setfit safetensors xlm-roberta sentence-transformers text-classification generated_from_setfit_trainer arxiv:2209.11055 base_model:T-Systems-onsite/cross-en-de-roberta-sentence-transformer base_model:finetune:T-Systems-onsite/cross-en-de-roberta-sentence-transformer model-index region:us
---
base_model: T-Systems-onsite/cross-en-de-roberta-sentence-transformer
library_name: setfit
metrics:
- accuracy
pipeline_tag: text-classification
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
widget:
- text: Aber ratzfatz waren sie ab; Klima-Kleber blockieren Bundesstraße in Dortmund
    Dortmund - Klimaaktivist der Gruppierung ›DLG› haben am Mittwoch den Straßenverkehr
    in der Dortmunder Innenstadt vorübergehend gestört. Am Morgen blockierten fünf
    Menschen einen Teil der zentralen zweispurigen B54 stadteinwärts, einige klebten
    sich fest.
- text: 'Es gebe aber auch Hoffnung für schnelle Veränderungen. Als FFF erstmals auf
    die Straße gingen, habe er erstmals das Gefühl gehabt, dass Unternehmer feststellten:
    ›Scheiße, da passiert gerade was.› Das habe einen ersten Bruch gegeben, in der
    Autobranche habe plötzlich jeder auf E-Mobilität gesetzt. Auch die gestiegenen
    Energiepreise seien ein Auslöser: ›Der Preis treibt die Unternehmen schneller
    in die nachhaltige Energieversorgung als jede andere Diskussion. Und zwar innerhalb
    von anderthalb bis zwei Jahren.›'
- text: Die ›DLG› ist eine verschworene Truppe - aber kein Geheimbund. Die Vorträge
    sind öffentlich. Jeder kann sich für Trainings anmelden, bei denen der Umgang
    mit Autofahrern geprobt wird. Nach eigenen Angaben finanzieren sich die radikalen
    Klimaschützer vor allem über Spenden. Mit Geld des ›Climate Emergency Fund›, einem
    US-Fond, bezahlen die radikalen Klimaschützer Werbung oder Weiterbildung.
- text: Die aktivist bezeichnen sich als ›DLG›. Sie fordern von Bundeswirtschaftsminister
    Robert Habeck Grüne, auf fossile Energie zu verzichten. Zudem verlangen sie eine
    Lebenserklärung der Rektorin der Leipziger Universität. Diese soll sich ›offiziell,
    öffentlich und gerichtet an Robert Habeck gegen den Bau und die Finanzierung neuer
    fossiler Infrastruktur aussprechen. Insbesondere gegen neue Ölbohrungen in der
    Nordsee sowie neue Flüssiggas-Terminals›, hieß es in einer Mitteilung der Gruppe
    am Donnerstag.
- text: Der Anlass für Faesers Aussagen ist ein Vorfall Anfang der Woche. Dabei war
    eine Radfahrerin in Berlin von einem Betonmischer überfahren worden. Ein Bergungsfahrzeug
    der Feuerwehr steckte in einem Stau, der offenbar von Klima-aktivist der ›DLG›
    ausgelöst oder verstärkt worden war. Die Feuerwehr befreite die lebensgefährlich
    verletzte Frau schließlich ohne das Spezialfahrzeug. An diesem Donnerstag wurde
    die Radfahrerin für hirntot erklärt. Unklar ist, ob ein schnelleres Eintreffen
    des Spezialfahrzeugs ihr hätte helfen können.
