setfit safetensors bert sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 base_model:finetune:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model-index region:us
---
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Mit dem geplanten Heizungsgesetz setzt die Regierung einen wichtigen Schritt
    in Richtung eines klimafreundlichen Wärmemarktes. Die flächendeckende Einführung
    von Wärmepumpen soll den Verbrauch von fossilen Energieträgern reduzieren und
    den Ausstoß an Treibhausgasen senken. Damit trägt das Gesetz zu einer umweltfreundlicheren
    Heizungsinfrastruktur bei.
- text: '"Das Heizungsgesetz: Eine teure, ineffiziente und überbordete Lösung für
    unsere Energieprobleme? Die geplanten Wärmepumpen in jedem Haus wirken sich negativ
    auf die Umwelt aus und werden wahrscheinlich Millionen von Steuergeldern verschlingen.
    Wir brauchen eine realistische Energiewende, nicht ein teures Experiment."'
- text: Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden Einführung
    von Wärmepumpen verabschiedet, das darauf abzielt, den CO2-Ausstoß im Gebäudesektor
    zu reduzieren. Kritiker bemängeln mögliche hohe Kosten und technische Herausforderungen,
    während Befürworter die Maßnahme als wichtigen Schritt zur Erreichung der Klimaziele
    sehen.
- text: In verschiedenen Städten Deutschlands haben sich wiederum Menschen versammelt,
    um für den Klimaschutz zu demonstrieren. Die Teilnehmer von Fridays for Future
    und der Letzten Generation fordern die Regierung auf, ambitioniertere Maßnahmen
    gegen den Klimawandel zu ergreifen. Ihre Forderungen richten sich an die politischen
    Entscheidungsträger, um eine bessere Zukunft für kommende Generationen zu schaffen.
- text: Der Bundestag debattiert erneut über die Einführung eines generellen Tempolimits
    auf deutschen Autobahnen. Befürworter betonen die positiven Auswirkungen auf Verkehrssicherheit
    und Umwelt, während Kritiker die Einschränkung individueller Freiheit und mögliche
    wirtschaftliche Folgen anführen. Die Entscheidung bleibt umstritten und spiegelt
    die vielfältigen Interessen in der Gesellschaft wider.
inference: true
model-index:
- name: SetFit with sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
  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 sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-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
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)

### Model Labels
| Label      | Examples                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
|:-----------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| supportive | <ul><li>"„Junge Menschen fordern Aktion, um den Klimawandel zu stoppen. Unter dem Motto 'Die Zukunft ist unsere' haben sie sich zu Aktivisten für eine nachhaltige Zukunft entwickelt und mit Massenprotesten auf die dringende Notwendigkeit von Klimaschutzmaßnahmen aufmerksam gemacht.“"</li><li>'Die flächendeckende Einführung von Wärmepumpen, wie sie im neuen Heizungsgesetz vorgesehen ist, könnte einen bedeutenden Schritt hin zu einer nachhaltigeren Energieversorgung darstellen. Durch die Förderung dieser umweltfreundlichen Technologie könnte nicht nur der CO2-Ausstoß erheblich reduziert werden, sondern auch die Abhängigkeit von fossilen Brennstoffen langfristig sinken.'</li><li>'In den letzten Jahren haben Klima-Aktivismus-Gruppen wie Fridays for Future und die Letzte Generation durch ihr Engagement und ihre Beharrlichkeit das Bewusstsein für die dringende Notwendigkeit von Klimaschutzmaßnahmen geschärft. Ihre Aktionen haben es geschafft, das Thema Klimawandel in den Mittelpunkt der gesellschaftlichen und politischen Debatte zu rücken, was langfristig zu einer stärkeren Auseinandersetzung mit umweltpolitischen Herausforderungen führen könnte.'</li></ul> |
