 | 3250 | 0.0009 | - |
| 0.1277 | 3300 | 0.0026 | - |
| 0.1296 | 3350 | 0.0008 | - |
| 0.1315 | 3400 | 0.0009 | - |
| 0.1335 | 3450 | 0.0013 | - |
| 0.1354 | 3500 | 0.0009 | - |
| 0.1373 | 3550 | 0.0011 | - |
| 0.1393 | 3600 | 0.0008 | - |
| 0.1412 | 3650 | 0.0004 | - |
| 0.1431 | 3700 | 0.0009 | - |
| 0.1451 | 3750 | 0.0008 | - |
| 0.1470 | 3800 | 0.0012 | - |
| 0.1489 | 3850 | 0.001 | - |
| 0.1509 | 3900 | 0.0003 | - |
| 0.1528 | 3950 | 0.0005 | - |
| 0.1548 | 4000 | 0.0006 | - |
| 0.1567 | 4050 | 0.0007 | - |
| 0.1586 | 4100 | 0.0009 | - |
| 0.1606 | 4150 | 0.0003 | - |
| 0.1625 | 4200 | 0.0001 | - |
| 0.1644 | 4250 | 0.0011 | - |
| 0.1664 | 4300 | 0.0004 | - |
| 0.1683 | 4350 | 0.0005 | - |
| 0.1702 | 4400 | 0.001 | - |
| 0.1722 | 4450 | 0.0001 | - |
| 0.1741 | 4500 | 0.0001 | - |
| 0.1760 | 4550 | 0.0001 | - |
| 0.1780 | 4600 | 0.0007 | - |
| 0.1799 | 4650 | 0.0001 | - |
| 0.1818 | 4700 | 0.0 | - |
| 0.1838 | 4750 | 0.0 | - |
| 0.1857 | 4800 | 0.0001 | - |
| 0.1876 | 4850 | 0.0001 | - |
| 0.1896 | 4900 | 0.0 | - |
| 0.1915 | 4950 | 0.0002 | - |
| 0.1934 | 5000 | 0.0008 | - |
| 0.1954 | 5050 | 0.0006 | - |
| 0.1973 | 5100 | 0.0001 | - |
| 0.1992 | 5150 | 0.0 | - |
| 0.2012 | 5200 | 0.0 | - |
| 0.2031 | 5250 | 0.0006 | - |
| 0.2050 | 5300 | 0.0009 | - |
| 0.2070 | 5350 | 0.0001 | - |
| 0.2089 | 5400 | 0.0004 | - |
| 0.2108 | 5450 | 0.0032 | - |
| 0.2128 | 5500 | 0.0029 | - |
| 0.2147 | 5550 | 0.001 | - |
| 0.2167 | 5600 | 0.0014 | - |
| 0.2186 | 5650 | 0.0004 | - |
| 0.2205 | 5700 | 0.0034 | - |
| 0.2225 | 5750 | 0.0003 | - |
| 0.2244 | 5800 | 0.0002 | - |
| 0.2263 | 5850 | 0.0001 | - |
| 0.2283 | 5900 | 0.0 | - |
| 0.2302 | 5950 | 0.0 | - |
| 0.2321 | 6000 | 0.0 | - |
| 0.2341 | 6050 | 0.0 | - |
| 0.2360 | 6100 | 0.0 | - |
| 0.2379 | 6150 | 0.0 | - |
| 0.2399 | 6200 | 0.0 | - |
| 0.2418 | 6250 | 0.0 | - |
| 0.2437 | 6300 | 0.0001 | - |
| 0.2457 | 6350 | 0.0024 | - |
| 0.2476 | 6400 | 0.0009 | - |
| 0.2495 | 6450 | 0.0005 | - |
| 0.2515 | 6500 | 0.0016 | - |
| 0.2534 | 6550 | 0.0003 | - |
| 0.2553 | 6600 | 0.0001 | - |
| 0.2573 | 6650 | 0.0 | - |
| 0.2592 | 6700 | 0.0 | - |
| 0.2611 | 6750 | 0.0 | - |
| 0.2631 | 6800 | 0.0 | - |
| 0.2650 | 6850 | 0.0 | - |
| 0.2669 | 6900 | 0.0 | - |