inference: true
model-index:
- name: SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: Unknown
      type: unknown
      split: test
    metrics:
    - type: accuracy
      value: 0.5616438356164384
      name: Accuracy
---

# SetFit with T-Systems-onsite/cross-en-de-roberta-sentence-transformer

This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer) 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:** [T-Systems-onsite/cross-en-de-roberta-sentence-transformer](https://huggingface.co/T-Systems-onsite/cross-en-de-roberta-sentence-transformer)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 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                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
|:-----------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| opposed    | <ul><li>'In jüngster Zeit häufen sich die Proteste der Gruppierungen "Fridays for Future" und "Letzte Generation", deren drastische Aktionen mehr Fragen als Antworten hinterlassen. Während sie mit emotional aufgeladenen Szenarien von Klimakatastrophen für Aufmerksamkeit sorgen, scheint ihr Vorgehen oft wenig sachlich fundiert zu sein. Anstatt konstruktiver Lösungen bieten ihre teils chaotischen Demonstrationen eher eine Plattform für mediale Schlagzeilen als für nachhaltigen Fortschritt in der Klimapolitik.'</li><li>'Die radikalen Klima-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation finanzieren ihre umstrittenen Aktionen durch Spenden und Unterstützung von internationalen Fonds, was Fragen zur Unabhängigkeit und den tatsächlichen Motiven dieser Bewegungen aufwirft. Während sie öffentlich für den Klimaschutz eintreten, bleibt unklar, wie viel Einfluss externe Geldgeber auf ihre Strategien und Entscheidungen haben.'</li><li>'In den letzten Jahren hat der Druck von Gruppen wie Fridays for Future und der Letzte Generation auf die öffentliche Meinung stetig zugenommen – oft ohne Rücksicht auf die Auswirkungen ihrer Methoden auf Alltagsleben und Wirtschaft. Während diese Aktivisten ihre Aktionen als nötige Maßnahmen für den Klimaschutz darstellen, bleibt unklar, inwiefern ihre radikalen Ansätze tatsächlich nachhaltige Lösungen oder bloße Aufmerksamkeitsstunts sind. Finanziert durch Spenden und Unterstützung aus dem Ausland wie dem ›Climate Emergency Fund‹, scheinen die Mittel oft mehr auf Kampagnen als auf konkrete Veränderungen gerichtet zu sein.\n\n**Disclaimer:** Dieser Beitrag reflektiert eine kritische Haltung und nicht unbedingt die Meinung des gesamten Lesepublikums.'</li></ul> |
| supportive | <ul><li>'Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation überschreiten mit blockierenden und teils störenden Aktionen oft die Grenze des Zumutbaren, doch sie zwingen dringend benötigte Debatten über Umweltpolitik auf die Agenda. Angesichts des stockenden Fortschritts beim Klimaschutz könnte ihr vehementes Auftreten jedoch der nötige Weckruf sein, um die Politik zu entschlossenem Handeln zu bewegen.'</li><li>'Inmitten wachsender Spannungen um die Klima-Agenda ziehen die Aktionen von Gruppen wie Fridays for Future und Letzte Generation sowohl Bewunderung als auch Kritik auf sich. Während ihre Methoden oft als disruptiv empfunden werden, lenken sie unbestreitbar den dringend benötigten Fokus auf die klimatischen Herausforderungen unserer Zeit.'</li><li>'Die jüngsten Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und Letzte Generation haben zweifellos die öffentliche Wahrnehmung verschärft und Unternehmer dazu gezwungen, den Kopf aus dem Sand zu ziehen. Es bleibt jedoch fraglich, ob diese Druckmittel langfristig zu nachhaltigen Veränderungen führen oder nur kurzfristige Reaktionen hervorrufen. Trotzdem ist unverkennbar, dass der Anstieg der Energiepreise einen entscheidenden Katalysator darstellt, der Unternehmen schneller in die nachhaltige Energieversorgung treibt – ein Zeichen dafür, dass auch ökonomische Faktoren eine bedeutende Rolle spielen.'</li></ul>                                                                                                                                                                                                                                                                                                                             |
| neutral    | <ul><li>'Klimaschutz-Aktivisten von Gruppen wie Fridays for Future und Letzte Generation haben erneut durch Protestaktionen auf die Dringlichkeit klimapolitischer Maßnahmen aufmerksam gemacht. Während Befürworter die Notwendigkeit solcher Aktionen zur Erreichung ehrgeiziger Klimaziele betonen, kritisieren Gegner die teils disruptiven Methoden und fordern einen Dialog innerhalb bestehender politischer Prozesse.'</li><li>'Bei Aktionen der Klima-Aktivismus-Gruppen wie Fridays for Future und der Letzten Generation haben am Wochenende in mehreren deutschen Städten zahlreiche Menschen für mehr Klimaschutz demonstriert. In Berlin versammelten sich laut Polizeiangaben rund 3000 Teilnehmer, während die Veranstalter von etwa 5000 Demonstrierenden sprachen. Die Gruppen fordern unter anderem eine schnellere Umsetzung von Maßnahmen zur Reduzierung der CO2-Emissionen.'</li><li>'Am Freitag haben in Baden-Württemberg mehrere Tausend Menschen bei Demonstrationen der Klimaschutzbewegung Fridays for Future (FFF) und der Letzten Generation für mehr Umweltschutz demonstriert. In Freiburg zählte die Polizei bis zu 5000 Teilnehmer, während FFF von etwa 8000 Beteiligten berichtete. Die Proteste standen unter dem Motto „People not Profit“ und richteten sich gegen wirtschaftliche Interessen zugunsten des Klimaschutzes.\n\nDie Demonstrationen spiegeln die wachsende Besorgnis junger Menschen über den Klimawandel wider, während sie auch Diskussionen über die Balance zwischen ökonomischen Entwicklungen und Umweltschutz anstoßen.'</li></ul>                                                                                                                                                                                                  |

## Evaluation

### Metrics
| Label   | Accuracy |
|:--------|:---------|
| **all** | 0.5616   |

## Uses

### Direct Use for Inference

First install the SetFit library:

```bash
pip install setfit
```

Then you can load this model and run inference.

