---
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: '"Die jungen Klimaaktivisten haben mit ihren Protestaktionen und Straßenblockaden
ein dringend benötigtes Gespräch über die Notwendigkeit von sofortigem Handeln
im Kampf gegen den Klimawandel angestoßen."'
- text: Die Bundesregierung plant, den Einsatz von Wärmepumpen durch ein neues Heizungsgesetz
zu fördern, was laut Experten einen wichtigen Schritt zur Erreichung der Klimaziele
darstellen könnte.
- text: ' "Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung
eines grünen Diktats, das die Bürger in die Kälte schickt."'
- text: ' Die Klima-Aktivisten von Fridays for Future und der Letzten Generation haben
heute in mehreren Städten Proteste organisiert, um auf den Klimawandel aufmerksam
zu machen.'
- text: ' "Die Diskussion über ein Tempolimit auf Autobahnen spaltet die Gemüter,
während Experten auf die potenziellen Vorteile für die Verkehrssicherheit und
den Klimaschutz hinweisen."'
metrics:
- accuracy
pipeline_tag: text-classification
library_name: setfit
inference: true
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: Unknown
type: unknown
split: test
metrics:
- type: accuracy
value: 0.953405017921147
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-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/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 128 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 Bundesregierung plant, bis 2024 ein sogenanntes Heizungsgesetz vorzulegen, das unter anderem eine flächendeckende Nutzung von Wärmepumpen als Teil eines umfassenden Plans zur Reduzierung der Treibhausgasemissionen im Gebäudesektor vorsehen soll.'
- '"Die Bundesregierung plant, die Einführung von Wärmepumpen für Neubauten und den Austausch alter Heizungsanlagen in Bestandsgebäuden durch ein Gesetz zu forcieren, während Kritiker warnen, dass die Maßnahmen die Belastung für private Haushalte und Unternehmen erhöhen könnten."'
- ' Die Diskussion über ein nationales Tempolimit auf Autobahnen spaltet die Gemüter, während Experten die potenziellen Vorteile und Nachteile abwägen.'
|
| opposed | - '"Millionen von Hausbesitzern sollen zu unfreiwilligen Versuchskaninchen für die teuren und unzuverlässlichen Wärmepumpen werden, ohne dass es auch nur einen Hauch von echter Wahlmöglichkeit gibt."'
- '"Die von den Grünen und Linken geträumte Tempodiktatur auf unseren Autobahnen ist nichts als ein weiterer Schritt in Richtung einer überbürokratisierten, unfreien Gesellschaft."'
- '"Die geplanten Vorschriften würden vielen Familien den Traum vom Eigenheim in weite Ferne rücken, da die Kosten für die Installation einer Wärmepumpe oft ein Vielfaches dessen betragen, was ein durchschnittlicher Haushalt in einem Jahr für Heizkosten ausgibt."'
|
| supportive | - 'Die Bundesregierung hat mit dem Heizungsgesetz einen wichtigen Schritt in Richtung Klimaneutralität gemacht, indem sie die Verpflichtung zur Nutzung erneuerbarer Wärmequellen bei Neubauten festlegt.'
- '"Ein Tempolimit auf Autobahnen könnte nicht nur die Umweltbelastung verringern, sondern auch die Zahl der Verkehrsunfälle reduzieren und somit Menschenleben retten."'
- ' Eine nationale Geschwindigkeitsbegrenzung auf Autobahnen könnte nicht nur die Unfallzahlen senken, sondern auch einen wichtigen Beitrag zum Klimaschutz leisten.'