| 0.2689 | 6950 | 0.0 | - |
| 0.2708 | 7000 | 0.0 | - |
| 0.2727 | 7050 | 0.0 | - |
| 0.2747 | 7100 | 0.0 | - |
| 0.2766 | 7150 | 0.0 | - |
| 0.2786 | 7200 | 0.0 | - |
| 0.2805 | 7250 | 0.0002 | - |
| 0.2824 | 7300 | 0.0006 | - |
| 0.2844 | 7350 | 0.0008 | - |
| 0.2863 | 7400 | 0.0013 | - |
| 0.2882 | 7450 | 0.0001 | - |
| 0.2902 | 7500 | 0.0005 | - |
| 0.2921 | 7550 | 0.0 | - |
| 0.2940 | 7600 | 0.0 | - |
| 0.2960 | 7650 | 0.0 | - |
| 0.2979 | 7700 | 0.0006 | - |
| 0.2998 | 7750 | 0.0 | - |
| 0.3018 | 7800 | 0.0 | - |
| 0.3037 | 7850 | 0.0 | - |
| 0.3056 | 7900 | 0.0 | - |
| 0.3076 | 7950 | 0.0 | - |
| 0.3095 | 8000 | 0.0 | - |
| 0.3114 | 8050 | 0.0 | - |
| 0.3134 | 8100 | 0.0 | - |
| 0.3153 | 8150 | 0.0 | - |
| 0.3172 | 8200 | 0.0 | - |
| 0.3192 | 8250 | 0.0 | - |
| 0.3211 | 8300 | 0.0 | - |
| 0.3230 | 8350 | 0.0 | - |
| 0.3250 | 8400 | 0.0 | - |
| 0.3269 | 8450 | 0.0 | - |
| 0.3288 | 8500 | 0.0 | - |
| 0.3308 | 8550 | 0.0 | - |
| 0.3327 | 8600 | 0.0 | - |
| 0.3346 | 8650 | 0.0004 | - |
| 0.3366 | 8700 | 0.0 | - |
| 0.3385 | 8750 | 0.0 | - |
| 0.3405 | 8800 | 0.0 | - |
| 0.3424 | 8850 | 0.0 | - |
| 0.3443 | 8900 | 0.0 | - |
| 0.3463 | 8950 | 0.0 | - |
| 0.3482 | 9000 | 0.0 | - |
| 0.3501 | 9050 | 0.0 | - |
| 0.3521 | 9100 | 0.0001 | - |
| 0.3540 | 9150 | 0.0037 | - |
| 0.3559 | 9200 | 0.0013 | - |
| 0.3579 | 9250 | 0.0007 | - |
| 0.3598 | 9300 | 0.0032 | - |
| 0.3617 | 9350 | 0.0006 | - |
| 0.3637 | 9400 | 0.0007 | - |
| 0.3656 | 9450 | 0.0 | - |
| 0.3675 | 9500 | 0.0006 | - |
| 0.3695 | 9550 | 0.0001 | - |
| 0.3714 | 9600 | 0.0004 | - |
| 0.3733 | 9650 | 0.0001 | - |
| 0.3753 | 9700 | 0.0001 | - |
| 0.3772 | 9750 | 0.0 | - |
| 0.3791 | 9800 | 0.0 | - |
| 0.3811 | 9850 | 0.0 | - |
| 0.3830 | 9900 | 0.0 | - |
| 0.3849 | 9950 | 0.0 | - |
| 0.3869 | 10000 | 0.0 | - |
| 0.3888 | 10050 | 0.0 | - |
| 0.3907 | 10100 | 0.0 | - |
| 0.3927 | 10150 | 0.0 | - |
| 0.3946 | 10200 | 0.0 | - |
| 0.3965 | 10250 | 0.0 | - |
| 0.3985 | 10300 | 0.0 | - |
| 0.4004 | 10350 | 0.0 | - |
| 0.4024 | 10400 | 0.0 | - |
| 0.4043 | 10450 | 0.0 | - |
| 0.4062 | 10500 | 0.0 | - |
| 0.4082 | 10550 | 0.0 | - |
| 0.4101 | 10600 | 0.0 | - |
| 0.4120 | 10650 | 0.0 | - |
| 0.4140 | 10700 | 0.0 | - |