```python
from setfit import SetFitModel

# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("cbpuschmann/klimacoder_protest_v0.1")
# Run inference
preds = model("Aber ratzfatz waren sie ab; Klima-Kleber blockieren Bundesstraße in Dortmund Dortmund - Klimaaktivist der Gruppierung ›DLG› haben am Mittwoch den Straßenverkehr in der Dortmunder Innenstadt vorübergehend gestört. Am Morgen blockierten fünf Menschen einen Teil der zentralen zweispurigen B54 stadteinwärts, einige klebten sich fest.")
```

<!--
### 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.*
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<!--
### 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   | 35  | 69.3880 | 934 |

| Label      | Training Sample Count |
|:-----------|:----------------------|
| neutral    | 364                   |
| opposed    | 398                   |
| supportive | 367                   |

### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (3, 3)
- 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      | 0.1907        | -               |
| 0.0009 | 50     | 0.2333        | -               |
| 0.0019 | 100    | 0.2279        | -               |
| 0.0028 | 150    | 0.2239        | -               |
| 0.0038 | 200    | 0.2268        | -               |
| 0.0047 | 250    | 0.2008        | -               |
| 0.0057 | 300    | 0.1987        | -               |
| 0.0066 | 350    | 0.1811        | -               |
| 0.0075 | 400    | 0.176         | -               |
| 0.0085 | 450    | 0.1651        | -               |
| 0.0094 | 500    | 0.149         | -               |
| 0.0104 | 550    | 0.1267        | -               |
| 0.0113 | 600    | 0.1133        | -               |
| 0.0122 | 650    | 0.0895        | -               |
| 0.0132 | 700    | 0.0827        | -               |
| 0.0141 | 750    | 0.0593        | -               |
| 0.0151 | 800    | 0.0472        | -               |
| 0.0160 | 850    | 0.0357        | -               |
| 0.0170 | 900    | 0.0255        | -               |
| 0.0179 | 950    | 0.0213        | -               |
| 0.0188 | 1000   | 0.0149        | -               |
| 0.0198 | 1050   | 0.0144        | -               |
| 0.0207 | 1100   | 0.0124        | -               |
| 0.0217 | 1150   | 0.0091        | -               |
| 0.0226 | 1200   | 0.0072        | -               |
| 0.0236 | 1250   | 0.0049        | -               |
| 0.0245 | 1300   | 0.0044        | -               |
| 0.0254 | 1350   | 0.003         | -               |
| 0.0264 | 1400   | 0.0036        | -               |
| 0.0273 | 1450   | 0.0028        | -               |
| 0.0283 | 1500   | 0.0024        | -               |
| 0.0292 | 1550   | 0.0017        | -               |
| 0.0302 | 1600   | 0.0021        | -               |
| 0.0311 | 1650   | 0.0013        | -               |
| 0.0320 | 1700   | 0.0012        | -               |
| 0.0330 | 1750   | 0.0013        | -               |
| 0.0339 | 1800   | 0.001         | -               |
| 0.0349 | 1850   | 0.0007        | -               |
| 0.0358 | 1900   | 0.0006        | -               |
| 0.0367 | 1950   | 0.0016        | -               |
| 0.0377 | 2000   | 0.0007        | -               |
| 0.0386 | 2050   | 0.0006        | -               |
| 0.0396 | 2100   | 0.0005        | -               |
| 0.0405 | 2150   | 0.0005        | -               |
| 0.0415 | 2200   | 0.0003        | -               |
| 0.0424 | 2250   | 0.0004        | -               |
| 0.0433 | 2300   | 0.0003        | -               |
| 0.0443 | 2350   | 0.0003        | -               |
| 0.0452 | 2400   | 0.0002        | -               |
| 0.0462 | 2450   | 0.0002        | -               |
| 0.0471 | 2500   | 0.0002        | -               |
| 0.0481 | 2550   | 0.0002        | -               |
| 0.0490 | 2600   | 0.0002        | -               |
| 0.0499 | 2650   | 0.0003        | -               |
| 0.0509 | 2700   | 0.0002        | -               |
| 0.0518 | 2750   | 0.0002        | -               |
| 0.0528 | 2800   | 0.0002        | -               |
| 0.0537 | 2850   | 0.0001        | -               |
| 0.0546 | 2900   | 0.0001        | -               |
| 0.0556 | 2950   | 0.0001        | -               |
| 0.0565 | 3000   | 0.0001        | -               |
| 0.0575 | 3050   | 0.0001        | -               |
| 0.0584 | 3100   | 0.0001        | -               |
| 0.0594 | 3150   | 0.0001        | -               |