|
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9534 |
## 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/paraphrase-multilingual-mpnet-klimacoder_v0.8")
# Run inference
preds = model(" \"Das Heizungsgesetz ist nichts weiter als ein weiterer Schritt in Richtung eines grünen Diktats, das die Bürger in die Kälte schickt.\"")
```
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 10 | 25.6541 | 57 |
| Label | Training Sample Count |
|:-----------|:----------------------|
| neutral | 321 |
| opposed | 391 |
| supportive | 404 |
### 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 | 0.1985 | - |
| 0.0019 | 50 | 0.2445 | - |
| 0.0039 | 100 | 0.2321 | - |
| 0.0058 | 150 | 0.2012 | - |
| 0.0077 | 200 | 0.1614 | - |
| 0.0097 | 250 | 0.1188 | - |
| 0.0116 | 300 | 0.0849 | - |
| 0.0136 | 350 | 0.0563 | - |
| 0.0155 | 400 | 0.0374 | - |
| 0.0174 | 450 | 0.0216 | - |
| 0.0194 | 500 | 0.0144 | - |
| 0.0213 | 550 | 0.0099 | - |
| 0.0232 | 600 | 0.0061 | - |
| 0.0252 | 650 | 0.007 | - |
| 0.0271 | 700 | 0.0026 | - |
| 0.0290 | 750 | 0.0017 | - |
| 0.0310 | 800 | 0.0012 | - |
| 0.0329 | 850 | 0.0014 | - |
| 0.0349 | 900 | 0.002 | - |
| 0.0368 | 950 | 0.0008 | - |
| 0.0387 | 1000 | 0.0009 | - |
| 0.0407 | 1050 | 0.0003 | - |
| 0.0426 | 1100 | 0.0007 | - |
| 0.0445 | 1150 | 0.0008 | - |
| 0.0465 | 1200 | 0.0006 | - |
| 0.0484 | 1250 | 0.0002 | - |
| 0.0503 | 1300 | 0.0001 | - |
| 0.0523 | 1350 | 0.0001 | - |
| 0.0542 | 1400 | 0.0001 | - |
| 0.0562 | 1450 | 0.0001 | - |
| 0.0581 | 1500 | 0.0007 | - |
| 0.0600 | 1550 | 0.0005 | - |
| 0.0620 | 1600 | 0.0007 | - |
| 0.0639 | 1650 | 0.0012 | - |
| 0.0658 | 1700 | 0.0007 | - |
| 0.0678 | 1750 | 0.0038 | - |
| 0.0697 | 1800 | 0.0018 | - |
| 0.0716 | 1850 | 0.0049 | - |
| 0.0736 | 1900 | 0.0061 | - |
| 0.0755 | 1950 | 0.0038 | - |
| 0.0775 | 2000 | 0.0037 | - |
| 0.0794 | 2050 | 0.0006 | - |
| 0.0813 | 2100 | 0.0001 | - |
| 0.0833 | 2150 | 0.0 | - |
| 0.0852 | 2200 | 0.0 | - |
| 0.0871 | 2250 | 0.0 | - |
| 0.0891 | 2300 | 0.0 | - |
| 0.0910 | 2350 | 0.0 | - |
| 0.0929 | 2400 | 0.0 | - |
| 0.0949 | 2450 | 0.0 | - |
| 0.0968 | 2500 | 0.0 | - |
| 0.0987 | 2550 | 0.0 | - |
| 0.1007 | 2600 | 0.0 | - |
| 0.1026 | 2650 | 0.0 | - |