| neutral    | <ul><li>'Die Debatte um die Einführung eines nationalen Tempolimits auf Autobahnen bleibt ein kontroverses Thema in Deutschland. Befürworter argumentieren mit Vorteilen für die Verkehrssicherheit und den Umweltschutz, während Gegner mögliche Einschränkungen der individuellen Freiheit und wirtschaftliche Auswirkungen betonen. Der Gesetzgebungsprozess zu diesem Thema wird weiterhin aufmerksam verfolgt.'</li><li>'Das Bundeskabinett hat den Entwurf eines Heizungsgesetzes beschlossen, das die flächendeckende Einführung von Wärmepumpen in Deutschland vorsieht. Demnach soll der Einsatz erneuerbarer Wärmequellen in Gebäuden gefördert werden. Der Gesetzentwurf wird nun dem Bundestag vorgelegt, wo er behandelt und abgestimmt werden muss.'</li><li>'Die Bundesregierung hat ein Gesetz zur Förderung der flächendeckenden Einführung von Wärmepumpen verabschiedet, das den Einsatz erneuerbarer Energien im Heizungssektor vorantreiben soll. Kritiker bemängeln die Umsetzbarkeit und finanzielle Belastung für Hausbesitzer, während Befürworter die Maßnahme als wichtigen Schritt zur Erreichung der Klimaziele betrachten.'</li></ul>                                               |
| opposed    | <ul><li>'Die Straßen blockiert, der Alltag gestört – Klima-Aktivisten wie die Letzte Generation und Fridays for Future sorgen mit ihren Aktionen immer wieder für Chaos und Unmut. Während sie ihre Botschaft lautstark verkünden, fragen sich viele: Ist das der richtige Weg, um echte Veränderungen zu erreichen, oder treibt das nur einen Keil zwischen die Menschen?'</li><li>'Die grüne Verbotskultur schlägt wieder zu: Mit der Einführung eines nationalen Tempolimits auf Autobahnen wird einmal mehr die Freiheit der Autofahrer beschnitten. Statt auf Eigenverantwortung zu setzen, wird der mündige Bürger bevormundet und der deutschen Wirtschaft ein weiterer Stein in den Weg gelegt.'</li><li>'Die selbsternannten Klima-Retter von Fridays for Future und der Letzten Generation scheinen mehr daran interessiert zu sein, den Alltag der Bürger mit ihren fragwürdigen Aktionen zu stören, als tatsächlich sinnvolle Lösungen für den Klimawandel zu präsentieren. Während sie Straßen blockieren und Chaos verursachen, bleibt die Frage offen, ob ihre Methoden mehr Schaden anrichten als Nutzen bringen.'</li></ul>                                                                      |

## 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/paraphrase-multilingual-minilm-klimacoder_v0.10")
# Run inference
preds = model("\"Das Heizungsgesetz: Eine teure, ineffiziente und überbordete Lösung für unsere Energieprobleme? Die geplanten Wärmepumpen in jedem Haus wirken sich negativ auf die Umwelt aus und werden wahrscheinlich Millionen von Steuergeldern verschlingen. Wir brauchen eine realistische Energiewende, nicht ein teures Experiment.\"")
```

<!--
### 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   | 24  | 44.1537 | 73  |

| Label      | Training Sample Count |
|:-----------|:----------------------|
| neutral    | 500                   |
| opposed    | 549                   |
| supportive | 526                   |

### 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     | 0.2419        | -               |
| 0.0010 | 50    | 0.2541        | -               |
| 0.0019 | 100   | 0.2489        | -               |
| 0.0029 | 150   | 0.2404        | -               |
| 0.0039 | 200   | 0.2281        | -               |
| 0.0048 | 250   | 0.2168        | -               |
| 0.0058 | 300   | 0.193         | -               |
| 0.0068 | 350   | 0.1604        | -               |
| 0.0077 | 400   | 0.1304        | -               |
| 0.0087 | 450   | 0.1218        | -               |
| 0.0097 | 500   | 0.1046        | -               |
| 0.0107 | 550   | 0.0978        | -               |
| 0.0116 | 600   | 0.0733        | -               |
| 0.0126 | 650   | 0.061         | -               |
| 0.0136 | 700   | 0.0496        | -               |
| 0.0145 | 750   | 0.0397        | -               |
| 0.0155 | 800   | 0.0331        | -               |
| 0.0165 | 850   | 0.0329        | -               |
| 0.0174 | 900   | 0.0254        | -               |
| 0.0184 | 950   | 0.0194        | -               |
| 0.0194 | 1000  | 0.0154        | -               |
| 0.0203 | 1050  | 0.0111        | -               |
| 0.0213 | 1100  | 0.0112        | -               |
| 0.0223 | 1150  | 0.0107        | -               |
| 0.0232 | 1200  | 0.0065        | -               |
| 0.0242 | 1250  | 0.0046        | -               |