| 0.0603 | 3200   | 0.0001        | -               |
| 0.0612 | 3250   | 0.0001        | -               |
| 0.0622 | 3300   | 0.0001        | -               |
| 0.0631 | 3350   | 0.0001        | -               |
| 0.0641 | 3400   | 0.0001        | -               |
| 0.0650 | 3450   | 0.0001        | -               |
| 0.0660 | 3500   | 0.0001        | -               |
| 0.0669 | 3550   | 0.0005        | -               |
| 0.0678 | 3600   | 0.0018        | -               |
| 0.0688 | 3650   | 0.0026        | -               |
| 0.0697 | 3700   | 0.0001        | -               |
| 0.0707 | 3750   | 0.0001        | -               |
| 0.0716 | 3800   | 0.0001        | -               |
| 0.0726 | 3850   | 0.0001        | -               |
| 0.0735 | 3900   | 0.0001        | -               |
| 0.0744 | 3950   | 0.0           | -               |
| 0.0754 | 4000   | 0.0           | -               |
| 0.0763 | 4050   | 0.0           | -               |
| 0.0773 | 4100   | 0.0           | -               |
| 0.0782 | 4150   | 0.0           | -               |
| 0.0791 | 4200   | 0.0           | -               |
| 0.0801 | 4250   | 0.0           | -               |
| 0.0810 | 4300   | 0.0           | -               |
| 0.0820 | 4350   | 0.0           | -               |
| 0.0829 | 4400   | 0.0           | -               |
| 0.0839 | 4450   | 0.0           | -               |
| 0.0848 | 4500   | 0.0           | -               |
| 0.0857 | 4550   | 0.0           | -               |
| 0.0867 | 4600   | 0.0           | -               |
| 0.0876 | 4650   | 0.0           | -               |
| 0.0886 | 4700   | 0.0           | -               |
| 0.0895 | 4750   | 0.0           | -               |
| 0.0905 | 4800   | 0.0           | -               |
| 0.0914 | 4850   | 0.0           | -               |
| 0.0923 | 4900   | 0.0           | -               |
| 0.0933 | 4950   | 0.0           | -               |
| 0.0942 | 5000   | 0.0           | -               |
| 0.0952 | 5050   | 0.0           | -               |
| 0.0961 | 5100   | 0.0           | -               |
| 0.0970 | 5150   | 0.0           | -               |
| 0.0980 | 5200   | 0.0           | -               |
| 0.0989 | 5250   | 0.0           | -               |
| 0.0999 | 5300   | 0.0           | -               |
| 0.1008 | 5350   | 0.0           | -               |
| 0.1018 | 5400   | 0.0           | -               |
| 0.1027 | 5450   | 0.0           | -               |
| 0.1036 | 5500   | 0.0           | -               |
| 0.1046 | 5550   | 0.0           | -               |
| 0.1055 | 5600   | 0.0           | -               |
| 0.1065 | 5650   | 0.0           | -               |
| 0.1074 | 5700   | 0.0           | -               |
| 0.1084 | 5750   | 0.0           | -               |
| 0.1093 | 5800   | 0.0           | -               |
| 0.1102 | 5850   | 0.0           | -               |
| 0.1112 | 5900   | 0.0           | -               |
| 0.1121 | 5950   | 0.0           | -               |
| 0.1131 | 6000   | 0.0           | -               |
| 0.1140 | 6050   | 0.0           | -               |
| 0.1150 | 6100   | 0.0           | -               |
| 0.1159 | 6150   | 0.0           | -               |
| 0.1168 | 6200   | 0.0           | -               |
| 0.1178 | 6250   | 0.0           | -               |
| 0.1187 | 6300   | 0.0           | -               |
| 0.1197 | 6350   | 0.0           | -               |
| 0.1206 | 6400   | 0.0           | -               |
| 0.1215 | 6450   | 0.0           | -               |
| 0.1225 | 6500   | 0.0           | -               |
| 0.1234 | 6550   | 0.0           | -               |
| 0.1244 | 6600   | 0.0           | -               |
| 0.1253 | 6650   | 0.0           | -               |
| 0.1263 | 6700   | 0.0           | -               |
| 0.1272 | 6750   | 0.0           | -               |
| 0.1281 | 6800   | 0.0           | -               |
| 0.1291 | 6850   | 0.0           | -               |
| 0.1300 | 6900   | 0.0           | -               |
| 0.1310 | 6950   | 0.0           | -               |
| 0.1319 | 7000   | 0.0           | -               |
| 0.1329 | 7050   | 0.0           | -               |
| 0.1338 | 7100   | 0.0           | -               |
| 0.1347 | 7150   | 0.0           | -               |
| 0.1357 | 7200   | 0.0           | -               |
| 0.1366 | 7250   | 0.0           | -               |