| 0.1046 | 2700 | 0.0 | - |
| 0.1065 | 2750 | 0.0 | - |
| 0.1084 | 2800 | 0.0 | - |
| 0.1104 | 2850 | 0.0 | - |
| 0.1123 | 2900 | 0.0 | - |
| 0.1142 | 2950 | 0.0 | - |
| 0.1162 | 3000 | 0.0 | - |
| 0.1181 | 3050 | 0.0 | - |
| 0.1200 | 3100 | 0.0 | - |
| 0.1220 | 3150 | 0.0 | - |
| 0.1239 | 3200 | 0.0 | - |
| 0.1259 | 3250 | 0.0 | - |
| 0.1278 | 3300 | 0.0 | - |
| 0.1297 | 3350 | 0.0 | - |
| 0.1317 | 3400 | 0.0 | - |
| 0.1336 | 3450 | 0.0 | - |
| 0.1355 | 3500 | 0.0 | - |
| 0.1375 | 3550 | 0.0 | - |
| 0.1394 | 3600 | 0.0 | - |
| 0.1413 | 3650 | 0.0 | - |
| 0.1433 | 3700 | 0.0 | - |
| 0.1452 | 3750 | 0.0 | - |
| 0.1472 | 3800 | 0.0 | - |
| 0.1491 | 3850 | 0.0 | - |
| 0.1510 | 3900 | 0.0 | - |
| 0.1530 | 3950 | 0.0 | - |
| 0.1549 | 4000 | 0.0 | - |
| 0.1568 | 4050 | 0.0 | - |
| 0.1588 | 4100 | 0.0 | - |
| 0.1607 | 4150 | 0.0 | - |
| 0.1626 | 4200 | 0.0 | - |
| 0.1646 | 4250 | 0.0 | - |
| 0.1665 | 4300 | 0.0 | - |
| 0.1685 | 4350 | 0.0 | - |
| 0.1704 | 4400 | 0.0 | - |
| 0.1723 | 4450 | 0.0 | - |
| 0.1743 | 4500 | 0.0 | - |
| 0.1762 | 4550 | 0.0 | - |
| 0.1781 | 4600 | 0.0 | - |
| 0.1801 | 4650 | 0.0 | - |
| 0.1820 | 4700 | 0.0 | - |
| 0.1839 | 4750 | 0.0 | - |
| 0.1859 | 4800 | 0.0 | - |
| 0.1878 | 4850 | 0.0 | - |
| 0.1898 | 4900 | 0.0 | - |
| 0.1917 | 4950 | 0.0 | - |
| 0.1936 | 5000 | 0.0 | - |
| 0.1956 | 5050 | 0.0 | - |
| 0.1975 | 5100 | 0.0 | - |
| 0.1994 | 5150 | 0.0 | - |
| 0.2014 | 5200 | 0.0 | - |
| 0.2033 | 5250 | 0.0 | - |
| 0.2052 | 5300 | 0.0 | - |
| 0.2072 | 5350 | 0.0 | - |
| 0.2091 | 5400 | 0.0 | - |
| 0.2111 | 5450 | 0.0 | - |
| 0.2130 | 5500 | 0.0 | - |
| 0.2149 | 5550 | 0.0 | - |
| 0.2169 | 5600 | 0.0 | - |
| 0.2188 | 5650 | 0.0 | - |
| 0.2207 | 5700 | 0.0 | - |
| 0.2227 | 5750 | 0.0 | - |
| 0.2246 | 5800 | 0.0 | - |
| 0.2265 | 5850 | 0.0 | - |
| 0.2285 | 5900 | 0.0 | - |
| 0.2304 | 5950 | 0.0 | - |
| 0.2324 | 6000 | 0.0 | - |
| 0.2343 | 6050 | 0.0 | - |
| 0.2362 | 6100 | 0.0 | - |
| 0.2382 | 6150 | 0.0 | - |
| 0.2401 | 6200 | 0.0 | - |
| 0.2420 | 6250 | 0.0 | - |
| 0.2440 | 6300 | 0.0 | - |
| 0.2459 | 6350 | 0.0 | - |
| 0.2478 | 6400 | 0.0 | - |
| 0.2498 | 6450 | 0.0 | - |
| 0.2517 | 6500 | 0.0 | - |
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| 0.2653 | 6850 | 0.0 | - |