| 0.4159 | 10750 | 0.0 | - |
| 0.4178 | 10800 | 0.0 | - |
| 0.4198 | 10850 | 0.0 | - |
| 0.4217 | 10900 | 0.0001 | - |
| 0.4236 | 10950 | 0.0 | - |
| 0.4256 | 11000 | 0.0 | - |
| 0.4275 | 11050 | 0.0007 | - |
| 0.4294 | 11100 | 0.0043 | - |
| 0.4314 | 11150 | 0.0011 | - |
| 0.4333 | 11200 | 0.0013 | - |
| 0.4352 | 11250 | 0.0005 | - |
| 0.4372 | 11300 | 0.0004 | - |
| 0.4391 | 11350 | 0.0001 | - |
| 0.4410 | 11400 | 0.0001 | - |
| 0.4430 | 11450 | 0.0 | - |
| 0.4449 | 11500 | 0.0001 | - |
| 0.4468 | 11550 | 0.0 | - |
| 0.4488 | 11600 | 0.0001 | - |
| 0.4507 | 11650 | 0.0004 | - |
| 0.4526 | 11700 | 0.0001 | - |
| 0.4546 | 11750 | 0.0 | - |
| 0.4565 | 11800 | 0.0013 | - |
| 0.4584 | 11850 | 0.0006 | - |
| 0.4604 | 11900 | 0.0001 | - |
| 0.4623 | 11950 | 0.0 | - |
| 0.4643 | 12000 | 0.0 | - |
| 0.4662 | 12050 | 0.0 | - |
| 0.4681 | 12100 | 0.0 | - |
| 0.4701 | 12150 | 0.0 | - |
| 0.4720 | 12200 | 0.0002 | - |
| 0.4739 | 12250 | 0.0 | - |
| 0.4759 | 12300 | 0.0 | - |
| 0.4778 | 12350 | 0.0 | - |
| 0.4797 | 12400 | 0.0 | - |
| 0.4817 | 12450 | 0.0 | - |
| 0.4836 | 12500 | 0.0 | - |
| 0.4855 | 12550 | 0.0 | - |
| 0.4875 | 12600 | 0.0 | - |
| 0.4894 | 12650 | 0.0 | - |
| 0.4913 | 12700 | 0.0 | - |
| 0.4933 | 12750 | 0.0 | - |
| 0.4952 | 12800 | 0.0 | - |
| 0.4971 | 12850 | 0.0 | - |
| 0.4991 | 12900 | 0.0 | - |
| 0.5010 | 12950 | 0.0 | - |
| 0.5029 | 13000 | 0.0 | - |
| 0.5049 | 13050 | 0.0 | - |
| 0.5068 | 13100 | 0.0 | - |
| 0.5087 | 13150 | 0.0 | - |
| 0.5107 | 13200 | 0.0 | - |
| 0.5126 | 13250 | 0.0 | - |
| 0.5145 | 13300 | 0.0 | - |
| 0.5165 | 13350 | 0.0 | - |
| 0.5184 | 13400 | 0.0 | - |
| 0.5203 | 13450 | 0.0 | - |
| 0.5223 | 13500 | 0.0 | - |
| 0.5242 | 13550 | 0.0 | - |
| 0.5262 | 13600 | 0.0 | - |
| 0.5281 | 13650 | 0.0 | - |
| 0.5300 | 13700 | 0.0 | - |
| 0.5320 | 13750 | 0.0 | - |
| 0.5339 | 13800 | 0.0 | - |
| 0.5358 | 13850 | 0.0 | - |
| 0.5378 | 13900 | 0.0 | - |
| 0.5397 | 13950 | 0.0 | - |
| 0.5416 | 14000 | 0.0 | - |
| 0.5436 | 14050 | 0.0 | - |
| 0.5455 | 14100 | 0.0 | - |
| 0.5474 | 14150 | 0.0 | - |
| 0.5494 | 14200 | 0.0 | - |
| 0.5513 | 14250 | 0.0 | - |
| 0.5532 | 14300 | 0.0 | - |
| 0.5552 | 14350 | 0.0 | - |
| 0.5571 | 14400 | 0.0 | - |
| 0.5590 | 14450 | 0.0 | - |