| 0.1376 | 7300   | 0.0           | -               |
| 0.1385 | 7350   | 0.0           | -               |
| 0.1394 | 7400   | 0.0           | -               |
| 0.1404 | 7450   | 0.0           | -               |
| 0.1413 | 7500   | 0.0           | -               |
| 0.1423 | 7550   | 0.0           | -               |
| 0.1432 | 7600   | 0.0           | -               |
| 0.1442 | 7650   | 0.0           | -               |
| 0.1451 | 7700   | 0.0           | -               |
| 0.1460 | 7750   | 0.0           | -               |
| 0.1470 | 7800   | 0.0           | -               |
| 0.1479 | 7850   | 0.1766        | -               |
| 0.1489 | 7900   | 0.0139        | -               |
| 0.1498 | 7950   | 0.0029        | -               |
| 0.1508 | 8000   | 0.0029        | -               |
| 0.1517 | 8050   | 0.0008        | -               |
| 0.1526 | 8100   | 0.0001        | -               |
| 0.1536 | 8150   | 0.0002        | -               |
| 0.1545 | 8200   | 0.0001        | -               |
| 0.1555 | 8250   | 0.0           | -               |
| 0.1564 | 8300   | 0.0           | -               |
| 0.1574 | 8350   | 0.0           | -               |
| 0.1583 | 8400   | 0.0           | -               |
| 0.1592 | 8450   | 0.0           | -               |
| 0.1602 | 8500   | 0.0           | -               |
| 0.1611 | 8550   | 0.0           | -               |
| 0.1621 | 8600   | 0.0           | -               |
| 0.1630 | 8650   | 0.0           | -               |
| 0.1639 | 8700   | 0.0           | -               |
| 0.1649 | 8750   | 0.0           | -               |
| 0.1658 | 8800   | 0.0           | -               |
| 0.1668 | 8850   | 0.0           | -               |
| 0.1677 | 8900   | 0.0           | -               |
| 0.1687 | 8950   | 0.0           | -               |
| 0.1696 | 9000   | 0.0           | -               |
| 0.1705 | 9050   | 0.0           | -               |
| 0.1715 | 9100   | 0.0           | -               |
| 0.1724 | 9150   | 0.0           | -               |
| 0.1734 | 9200   | 0.0           | -               |
| 0.1743 | 9250   | 0.0           | -               |
| 0.1753 | 9300   | 0.0           | -               |
| 0.1762 | 9350   | 0.0           | -               |
| 0.1771 | 9400   | 0.0           | -               |
| 0.1781 | 9450   | 0.0           | -               |
| 0.1790 | 9500   | 0.0           | -               |
| 0.1800 | 9550   | 0.0           | -               |
| 0.1809 | 9600   | 0.0           | -               |
| 0.1818 | 9650   | 0.0           | -               |
| 0.1828 | 9700   | 0.0           | -               |
| 0.1837 | 9750   | 0.0           | -               |
| 0.1847 | 9800   | 0.0           | -               |
| 0.1856 | 9850   | 0.0           | -               |
| 0.1866 | 9900   | 0.0           | -               |
| 0.1875 | 9950   | 0.0           | -               |
| 0.1884 | 10000  | 0.0           | -               |
| 0.1894 | 10050  | 0.0           | -               |
| 0.1903 | 10100  | 0.0           | -               |
| 0.1913 | 10150  | 0.0           | -               |
| 0.1922 | 10200  | 0.0           | -               |
| 0.1932 | 10250  | 0.0           | -               |
| 0.1941 | 10300  | 0.0           | -               |
| 0.1950 | 10350  | 0.0           | -               |
| 0.1960 | 10400  | 0.0           | -               |
| 0.1969 | 10450  | 0.0           | -               |
| 0.1979 | 10500  | 0.0           | -               |
| 0.1988 | 10550  | 0.0           | -               |
| 0.1998 | 10600  | 0.0           | -               |
| 0.2007 | 10650  | 0.0           | -               |
| 0.2016 | 10700  | 0.0           | -               |
| 0.2026 | 10750  | 0.0           | -               |
| 0.2035 | 10800  | 0.0           | -               |
| 0.2045 | 10850  | 0.0           | -               |
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| 0.2205 | 11700  | 0.0           | -               |
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| 0.2393 | 12700  | 0.0           | -               |
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| 0.2770 | 14700  | 0.0001        | -               |
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| 0.3006 | 15950  | 0.0           | -               |
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| 0.3411 | 18100  | 0.0           | -               |