| 0.2672 | 6900 | 0.0 | - |
| 0.2691 | 6950 | 0.0 | - |
| 0.2711 | 7000 | 0.0 | - |
| 0.2730 | 7050 | 0.0 | - |
| 0.2749 | 7100 | 0.0 | - |
| 0.2769 | 7150 | 0.0 | - |
| 0.2788 | 7200 | 0.0 | - |
| 0.2808 | 7250 | 0.0 | - |
| 0.2827 | 7300 | 0.0 | - |
| 0.2846 | 7350 | 0.0 | - |
| 0.2866 | 7400 | 0.0 | - |
| 0.2885 | 7450 | 0.0 | - |
| 0.2904 | 7500 | 0.0 | - |
| 0.2924 | 7550 | 0.0 | - |
| 0.2943 | 7600 | 0.0 | - |
| 0.2962 | 7650 | 0.0 | - |
| 0.2982 | 7700 | 0.0 | - |
| 0.3001 | 7750 | 0.0 | - |
| 0.3021 | 7800 | 0.0 | - |
| 0.3040 | 7850 | 0.0 | - |
| 0.3059 | 7900 | 0.0 | - |
| 0.3079 | 7950 | 0.0 | - |
| 0.3098 | 8000 | 0.0 | - |
| 0.3117 | 8050 | 0.0 | - |
| 0.3137 | 8100 | 0.0 | - |
| 0.3156 | 8150 | 0.0 | - |
| 0.3175 | 8200 | 0.0 | - |
| 0.3195 | 8250 | 0.0 | - |
| 0.3214 | 8300 | 0.0 | - |
| 0.3234 | 8350 | 0.0 | - |
| 0.3253 | 8400 | 0.0 | - |
| 0.3272 | 8450 | 0.0 | - |
| 0.3292 | 8500 | 0.0 | - |
| 0.3311 | 8550 | 0.0 | - |
| 0.3330 | 8600 | 0.0 | - |
| 0.3350 | 8650 | 0.0 | - |
| 0.3369 | 8700 | 0.0 | - |
| 0.3388 | 8750 | 0.0 | - |
| 0.3408 | 8800 | 0.0 | - |
| 0.3427 | 8850 | 0.0 | - |
| 0.3447 | 8900 | 0.0 | - |
| 0.3466 | 8950 | 0.0 | - |
| 0.3485 | 9000 | 0.0 | - |
| 0.3505 | 9050 | 0.0 | - |
| 0.3524 | 9100 | 0.0 | - |
| 0.3543 | 9150 | 0.0 | - |
| 0.3563 | 9200 | 0.0 | - |
| 0.3582 | 9250 | 0.0 | - |
| 0.3601 | 9300 | 0.0 | - |
| 0.3621 | 9350 | 0.0 | - |
| 0.3640 | 9400 | 0.0 | - |
| 0.3660 | 9450 | 0.0 | - |
| 0.3679 | 9500 | 0.0 | - |
| 0.3698 | 9550 | 0.0 | - |
| 0.3718 | 9600 | 0.0 | - |
| 0.3737 | 9650 | 0.0 | - |
| 0.3756 | 9700 | 0.0 | - |
| 0.3776 | 9750 | 0.0 | - |
| 0.3795 | 9800 | 0.0 | - |
| 0.3814 | 9850 | 0.0 | - |
| 0.3834 | 9900 | 0.0 | - |
| 0.3853 | 9950 | 0.0 | - |
| 0.3873 | 10000 | 0.0 | - |
| 0.3892 | 10050 | 0.0 | - |
| 0.3911 | 10100 | 0.0 | - |
| 0.3931 | 10150 | 0.0 | - |
| 0.3950 | 10200 | 0.0 | - |
| 0.3969 | 10250 | 0.0 | - |
| 0.3989 | 10300 | 0.0 | - |
| 0.4008 | 10350 | 0.0 | - |
| 0.4027 | 10400 | 0.0 | - |
| 0.4047 | 10450 | 0.0 | - |
| 0.4066 | 10500 | 0.0 | - |
| 0.4086 | 10550 | 0.0 | - |
| 0.4105 | 10600 | 0.0 | - |
| 0.4124 | 10650 | 0.0 | - |
| 0.4144 | 10700 | 0.0 | - |
| 0.4163 | 10750 | 0.0 | - |