| 0.0252 | 1300  | 0.0059        | -               |
| 0.0261 | 1350  | 0.0033        | -               |
| 0.0271 | 1400  | 0.003         | -               |
| 0.0281 | 1450  | 0.0024        | -               |
| 0.0290 | 1500  | 0.0018        | -               |
| 0.0300 | 1550  | 0.001         | -               |
| 0.0310 | 1600  | 0.0011        | -               |
| 0.0320 | 1650  | 0.0012        | -               |
| 0.0329 | 1700  | 0.0007        | -               |
| 0.0339 | 1750  | 0.0007        | -               |
| 0.0349 | 1800  | 0.0005        | -               |
| 0.0358 | 1850  | 0.0004        | -               |
| 0.0368 | 1900  | 0.0003        | -               |
| 0.0378 | 1950  | 0.0006        | -               |
| 0.0387 | 2000  | 0.0004        | -               |
| 0.0397 | 2050  | 0.0003        | -               |
| 0.0407 | 2100  | 0.0002        | -               |
| 0.0416 | 2150  | 0.0003        | -               |
| 0.0426 | 2200  | 0.0005        | -               |
| 0.0436 | 2250  | 0.0005        | -               |
| 0.0445 | 2300  | 0.0001        | -               |
| 0.0455 | 2350  | 0.0003        | -               |
| 0.0465 | 2400  | 0.0003        | -               |
| 0.0474 | 2450  | 0.0002        | -               |
| 0.0484 | 2500  | 0.0003        | -               |
| 0.0494 | 2550  | 0.0001        | -               |
| 0.0503 | 2600  | 0.0002        | -               |
| 0.0513 | 2650  | 0.0003        | -               |
| 0.0523 | 2700  | 0.0004        | -               |
| 0.0533 | 2750  | 0.0007        | -               |
| 0.0542 | 2800  | 0.0001        | -               |
| 0.0552 | 2850  | 0.0002        | -               |
| 0.0562 | 2900  | 0.0001        | -               |
| 0.0571 | 2950  | 0.0001        | -               |
| 0.0581 | 3000  | 0.0001        | -               |
| 0.0591 | 3050  | 0.0001        | -               |
| 0.0600 | 3100  | 0.0001        | -               |
| 0.0610 | 3150  | 0.0           | -               |
| 0.0620 | 3200  | 0.0           | -               |
| 0.0629 | 3250  | 0.0           | -               |
| 0.0639 | 3300  | 0.0001        | -               |
| 0.0649 | 3350  | 0.0006        | -               |
| 0.0658 | 3400  | 0.0           | -               |
| 0.0668 | 3450  | 0.0           | -               |
| 0.0678 | 3500  | 0.0001        | -               |
| 0.0687 | 3550  | 0.0           | -               |
| 0.0697 | 3600  | 0.0           | -               |
| 0.0707 | 3650  | 0.0001        | -               |
| 0.0716 | 3700  | 0.0001        | -               |
| 0.0726 | 3750  | 0.0           | -               |
| 0.0736 | 3800  | 0.0           | -               |
| 0.0746 | 3850  | 0.0           | -               |
| 0.0755 | 3900  | 0.0           | -               |
| 0.0765 | 3950  | 0.0           | -               |
| 0.0775 | 4000  | 0.0           | -               |
| 0.0784 | 4050  | 0.0           | -               |
| 0.0794 | 4100  | 0.0           | -               |
| 0.0804 | 4150  | 0.0           | -               |
| 0.0813 | 4200  | 0.0           | -               |
| 0.0823 | 4250  | 0.0           | -               |
| 0.0833 | 4300  | 0.0           | -               |
| 0.0842 | 4350  | 0.0027        | -               |
| 0.0852 | 4400  | 0.0021        | -               |
| 0.0862 | 4450  | 0.0013        | -               |
| 0.0871 | 4500  | 0.0022        | -               |
| 0.0881 | 4550  | 0.004         | -               |
| 0.0891 | 4600  | 0.0017        | -               |
| 0.0900 | 4650  | 0.0054        | -               |
| 0.0910 | 4700  | 0.0019        | -               |
| 0.0920 | 4750  | 0.0009        | -               |
| 0.0929 | 4800  | 0.0001        | -               |
| 0.0939 | 4850  | 0.0           | -               |
| 0.0949 | 4900  | 0.0           | -               |
| 0.0959 | 4950  | 0.0           | -               |
| 0.0968 | 5000  | 0.0           | -               |
| 0.0978 | 5050  | 0.0           | -               |
| 0.0988 | 5100  | 0.0           | -               |
| 0.0997 | 5150  | 0.0           | -               |
| 0.1007 | 5200  | 0.0           | -               |