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| 0.3684 | 19550  | 0.0001        | -               |
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| 0.3703 | 19650  | 0.0           | -               |
| 0.3712 | 19700  | 0.0           | -               |
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| 0.3816 | 20250  | 0.0           | -               |
| 0.3825 | 20300  | 0.0           | -               |
| 0.3835 | 20350  | 0.0           | -               |
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| 0.3920 | 20800  | 0.0           | -               |
| 0.3929 | 20850  | 0.0           | -               |
| 0.3938 | 20900  | 0.0           | -               |
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| 0.3957 | 21000  | 0.0           | -               |
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| 0.3976 | 21100  | 0.0           | -               |
| 0.3986 | 21150  | 0.0           | -               |
| 0.3995 | 21200  | 0.0           | -               |
| 0.4004 | 21250  | 0.0           | -               |
| 0.4014 | 21300  | 0.0           | -               |
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| 0.4033 | 21400  | 0.0           | -               |
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| 0.4070 | 21600  | 0.0           | -               |
| 0.4080 | 21650  | 0.0           | -               |
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| 0.4099 | 21750  | 0.0           | -               |
| 0.4108 | 21800  | 0.0           | -               |
| 0.4118 | 21850  | 0.0           | -               |
| 0.4127 | 21900  | 0.0           | -               |
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| 0.4353 | 23100  | 0.0           | -               |
| 0.4362 | 23150  | 0.0           | -               |
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| 0.4655 | 24700  | 0.0           | -               |
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| 0.4673 | 24800  | 0.0           | -               |
| 0.4683 | 24850  | 0.0           | -               |
| 0.4692 | 24900  | 0.0           | -               |
| 0.4702 | 24950  | 0.0           | -               |
| 0.4711 | 25000  | 0.0           | -               |
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| 0.4758 | 25250  | 0.0           | -               |
| 0.4768 | 25300  | 0.0           | -               |
| 0.4777 | 25350  | 0.0           | -               |
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| 0.4852 | 25750  | 0.0           | -               |
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| 0.4881 | 25900  | 0.0           | -               |
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| 0.4909 | 26050  | 0.0           | -               |
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| 0.4928 | 26150  | 0.0           | -               |
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| 0.4966 | 26350  | 0.0           | -               |
| 0.4975 | 26400  | 0.0           | -               |
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| 0.4994 | 26500  | 0.0           | -               |
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| 0.5088 | 27000  | 0.0           | -               |
| 0.5097 | 27050  | 0.0           | -               |
| 0.5107 | 27100  | 0.0           | -               |
| 0.5116 | 27150  | 0.0           | -               |
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| 0.5135 | 27250  | 0.0           | -               |
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| 0.5276 | 28000  | 0.0           | -               |
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| 0.5305 | 28150  | 0.0           | -               |
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| 0.5390 | 28600  | 0.0           | -               |
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| 0.5465 | 29000  | 0.0           | -               |
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| 0.5484 | 29100  | 0.0           | -               |
| 0.5493 | 29150  | 0.0           | -               |
| 0.5503 | 29200  | 0.0           | -               |
| 0.5512 | 29250  | 0.0           | -               |
| 0.5521 | 29300  | 0.0           | -               |
| 0.5531 | 29350  | 0.0           | -               |
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| 0.5550 | 29450  | 0.0           | -               |
| 0.5559 | 29500  | 0.0           | -               |