| 0.4182 | 10800 | 0.0 | - |
| 0.4202 | 10850 | 0.0 | - |
| 0.4221 | 10900 | 0.0 | - |
| 0.4240 | 10950 | 0.0 | - |
| 0.4260 | 11000 | 0.0 | - |
| 0.4279 | 11050 | 0.0 | - |
| 0.4298 | 11100 | 0.0 | - |
| 0.4318 | 11150 | 0.0 | - |
| 0.4337 | 11200 | 0.0 | - |
| 0.4357 | 11250 | 0.0 | - |
| 0.4376 | 11300 | 0.0 | - |
| 0.4395 | 11350 | 0.0 | - |
| 0.4415 | 11400 | 0.0 | - |
| 0.4434 | 11450 | 0.0 | - |
| 0.4453 | 11500 | 0.0 | - |
| 0.4473 | 11550 | 0.0 | - |
| 0.4492 | 11600 | 0.0 | - |
| 0.4511 | 11650 | 0.0 | - |
| 0.4531 | 11700 | 0.0 | - |
| 0.4550 | 11750 | 0.0 | - |
| 0.4570 | 11800 | 0.0 | - |
| 0.4589 | 11850 | 0.0109 | - |
| 0.4608 | 11900 | 0.0218 | - |
| 0.4628 | 11950 | 0.0073 | - |
| 0.4647 | 12000 | 0.0056 | - |
| 0.4666 | 12050 | 0.0037 | - |
| 0.4686 | 12100 | 0.0011 | - |
| 0.4705 | 12150 | 0.0002 | - |
| 0.4724 | 12200 | 0.0014 | - |
| 0.4744 | 12250 | 0.0031 | - |
| 0.4763 | 12300 | 0.0013 | - |
| 0.4783 | 12350 | 0.0012 | - |
| 0.4802 | 12400 | 0.0022 | - |
| 0.4821 | 12450 | 0.0003 | - |
| 0.4841 | 12500 | 0.0 | - |
| 0.4860 | 12550 | 0.0 | - |
| 0.4879 | 12600 | 0.0 | - |
| 0.4899 | 12650 | 0.0 | - |
| 0.4918 | 12700 | 0.0 | - |
| 0.4937 | 12750 | 0.0 | - |
| 0.4957 | 12800 | 0.0 | - |
| 0.4976 | 12850 | 0.0 | - |
| 0.4996 | 12900 | 0.0 | - |
| 0.5015 | 12950 | 0.0 | - |
| 0.5034 | 13000 | 0.0 | - |
| 0.5054 | 13050 | 0.0 | - |
| 0.5073 | 13100 | 0.0 | - |
| 0.5092 | 13150 | 0.0 | - |
| 0.5112 | 13200 | 0.0 | - |
| 0.5131 | 13250 | 0.0 | - |
| 0.5150 | 13300 | 0.0 | - |
| 0.5170 | 13350 | 0.0 | - |
| 0.5189 | 13400 | 0.0 | - |
| 0.5209 | 13450 | 0.0 | - |
| 0.5228 | 13500 | 0.0 | - |
| 0.5247 | 13550 | 0.0 | - |
| 0.5267 | 13600 | 0.0 | - |
| 0.5286 | 13650 | 0.0 | - |
| 0.5305 | 13700 | 0.0 | - |
| 0.5325 | 13750 | 0.0 | - |
| 0.5344 | 13800 | 0.0 | - |
| 0.5363 | 13850 | 0.0 | - |
| 0.5383 | 13900 | 0.0 | - |
| 0.5402 | 13950 | 0.0 | - |
| 0.5422 | 14000 | 0.0 | - |
| 0.5441 | 14050 | 0.0 | - |
| 0.5460 | 14100 | 0.0 | - |
| 0.5480 | 14150 | 0.0 | - |
| 0.5499 | 14200 | 0.0 | - |
| 0.5518 | 14250 | 0.0 | - |
| 0.5538 | 14300 | 0.0 | - |
| 0.5557 | 14350 | 0.0 | - |
| 0.5576 | 14400 | 0.0 | - |
| 0.5596 | 14450 | 0.0 | - |