| 0.1017 | 5250  | 0.0           | -               |
| 0.1026 | 5300  | 0.0           | -               |
| 0.1036 | 5350  | 0.0           | -               |
| 0.1046 | 5400  | 0.0           | -               |
| 0.1055 | 5450  | 0.0           | -               |
| 0.1065 | 5500  | 0.0           | -               |
| 0.1075 | 5550  | 0.0           | -               |
| 0.1084 | 5600  | 0.0           | -               |
| 0.1094 | 5650  | 0.0           | -               |
| 0.1104 | 5700  | 0.0           | -               |
| 0.1113 | 5750  | 0.0           | -               |
| 0.1123 | 5800  | 0.0           | -               |
| 0.1133 | 5850  | 0.0           | -               |
| 0.1142 | 5900  | 0.0           | -               |
| 0.1152 | 5950  | 0.0           | -               |
| 0.1162 | 6000  | 0.0           | -               |
| 0.1172 | 6050  | 0.0           | -               |
| 0.1181 | 6100  | 0.0           | -               |
| 0.1191 | 6150  | 0.0           | -               |
| 0.1201 | 6200  | 0.0           | -               |
| 0.1210 | 6250  | 0.0           | -               |
| 0.1220 | 6300  | 0.0           | -               |
| 0.1230 | 6350  | 0.0           | -               |
| 0.1239 | 6400  | 0.0           | -               |
| 0.1249 | 6450  | 0.0           | -               |
| 0.1259 | 6500  | 0.0           | -               |
| 0.1268 | 6550  | 0.0           | -               |
| 0.1278 | 6600  | 0.0           | -               |
| 0.1288 | 6650  | 0.0           | -               |
| 0.1297 | 6700  | 0.0           | -               |
| 0.1307 | 6750  | 0.0           | -               |
| 0.1317 | 6800  | 0.0           | -               |
| 0.1326 | 6850  | 0.0           | -               |
| 0.1336 | 6900  | 0.0           | -               |
| 0.1346 | 6950  | 0.0           | -               |
| 0.1355 | 7000  | 0.0           | -               |
| 0.1365 | 7050  | 0.0           | -               |
| 0.1375 | 7100  | 0.0           | -               |
| 0.1385 | 7150  | 0.0           | -               |
| 0.1394 | 7200  | 0.0           | -               |
| 0.1404 | 7250  | 0.0           | -               |
| 0.1414 | 7300  | 0.0           | -               |
| 0.1423 | 7350  | 0.0           | -               |
| 0.1433 | 7400  | 0.0           | -               |
| 0.1443 | 7450  | 0.0           | -               |
| 0.1452 | 7500  | 0.0           | -               |
| 0.1462 | 7550  | 0.0           | -               |
| 0.1472 | 7600  | 0.0           | -               |
| 0.1481 | 7650  | 0.0           | -               |
| 0.1491 | 7700  | 0.0           | -               |
| 0.1501 | 7750  | 0.0           | -               |
| 0.1510 | 7800  | 0.0           | -               |
| 0.1520 | 7850  | 0.0           | -               |
| 0.1530 | 7900  | 0.0           | -               |
| 0.1539 | 7950  | 0.0           | -               |
| 0.1549 | 8000  | 0.0           | -               |
| 0.1559 | 8050  | 0.0           | -               |
| 0.1568 | 8100  | 0.0           | -               |
| 0.1578 | 8150  | 0.0           | -               |
| 0.1588 | 8200  | 0.0           | -               |
| 0.1598 | 8250  | 0.0           | -               |
| 0.1607 | 8300  | 0.0           | -               |
| 0.1617 | 8350  | 0.0           | -               |
| 0.1627 | 8400  | 0.0           | -               |
| 0.1636 | 8450  | 0.0           | -               |
| 0.1646 | 8500  | 0.0           | -               |
| 0.1656 | 8550  | 0.0           | -               |
| 0.1665 | 8600  | 0.0           | -               |
| 0.1675 | 8650  | 0.0           | -               |
| 0.1685 | 8700  | 0.0           | -               |
| 0.1694 | 8750  | 0.0           | -               |
| 0.1704 | 8800  | 0.0           | -               |
| 0.1714 | 8850  | 0.0           | -               |
| 0.1723 | 8900  | 0.0           | -               |
| 0.1733 | 8950  | 0.0           | -               |
| 0.1743 | 9000  | 0.0           | -               |
| 0.1752 | 9050  | 0.0           | -               |
| 0.1762 | 9100  | 0.0           | -               |
| 0.1772 | 9150  | 0.0           | -               |
| 0.1781 | 9200  | 0.0           | -               |