| 0.5610 | 14500 | 0.0 | - |
| 0.5629 | 14550 | 0.0 | - |
| 0.5648 | 14600 | 0.0 | - |
| 0.5668 | 14650 | 0.0 | - |
| 0.5687 | 14700 | 0.0 | - |
| 0.5706 | 14750 | 0.0 | - |
| 0.5726 | 14800 | 0.0 | - |
| 0.5745 | 14850 | 0.0 | - |
| 0.5764 | 14900 | 0.0 | - |
| 0.5784 | 14950 | 0.0 | - |
| 0.5803 | 15000 | 0.0 | - |
| 0.5823 | 15050 | 0.0 | - |
| 0.5842 | 15100 | 0.0 | - |
| 0.5861 | 15150 | 0.0009 | - |
| 0.5881 | 15200 | 0.0006 | - |
| 0.5900 | 15250 | 0.0 | - |
| 0.5919 | 15300 | 0.0 | - |
| 0.5939 | 15350 | 0.0 | - |
| 0.5958 | 15400 | 0.0 | - |
| 0.5977 | 15450 | 0.0 | - |
| 0.5997 | 15500 | 0.0 | - |
| 0.6016 | 15550 | 0.0 | - |
| 0.6035 | 15600 | 0.0 | - |
| 0.6055 | 15650 | 0.0 | - |
| 0.6074 | 15700 | 0.0 | - |
| 0.6093 | 15750 | 0.0006 | - |
| 0.6113 | 15800 | 0.0007 | - |
| 0.6132 | 15850 | 0.0 | - |
| 0.6151 | 15900 | 0.0 | - |
| 0.6171 | 15950 | 0.0 | - |
| 0.6190 | 16000 | 0.0 | - |
| 0.6209 | 16050 | 0.0 | - |
| 0.6229 | 16100 | 0.0 | - |
| 0.6248 | 16150 | 0.0 | - |
| 0.6267 | 16200 | 0.0 | - |
| 0.6287 | 16250 | 0.0 | - |
| 0.6306 | 16300 | 0.0 | - |
| 0.6325 | 16350 | 0.0 | - |
| 0.6345 | 16400 | 0.0 | - |
| 0.6364 | 16450 | 0.0 | - |
| 0.6383 | 16500 | 0.0 | - |
| 0.6403 | 16550 | 0.0 | - |
| 0.6422 | 16600 | 0.0 | - |
| 0.6442 | 16650 | 0.0 | - |
| 0.6461 | 16700 | 0.0 | - |
| 0.6480 | 16750 | 0.0 | - |
| 0.6500 | 16800 | 0.0 | - |
| 0.6519 | 16850 | 0.0 | - |
| 0.6538 | 16900 | 0.0 | - |
| 0.6558 | 16950 | 0.0 | - |
| 0.6577 | 17000 | 0.0 | - |
| 0.6596 | 17050 | 0.0 | - |
| 0.6616 | 17100 | 0.0 | - |
| 0.6635 | 17150 | 0.0 | - |
| 0.6654 | 17200 | 0.0 | - |
| 0.6674 | 17250 | 0.0 | - |
| 0.6693 | 17300 | 0.0 | - |
| 0.6712 | 17350 | 0.0 | - |
| 0.6732 | 17400 | 0.0 | - |
| 0.6751 | 17450 | 0.0 | - |
| 0.6770 | 17500 | 0.0 | - |
| 0.6790 | 17550 | 0.0 | - |
| 0.6809 | 17600 | 0.0 | - |
| 0.6828 | 17650 | 0.0 | - |
| 0.6848 | 17700 | 0.0 | - |
| 0.6867 | 17750 | 0.0 | - |
| 0.6886 | 17800 | 0.0 | - |
| 0.6906 | 17850 | 0.0 | - |
| 0.6925 | 17900 | 0.0 | - |
| 0.6944 | 17950 | 0.0 | - |
| 0.6964 | 18000 | 0.0 | - |
| 0.6983 | 18050 | 0.0007 | - |
| 0.7002 | 18100 | 0.0 | - |
| 0.7022 | 18150 | 0.0 | - |
| 0.7041 | 18200 | 0.0 | - |
| 0.7061 | 18250 | 0.0 | - |