| 0.5615 | 14500 | 0.0 | - |
| 0.5635 | 14550 | 0.0 | - |
| 0.5654 | 14600 | 0.0 | - |
| 0.5673 | 14650 | 0.0 | - |
| 0.5693 | 14700 | 0.0 | - |
| 0.5712 | 14750 | 0.0 | - |
| 0.5731 | 14800 | 0.0 | - |
| 0.5751 | 14850 | 0.0 | - |
| 0.5770 | 14900 | 0.0 | - |
| 0.5789 | 14950 | 0.0 | - |
| 0.5809 | 15000 | 0.0 | - |
| 0.5828 | 15050 | 0.0 | - |
| 0.5848 | 15100 | 0.0 | - |
| 0.5867 | 15150 | 0.0 | - |
| 0.5886 | 15200 | 0.0 | - |
| 0.5906 | 15250 | 0.0 | - |
| 0.5925 | 15300 | 0.0 | - |
| 0.5944 | 15350 | 0.0 | - |
| 0.5964 | 15400 | 0.0 | - |
| 0.5983 | 15450 | 0.0 | - |
| 0.6002 | 15500 | 0.0 | - |
| 0.6022 | 15550 | 0.0 | - |
| 0.6041 | 15600 | 0.0 | - |
| 0.6060 | 15650 | 0.0 | - |
| 0.6080 | 15700 | 0.0 | - |
| 0.6099 | 15750 | 0.0 | - |
| 0.6119 | 15800 | 0.0 | - |
| 0.6138 | 15850 | 0.0 | - |
| 0.6157 | 15900 | 0.0 | - |
| 0.6177 | 15950 | 0.0 | - |
| 0.6196 | 16000 | 0.0 | - |
| 0.6215 | 16050 | 0.0 | - |
| 0.6235 | 16100 | 0.0 | - |
| 0.6254 | 16150 | 0.0002 | - |
| 0.6273 | 16200 | 0.0 | - |
| 0.6293 | 16250 | 0.0002 | - |
| 0.6312 | 16300 | 0.0034 | - |
| 0.6332 | 16350 | 0.0062 | - |
| 0.6351 | 16400 | 0.0034 | - |
| 0.6370 | 16450 | 0.0001 | - |
| 0.6390 | 16500 | 0.0004 | - |
| 0.6409 | 16550 | 0.0 | - |
| 0.6428 | 16600 | 0.0 | - |
| 0.6448 | 16650 | 0.0 | - |
| 0.6467 | 16700 | 0.0 | - |
| 0.6486 | 16750 | 0.0 | - |
| 0.6506 | 16800 | 0.0 | - |
| 0.6525 | 16850 | 0.0 | - |
| 0.6545 | 16900 | 0.0 | - |
| 0.6564 | 16950 | 0.0 | - |
| 0.6583 | 17000 | 0.0 | - |
| 0.6603 | 17050 | 0.0 | - |
| 0.6622 | 17100 | 0.0 | - |
| 0.6641 | 17150 | 0.0 | - |
| 0.6661 | 17200 | 0.0 | - |
| 0.6680 | 17250 | 0.0 | - |
| 0.6699 | 17300 | 0.0 | - |
| 0.6719 | 17350 | 0.0 | - |
| 0.6738 | 17400 | 0.0 | - |
| 0.6758 | 17450 | 0.0 | - |
| 0.6777 | 17500 | 0.0 | - |
| 0.6796 | 17550 | 0.0 | - |
| 0.6816 | 17600 | 0.0 | - |
| 0.6835 | 17650 | 0.0 | - |
| 0.6854 | 17700 | 0.0 | - |
| 0.6874 | 17750 | 0.0 | - |
| 0.6893 | 17800 | 0.0 | - |
| 0.6912 | 17850 | 0.0 | - |
| 0.6932 | 17900 | 0.0 | - |
| 0.6951 | 17950 | 0.0 | - |
| 0.6971 | 18000 | 0.0 | - |
| 0.6990 | 18050 | 0.0 | - |
| 0.7009 | 18100 | 0.0 | - |
| 0.7029 | 18150 | 0.0 | - |
| 0.7048 | 18200 | 0.0 | - |
| 0.7067 | 18250 | 0.0 | - |