| 0.1791 | 9250  | 0.0           | -               |
| 0.1801 | 9300  | 0.0           | -               |
| 0.1811 | 9350  | 0.0           | -               |
| 0.1820 | 9400  | 0.0           | -               |
| 0.1830 | 9450  | 0.0           | -               |
| 0.1840 | 9500  | 0.0           | -               |
| 0.1849 | 9550  | 0.0           | -               |
| 0.1859 | 9600  | 0.0           | -               |
| 0.1869 | 9650  | 0.0           | -               |
| 0.1878 | 9700  | 0.0           | -               |
| 0.1888 | 9750  | 0.0           | -               |
| 0.1898 | 9800  | 0.0           | -               |
| 0.1907 | 9850  | 0.0           | -               |
| 0.1917 | 9900  | 0.0           | -               |
| 0.1927 | 9950  | 0.0           | -               |
| 0.1936 | 10000 | 0.0           | -               |
| 0.1946 | 10050 | 0.0           | -               |
| 0.1956 | 10100 | 0.0           | -               |
| 0.1965 | 10150 | 0.0           | -               |
| 0.1975 | 10200 | 0.0           | -               |
| 0.1985 | 10250 | 0.0           | -               |
| 0.1994 | 10300 | 0.0           | -               |
| 0.2004 | 10350 | 0.0           | -               |
| 0.2014 | 10400 | 0.0           | -               |
| 0.2024 | 10450 | 0.0           | -               |
| 0.2033 | 10500 | 0.0           | -               |
| 0.2043 | 10550 | 0.0           | -               |
| 0.2053 | 10600 | 0.0           | -               |
| 0.2062 | 10650 | 0.0           | -               |
| 0.2072 | 10700 | 0.0           | -               |
| 0.2082 | 10750 | 0.0           | -               |
| 0.2091 | 10800 | 0.0           | -               |
| 0.2101 | 10850 | 0.0           | -               |
| 0.2111 | 10900 | 0.0           | -               |
| 0.2120 | 10950 | 0.0           | -               |
| 0.2130 | 11000 | 0.0           | -               |
| 0.2140 | 11050 | 0.0           | -               |
| 0.2149 | 11100 | 0.0           | -               |
| 0.2159 | 11150 | 0.0           | -               |
| 0.2169 | 11200 | 0.0           | -               |
| 0.2178 | 11250 | 0.0           | -               |
| 0.2188 | 11300 | 0.0           | -               |
| 0.2198 | 11350 | 0.0           | -               |
| 0.2207 | 11400 | 0.0           | -               |
| 0.2217 | 11450 | 0.0           | -               |
| 0.2227 | 11500 | 0.0           | -               |
| 0.2237 | 11550 | 0.0           | -               |
| 0.2246 | 11600 | 0.0           | -               |
| 0.2256 | 11650 | 0.0           | -               |
| 0.2266 | 11700 | 0.0           | -               |
| 0.2275 | 11750 | 0.0           | -               |
| 0.2285 | 11800 | 0.0           | -               |
| 0.2295 | 11850 | 0.0           | -               |
| 0.2304 | 11900 | 0.0           | -               |
| 0.2314 | 11950 | 0.0           | -               |
| 0.2324 | 12000 | 0.0           | -               |
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| 0.2430 | 12550 | 0.0           | -               |
| 0.2440 | 12600 | 0.0           | -               |
| 0.2450 | 12650 | 0.0           | -               |
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| 0.2469 | 12750 | 0.0           | -               |
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| 0.2488 | 12850 | 0.0           | -               |
| 0.2498 | 12900 | 0.0           | -               |
| 0.2508 | 12950 | 0.0001        | -               |
| 0.2517 | 13000 | 0.0           | -               |
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| 0.3011 | 15550 | 0.0           | -               |
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| 0.3863 | 19950 | 0.0           | -               |
| 0.3873 | 20000 | 0.0165        | -               |
| 0.3882 | 20050 | 0.0065        | -               |
| 0.3892 | 20100 | 0.0014        | -               |
| 0.3902 | 20150 | 0.002         | -               |
| 0.3911 | 20200 | 0.0011        | -               |
| 0.3921 | 20250 | 0.0           | -               |
| 0.3931 | 20300 | 0.0014        | -               |
| 0.3941 | 20350 | 0.0           | -               |