| 0.5569 | 29550  | 0.0           | -               |
| 0.5578 | 29600  | 0.0           | -               |
| 0.5587 | 29650  | 0.0           | -               |
| 0.5597 | 29700  | 0.0           | -               |
| 0.5606 | 29750  | 0.0           | -               |
| 0.5616 | 29800  | 0.0           | -               |
| 0.5625 | 29850  | 0.0           | -               |
| 0.5634 | 29900  | 0.0           | -               |
| 0.5644 | 29950  | 0.0           | -               |
| 0.5653 | 30000  | 0.0           | -               |
| 0.5663 | 30050  | 0.0           | -               |
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| 0.5682 | 30150  | 0.0           | -               |
| 0.5691 | 30200  | 0.0           | -               |
| 0.5700 | 30250  | 0.0           | -               |
| 0.5710 | 30300  | 0.0           | -               |
| 0.5719 | 30350  | 0.0           | -               |
| 0.5729 | 30400  | 0.0           | -               |
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| 0.9761 | 51800  | 0.0           | -               |
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| 1.0006 | 53100  | 0.0           | -               |
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| 1.0025 | 53200  | 0.0           | -               |
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| 1.0986 | 58300  | 0.0           | -               |
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| 1.1495 | 61000  | 0.0           | -               |
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| 1.1646 | 61800  | 0.0           | -               |
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| 1.1665 | 61900  | 0.0           | -               |
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| 1.1834 | 62800  | 0.0           | -               |
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| 1.1900 | 63150  | 0.0           | -               |
| 1.1910 | 63200  | 0.0           | -               |
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| 1.1929 | 63300  | 0.0           | -               |
| 1.1938 | 63350  | 0.0           | -               |
| 1.1947 | 63400  | 0.0           | -               |
| 1.1957 | 63450  | 0.0           | -               |
| 1.1966 | 63500  | 0.0           | -               |
| 1.1976 | 63550  | 0.0           | -               |
| 1.1985 | 63600  | 0.0           | -               |
| 1.1994 | 63650  | 0.0           | -               |
| 1.2004 | 63700  | 0.0           | -               |
| 1.2013 | 63750  | 0.0           | -               |
| 1.2023 | 63800  | 0.0           | -               |
| 1.2032 | 63850  | 0.0           | -               |
| 1.2042 | 63900  | 0.0           | -               |
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| 1.2060 | 64000  | 0.0           | -               |
| 1.2070 | 64050  | 0.0           | -               |
| 1.2079 | 64100  | 0.0           | -               |
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| 1.2305 | 65300  | 0.0           | -               |
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| 1.2343 | 65500  | 0.0           | -               |
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| 1.2362 | 65600  | 0.0           | -               |
| 1.2371 | 65650  | 0.0           | -               |
| 1.2381 | 65700  | 0.0           | -               |
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| 1.2400 | 65800  | 0.0           | -               |
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| 1.2437 | 66000  | 0.0           | -               |
| 1.2447 | 66050  | 0.0           | -               |
| 1.2456 | 66100  | 0.0           | -               |
| 1.2466 | 66150  | 0.0           | -               |
| 1.2475 | 66200  | 0.0           | -               |
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| 1.2494 | 66300  | 0.0           | -               |
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| 1.2550 | 66600  | 0.0           | -               |
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| 1.2626 | 67000  | 0.0           | -               |
| 1.2635 | 67050  | 0.0           | -               |
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| 1.2871 | 68300  | 0.0           | -               |
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| 1.3003 | 69000  | 0.0           | -               |
| 1.3012 | 69050  | 0.0           | -               |