| 0.7087 | 18300 | 0.0 | - |
| 0.7106 | 18350 | 0.0 | - |
| 0.7125 | 18400 | 0.0 | - |
| 0.7145 | 18450 | 0.0 | - |
| 0.7164 | 18500 | 0.0 | - |
| 0.7184 | 18550 | 0.0 | - |
| 0.7203 | 18600 | 0.0 | - |
| 0.7222 | 18650 | 0.0 | - |
| 0.7242 | 18700 | 0.0 | - |
| 0.7261 | 18750 | 0.0 | - |
| 0.7280 | 18800 | 0.0 | - |
| 0.7300 | 18850 | 0.0 | - |
| 0.7319 | 18900 | 0.0 | - |
| 0.7338 | 18950 | 0.0 | - |
| 0.7358 | 19000 | 0.0 | - |
| 0.7377 | 19050 | 0.0 | - |
| 0.7397 | 19100 | 0.0 | - |
| 0.7416 | 19150 | 0.0 | - |
| 0.7435 | 19200 | 0.0 | - |
| 0.7455 | 19250 | 0.0 | - |
| 0.7474 | 19300 | 0.0 | - |
| 0.7493 | 19350 | 0.0 | - |
| 0.7513 | 19400 | 0.0 | - |
| 0.7532 | 19450 | 0.0 | - |
| 0.7551 | 19500 | 0.0 | - |
| 0.7571 | 19550 | 0.0 | - |
| 0.7590 | 19600 | 0.0 | - |
| 0.7609 | 19650 | 0.0 | - |
| 0.7629 | 19700 | 0.0 | - |
| 0.7648 | 19750 | 0.0 | - |
| 0.7668 | 19800 | 0.0 | - |
| 0.7687 | 19850 | 0.0 | - |
| 0.7706 | 19900 | 0.0 | - |
| 0.7726 | 19950 | 0.0 | - |
| 0.7745 | 20000 | 0.0 | - |
| 0.7764 | 20050 | 0.0 | - |
| 0.7784 | 20100 | 0.0 | - |
| 0.7803 | 20150 | 0.0 | - |
| 0.7822 | 20200 | 0.0 | - |
| 0.7842 | 20250 | 0.0 | - |
| 0.7861 | 20300 | 0.0 | - |
| 0.7881 | 20350 | 0.0 | - |
| 0.7900 | 20400 | 0.0 | - |
| 0.7919 | 20450 | 0.0 | - |
| 0.7939 | 20500 | 0.0 | - |
| 0.7958 | 20550 | 0.0 | - |
| 0.7977 | 20600 | 0.0 | - |
| 0.7997 | 20650 | 0.0 | - |
| 0.8016 | 20700 | 0.0 | - |
| 0.8035 | 20750 | 0.0 | - |
| 0.8055 | 20800 | 0.0 | - |
| 0.8074 | 20850 | 0.0 | - |
| 0.8094 | 20900 | 0.0 | - |
| 0.8113 | 20950 | 0.0 | - |
| 0.8132 | 21000 | 0.0 | - |
| 0.8152 | 21050 | 0.0 | - |
| 0.8171 | 21100 | 0.0 | - |
| 0.8190 | 21150 | 0.0 | - |
| 0.8210 | 21200 | 0.0 | - |
| 0.8229 | 21250 | 0.0 | - |
| 0.8248 | 21300 | 0.0 | - |
| 0.8268 | 21350 | 0.0 | - |
| 0.8287 | 21400 | 0.0 | - |
| 0.8307 | 21450 | 0.0 | - |
| 0.8326 | 21500 | 0.0 | - |
| 0.8345 | 21550 | 0.0 | - |
| 0.8365 | 21600 | 0.0 | - |
| 0.8384 | 21650 | 0.0 | - |
| 0.8403 | 21700 | 0.0 | - |
| 0.8423 | 21750 | 0.0 | - |
| 0.8442 | 21800 | 0.0 | - |
| 0.8461 | 21850 | 0.0 | - |
| 0.8481 | 21900 | 0.0 | - |
| 0.8500 | 21950 | 0.0 | - |
| 0.8520 | 22000 | 0.0 | - |
| 0.8539 | 22050 | 0.0 | - |