| 0.3950 | 20400 | 0.0008        | -               |
| 0.3960 | 20450 | 0.0           | -               |
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| 0.4889 | 25250 | 0.0           | -               |
| 0.4899 | 25300 | 0.0           | -               |
| 0.4909 | 25350 | 0.0           | -               |
| 0.4918 | 25400 | 0.0           | -               |
| 0.4928 | 25450 | 0.0           | -               |
| 0.4938 | 25500 | 0.0           | -               |
| 0.4947 | 25550 | 0.0           | -               |
| 0.4957 | 25600 | 0.0           | -               |
| 0.4967 | 25650 | 0.0           | -               |
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| 0.4986 | 25750 | 0.0           | -               |
| 0.4996 | 25800 | 0.0           | -               |
| 0.5006 | 25850 | 0.0           | -               |
| 0.5015 | 25900 | 0.0           | -               |
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| 0.5035 | 26000 | 0.0           | -               |
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| 0.5064 | 26150 | 0.0           | -               |
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| 0.5102 | 26350 | 0.0           | -               |
| 0.5112 | 26400 | 0.0           | -               |
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| 0.5131 | 26500 | 0.0           | -               |
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| 0.5180 | 26750 | 0.0           | -               |
| 0.5189 | 26800 | 0.0           | -               |
| 0.5199 | 26850 | 0.0           | -               |
| 0.5209 | 26900 | 0.0           | -               |
| 0.5219 | 26950 | 0.0           | -               |
| 0.5228 | 27000 | 0.0           | -               |
| 0.5238 | 27050 | 0.0           | -               |
| 0.5248 | 27100 | 0.0           | -               |
| 0.5257 | 27150 | 0.0           | -               |
| 0.5267 | 27200 | 0.0           | -               |
| 0.5277 | 27250 | 0.0           | -               |
| 0.5286 | 27300 | 0.0           | -               |
| 0.5296 | 27350 | 0.0           | -               |
| 0.5306 | 27400 | 0.0           | -               |
| 0.5315 | 27450 | 0.0           | -               |
| 0.5325 | 27500 | 0.0           | -               |
| 0.5335 | 27550 | 0.0           | -               |
| 0.5344 | 27600 | 0.0           | -               |
| 0.5354 | 27650 | 0.0           | -               |
| 0.5364 | 27700 | 0.0           | -               |
| 0.5373 | 27750 | 0.0           | -               |
| 0.5383 | 27800 | 0.0           | -               |
| 0.5393 | 27850 | 0.0           | -               |
| 0.5402 | 27900 | 0.0           | -               |
| 0.5412 | 27950 | 0.0           | -               |
| 0.5422 | 28000 | 0.0           | -               |
| 0.5432 | 28050 | 0.0           | -               |
| 0.5441 | 28100 | 0.0           | -               |
| 0.5451 | 28150 | 0.0           | -               |
| 0.5461 | 28200 | 0.0           | -               |
| 0.5470 | 28250 | 0.0           | -               |
| 0.5480 | 28300 | 0.0           | -               |
| 0.5490 | 28350 | 0.0           | -               |
| 0.5499 | 28400 | 0.0           | -               |
| 0.5509 | 28450 | 0.0           | -               |
| 0.5519 | 28500 | 0.0           | -               |
| 0.5528 | 28550 | 0.0           | -               |
| 0.5538 | 28600 | 0.0           | -               |
| 0.5548 | 28650 | 0.0           | -               |
| 0.5557 | 28700 | 0.0           | -               |
| 0.5567 | 28750 | 0.0           | -               |
| 0.5577 | 28800 | 0.0           | -               |
| 0.5586 | 28850 | 0.0           | -               |
| 0.5596 | 28900 | 0.0           | -               |
| 0.5606 | 28950 | 0.0           | -               |
| 0.5615 | 29000 | 0.0           | -               |
| 0.5625 | 29050 | 0.0           | -               |
| 0.5635 | 29100 | 0.0           | -               |
| 0.5645 | 29150 | 0.0           | -               |
| 0.5654 | 29200 | 0.0           | -               |
| 0.5664 | 29250 | 0.0           | -               |
| 0.5674 | 29300 | 0.0           | -               |