| 0.7080 | 18300 | 0.0 | - |
| 0.7099 | 18350 | 0.0 | - |
| 0.7119 | 18400 | 0.0 | - |
| 0.7138 | 18450 | 0.0 | - |
| 0.7157 | 18500 | 0.0001 | - |
| 0.7177 | 18550 | 0.0 | - |
| 0.7196 | 18600 | 0.0 | - |
| 0.7215 | 18650 | 0.0004 | - |
| 0.7235 | 18700 | 0.0 | - |
| 0.7254 | 18750 | 0.0 | - |
| 0.7273 | 18800 | 0.0 | - |
| 0.7293 | 18850 | 0.0 | - |
| 0.7312 | 18900 | 0.0 | - |
| 0.7331 | 18950 | 0.0 | - |
| 0.7351 | 19000 | 0.0 | - |
| 0.7370 | 19050 | 0.0 | - |
| 0.7389 | 19100 | 0.0 | - |
| 0.7409 | 19150 | 0.0 | - |
| 0.7428 | 19200 | 0.0 | - |
| 0.7447 | 19250 | 0.0 | - |
| 0.7467 | 19300 | 0.0 | - |
| 0.7486 | 19350 | 0.0 | - |
| 0.7505 | 19400 | 0.0 | - |
| 0.7525 | 19450 | 0.0 | - |
| 0.7544 | 19500 | 0.0 | - |
| 0.7563 | 19550 | 0.0 | - |
| 0.7583 | 19600 | 0.0 | - |
| 0.7602 | 19650 | 0.0 | - |
| 0.7621 | 19700 | 0.0 | - |
| 0.7641 | 19750 | 0.0 | - |
| 0.7660 | 19800 | 0.0 | - |
| 0.7680 | 19850 | 0.0 | - |
| 0.7699 | 19900 | 0.0 | - |
| 0.7718 | 19950 | 0.0 | - |
| 0.7738 | 20000 | 0.0 | - |
| 0.7757 | 20050 | 0.0 | - |
| 0.7776 | 20100 | 0.0 | - |
| 0.7796 | 20150 | 0.0 | - |
| 0.7815 | 20200 | 0.0 | - |
| 0.7834 | 20250 | 0.0 | - |
| 0.7854 | 20300 | 0.0 | - |
| 0.7873 | 20350 | 0.0 | - |
| 0.7892 | 20400 | 0.0 | - |
| 0.7912 | 20450 | 0.0 | - |
| 0.7931 | 20500 | 0.0 | - |
| 0.7950 | 20550 | 0.0 | - |
| 0.7970 | 20600 | 0.0 | - |
| 0.7989 | 20650 | 0.0 | - |
| 0.8008 | 20700 | 0.0 | - |
| 0.8028 | 20750 | 0.0 | - |
| 0.8047 | 20800 | 0.0 | - |
| 0.8066 | 20850 | 0.0 | - |
| 0.8086 | 20900 | 0.0 | - |
| 0.8105 | 20950 | 0.0 | - |
| 0.8124 | 21000 | 0.0 | - |
| 0.8144 | 21050 | 0.0 | - |
| 0.8163 | 21100 | 0.0 | - |
| 0.8182 | 21150 | 0.0 | - |
| 0.8202 | 21200 | 0.0 | - |
| 0.8221 | 21250 | 0.0 | - |
| 0.8240 | 21300 | 0.0 | - |
| 0.8260 | 21350 | 0.0 | - |
| 0.8279 | 21400 | 0.0 | - |
| 0.8299 | 21450 | 0.0 | - |
| 0.8318 | 21500 | 0.0 | - |
| 0.8337 | 21550 | 0.0 | - |
| 0.8357 | 21600 | 0.0 | - |
| 0.8376 | 21650 | 0.0 | - |
| 0.8395 | 21700 | 0.0 | - |
| 0.8415 | 21750 | 0.0 | - |
| 0.8434 | 21800 | 0.0 | - |
| 0.8453 | 21850 | 0.0 | - |
| 0.8473 | 21900 | 0.0 | - |
| 0.8492 | 21950 | 0.0 | - |
| 0.8511 | 22000 | 0.0 | - |
| 0.8531 | 22050 | 0.0 | - |