| 0.8558 | 22100 | 0.0 | - |
| 0.8578 | 22150 | 0.0 | - |
| 0.8597 | 22200 | 0.0 | - |
| 0.8616 | 22250 | 0.0 | - |
| 0.8636 | 22300 | 0.0 | - |
| 0.8655 | 22350 | 0.0 | - |
| 0.8674 | 22400 | 0.0 | - |
| 0.8694 | 22450 | 0.0 | - |
| 0.8713 | 22500 | 0.0 | - |
| 0.8733 | 22550 | 0.0 | - |
| 0.8752 | 22600 | 0.0 | - |
| 0.8771 | 22650 | 0.0 | - |
| 0.8791 | 22700 | 0.0 | - |
| 0.8810 | 22750 | 0.0 | - |
| 0.8829 | 22800 | 0.0 | - |
| 0.8849 | 22850 | 0.0 | - |
| 0.8868 | 22900 | 0.0 | - |
| 0.8887 | 22950 | 0.0 | - |
| 0.8907 | 23000 | 0.0 | - |
| 0.8926 | 23050 | 0.0 | - |
| 0.8946 | 23100 | 0.0 | - |
| 0.8965 | 23150 | 0.0 | - |
| 0.8984 | 23200 | 0.0 | - |
| 0.9004 | 23250 | 0.0 | - |
| 0.9023 | 23300 | 0.0 | - |
| 0.9042 | 23350 | 0.0 | - |
| 0.9062 | 23400 | 0.0 | - |
| 0.9081 | 23450 | 0.0 | - |
| 0.9100 | 23500 | 0.0 | - |
| 0.9120 | 23550 | 0.0 | - |
| 0.9139 | 23600 | 0.0 | - |
| 0.9159 | 23650 | 0.0 | - |
| 0.9178 | 23700 | 0.0 | - |
| 0.9197 | 23750 | 0.0 | - |
| 0.9217 | 23800 | 0.0 | - |
| 0.9236 | 23850 | 0.0 | - |
| 0.9255 | 23900 | 0.0 | - |
| 0.9275 | 23950 | 0.0 | - |
| 0.9294 | 24000 | 0.0 | - |
| 0.9313 | 24050 | 0.0 | - |
| 0.9333 | 24100 | 0.0 | - |
| 0.9352 | 24150 | 0.0 | - |
| 0.9371 | 24200 | 0.0 | - |
| 0.9391 | 24250 | 0.0 | - |
| 0.9410 | 24300 | 0.0 | - |
| 0.9430 | 24350 | 0.0 | - |
| 0.9449 | 24400 | 0.0 | - |
| 0.9468 | 24450 | 0.0 | - |
| 0.9488 | 24500 | 0.0 | - |
| 0.9507 | 24550 | 0.0 | - |
| 0.9526 | 24600 | 0.0 | - |
| 0.9546 | 24650 | 0.0 | - |
| 0.9565 | 24700 | 0.0 | - |
| 0.9584 | 24750 | 0.0 | - |
| 0.9604 | 24800 | 0.0 | - |
| 0.9623 | 24850 | 0.0 | - |
| 0.9643 | 24900 | 0.0 | - |
| 0.9662 | 24950 | 0.0 | - |
| 0.9681 | 25000 | 0.0 | - |
| 0.9701 | 25050 | 0.0 | - |
| 0.9720 | 25100 | 0.0 | - |
| 0.9739 | 25150 | 0.0 | - |
| 0.9759 | 25200 | 0.0 | - |
| 0.9778 | 25250 | 0.0 | - |
| 0.9797 | 25300 | 0.0 | - |
| 0.9817 | 25350 | 0.0 | - |
| 0.9836 | 25400 | 0.0 | - |
| 0.9856 | 25450 | 0.0 | - |
| 0.9875 | 25500 | 0.0 | - |
| 0.9894 | 25550 | 0.0 | - |
| 0.9914 | 25600 | 0.0 | - |
| 0.9933 | 25650 | 0.0 | - |
| 0.9952 | 25700 | 0.0 | - |
| 0.9972 | 25750 | 0.0 | - |
| 0.9991 | 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}
}
```