| 0.5683 | 29350 | 0.0           | -               |
| 0.5693 | 29400 | 0.0           | -               |
| 0.5703 | 29450 | 0.0           | -               |
| 0.5712 | 29500 | 0.0           | -               |
| 0.5722 | 29550 | 0.0           | -               |
| 0.5732 | 29600 | 0.0           | -               |
| 0.5741 | 29650 | 0.0           | -               |
| 0.5751 | 29700 | 0.0           | -               |
| 0.5761 | 29750 | 0.0           | -               |
| 0.5770 | 29800 | 0.0           | -               |
| 0.5780 | 29850 | 0.0           | -               |
| 0.5790 | 29900 | 0.0           | -               |
| 0.5799 | 29950 | 0.0           | -               |
| 0.5809 | 30000 | 0.0           | -               |
| 0.5819 | 30050 | 0.0           | -               |
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| 0.5858 | 30250 | 0.0           | -               |
| 0.5867 | 30300 | 0.0           | -               |
| 0.5877 | 30350 | 0.0           | -               |
| 0.5887 | 30400 | 0.0           | -               |
| 0.5896 | 30450 | 0.0           | -               |
| 0.5906 | 30500 | 0.0           | -               |
| 0.5916 | 30550 | 0.0           | -               |
| 0.5925 | 30600 | 0.0           | -               |
| 0.5935 | 30650 | 0.0           | -               |
| 0.5945 | 30700 | 0.0           | -               |
| 0.5954 | 30750 | 0.0           | -               |
| 0.5964 | 30800 | 0.0           | -               |
| 0.5974 | 30850 | 0.0           | -               |
| 0.5983 | 30900 | 0.0           | -               |
| 0.5993 | 30950 | 0.0           | -               |
| 0.6003 | 31000 | 0.0           | -               |
| 0.6012 | 31050 | 0.0           | -               |
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| 0.6061 | 31300 | 0.0           | -               |
| 0.6071 | 31350 | 0.0           | -               |
| 0.6080 | 31400 | 0.0           | -               |
| 0.6090 | 31450 | 0.0           | -               |
| 0.6100 | 31500 | 0.0           | -               |
| 0.6109 | 31550 | 0.0           | -               |
| 0.6119 | 31600 | 0.0           | -               |
| 0.6129 | 31650 | 0.0           | -               |
| 0.6138 | 31700 | 0.0           | -               |
| 0.6148 | 31750 | 0.0           | -               |
| 0.6158 | 31800 | 0.0           | -               |
| 0.6167 | 31850 | 0.0           | -               |
| 0.6177 | 31900 | 0.0           | -               |
| 0.6187 | 31950 | 0.0           | -               |
| 0.6196 | 32000 | 0.0           | -               |
| 0.6206 | 32050 | 0.0           | -               |
| 0.6216 | 32100 | 0.0           | -               |
| 0.6225 | 32150 | 0.0           | -               |
| 0.6235 | 32200 | 0.0           | -               |
| 0.6245 | 32250 | 0.0           | -               |
| 0.6254 | 32300 | 0.0           | -               |
| 0.6264 | 32350 | 0.0           | -               |
| 0.6274 | 32400 | 0.0           | -               |
| 0.6284 | 32450 | 0.0           | -               |
| 0.6293 | 32500 | 0.0           | -               |
| 0.6303 | 32550 | 0.0           | -               |
| 0.6313 | 32600 | 0.0           | -               |
| 0.6322 | 32650 | 0.0           | -               |
| 0.6332 | 32700 | 0.0           | -               |
| 0.6342 | 32750 | 0.0           | -               |
| 0.6351 | 32800 | 0.0           | -               |
| 0.6361 | 32850 | 0.0           | -               |
| 0.6371 | 32900 | 0.0           | -               |
| 0.6380 | 32950 | 0.0           | -               |
| 0.6390 | 33000 | 0.0           | -               |
| 0.6400 | 33050 | 0.0           | -               |
| 0.6409 | 33100 | 0.0           | -               |
| 0.6419 | 33150 | 0.0           | -               |
| 0.6429 | 33200 | 0.0           | -               |
| 0.6438 | 33250 | 0.0           | -               |
| 0.6448 | 33300 | 0.0           | -               |
| 0.6458 | 33350 | 0.0           | -               |
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| 0.9992 | 51600 | 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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