| 0.8550 | 22100 | 0.0 | - |
| 0.8569 | 22150 | 0.0 | - |
| 0.8589 | 22200 | 0.0 | - |
| 0.8608 | 22250 | 0.0 | - |
| 0.8627 | 22300 | 0.0 | - |
| 0.8647 | 22350 | 0.0 | - |
| 0.8666 | 22400 | 0.0 | - |
| 0.8685 | 22450 | 0.0 | - |
| 0.8705 | 22500 | 0.0 | - |
| 0.8724 | 22550 | 0.0 | - |
| 0.8743 | 22600 | 0.0 | - |
| 0.8763 | 22650 | 0.0 | - |
| 0.8782 | 22700 | 0.0 | - |
| 0.8801 | 22750 | 0.0 | - |
| 0.8821 | 22800 | 0.0 | - |
| 0.8840 | 22850 | 0.0 | - |
| 0.8859 | 22900 | 0.0 | - |
| 0.8879 | 22950 | 0.0 | - |
| 0.8898 | 23000 | 0.0 | - |
| 0.8918 | 23050 | 0.0 | - |
| 0.8937 | 23100 | 0.0 | - |
| 0.8956 | 23150 | 0.0 | - |
| 0.8976 | 23200 | 0.0 | - |
| 0.8995 | 23250 | 0.0 | - |
| 0.9014 | 23300 | 0.0 | - |
| 0.9034 | 23350 | 0.0 | - |
| 0.9053 | 23400 | 0.0 | - |
| 0.9072 | 23450 | 0.0 | - |
| 0.9092 | 23500 | 0.0 | - |
| 0.9111 | 23550 | 0.0 | - |
| 0.9130 | 23600 | 0.0 | - |
| 0.9150 | 23650 | 0.0 | - |
| 0.9169 | 23700 | 0.0 | - |
| 0.9188 | 23750 | 0.0 | - |
| 0.9208 | 23800 | 0.0 | - |
| 0.9227 | 23850 | 0.0 | - |
| 0.9246 | 23900 | 0.0 | - |
| 0.9266 | 23950 | 0.0 | - |
| 0.9285 | 24000 | 0.0 | - |
| 0.9304 | 24050 | 0.0 | - |
| 0.9324 | 24100 | 0.0 | - |
| 0.9343 | 24150 | 0.0 | - |
| 0.9362 | 24200 | 0.0 | - |
| 0.9382 | 24250 | 0.0 | - |
| 0.9401 | 24300 | 0.0 | - |
| 0.9420 | 24350 | 0.0 | - |
| 0.9440 | 24400 | 0.0 | - |
| 0.9459 | 24450 | 0.0 | - |
| 0.9478 | 24500 | 0.0 | - |
| 0.9498 | 24550 | 0.0 | - |
| 0.9517 | 24600 | 0.0 | - |
| 0.9537 | 24650 | 0.0 | - |
| 0.9556 | 24700 | 0.0 | - |
| 0.9575 | 24750 | 0.0 | - |
| 0.9595 | 24800 | 0.0 | - |
| 0.9614 | 24850 | 0.0 | - |
| 0.9633 | 24900 | 0.0 | - |
| 0.9653 | 24950 | 0.0 | - |
| 0.9672 | 25000 | 0.0 | - |
| 0.9691 | 25050 | 0.0 | - |
| 0.9711 | 25100 | 0.0 | - |
| 0.9730 | 25150 | 0.0 | - |
| 0.9749 | 25200 | 0.0 | - |
| 0.9769 | 25250 | 0.0 | - |
| 0.9788 | 25300 | 0.0 | - |
| 0.9807 | 25350 | 0.0 | - |
| 0.9827 | 25400 | 0.0 | - |
| 0.9846 | 25450 | 0.0 | - |
| 0.9865 | 25500 | 0.0 | - |
| 0.9885 | 25550 | 0.0 | - |
| 0.9904 | 25600 | 0.0 | - |
| 0.9923 | 25650 | 0.0 | - |
| 0.9943 | 25700 | 0.0 | - |
| 0.9962 | 25750 | 0.0 | - |
| 0.9981 | 25800 | 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}
}
```