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| 1.3059 | 69300  | 0.0           | -               |
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| 1.3191 | 70000  | 0.0           | -               |
| 1.3201 | 70050  | 0.0           | -               |
| 1.3210 | 70100  | 0.0           | -               |
| 1.3219 | 70150  | 0.0           | -               |
| 1.3229 | 70200  | 0.0           | -               |
| 1.3238 | 70250  | 0.0           | -               |
| 1.3248 | 70300  | 0.0           | -               |
| 1.3257 | 70350  | 0.0           | -               |
| 1.3266 | 70400  | 0.0           | -               |
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| 1.3342 | 70800  | 0.0           | -               |
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| 1.8844 | 100000 | 0.0           | -               |
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| 1.9787 | 105000 | 0.0           | -               |
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| 1.9806 | 105100 | 0.0           | -               |
| 1.9815 | 105150 | 0.0           | -               |
| 1.9824 | 105200 | 0.0           | -               |
| 1.9834 | 105250 | 0.0           | -               |
| 1.9843 | 105300 | 0.0           | -               |
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| 1.9975 | 106000 | 0.0           | -               |
| 1.9985 | 106050 | 0.0           | -               |
| 1.9994 | 106100 | 0.0           | -               |
| 2.0003 | 106150 | 0.0           | -               |
| 2.0013 | 106200 | 0.0           | -               |
| 2.0022 | 106250 | 0.0           | -               |
| 2.0032 | 106300 | 0.0           | -               |
| 2.0041 | 106350 | 0.0           | -               |
| 2.0051 | 106400 | 0.0           | -               |
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| 2.0164 | 107000 | 0.0           | -               |
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| 2.0192 | 107150 | 0.0           | -               |
| 2.0201 | 107200 | 0.0           | -               |
| 2.0211 | 107250 | 0.0           | -               |
| 2.0220 | 107300 | 0.0           | -               |
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| 2.0352 | 108000 | 0.0           | -               |
| 2.0361 | 108050 | 0.0           | -               |
| 2.0371 | 108100 | 0.0           | -               |
| 2.0380 | 108150 | 0.0           | -               |
| 2.0390 | 108200 | 0.0           | -               |
| 2.0399 | 108250 | 0.0           | -               |
| 2.0409 | 108300 | 0.0           | -               |
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| 2.0569 | 109150 | 0.0           | -               |
| 2.0578 | 109200 | 0.0           | -               |
| 2.0588 | 109250 | 0.0           | -               |
| 2.0597 | 109300 | 0.0           | -               |
| 2.0606 | 109350 | 0.0           | -               |
| 2.0616 | 109400 | 0.0           | -               |
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| 2.0635 | 109500 | 0.0           | -               |
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| 2.0654 | 109600 | 0.0           | -               |
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| 2.0691 | 109800 | 0.0           | -               |
| 2.0701 | 109850 | 0.0           | -               |
| 2.0710 | 109900 | 0.0           | -               |
| 2.0719 | 109950 | 0.0           | -               |
| 2.0729 | 110000 | 0.0           | -               |
| 2.0738 | 110050 | 0.0           | -               |
| 2.0748 | 110100 | 0.0           | -               |
| 2.0757 | 110150 | 0.0           | -               |
| 2.0767 | 110200 | 0.0           | -               |
| 2.0776 | 110250 | 0.0           | -               |
| 2.0785 | 110300 | 0.0           | -               |
| 2.0795 | 110350 | 0.0           | -               |
| 2.0804 | 110400 | 0.0           | -               |
| 2.0814 | 110450 | 0.0           | -               |
| 2.0823 | 110500 | 0.0           | -               |
| 2.0833 | 110550 | 0.0           | -               |
| 2.0842 | 110600 | 0.0           | -               |
| 2.0851 | 110650 | 0.0           | -               |
| 2.0861 | 110700 | 0.0           | -               |
| 2.0870 | 110750 | 0.0           | -               |
| 2.0880 | 110800 | 0.0           | -               |
| 2.0889 | 110850 | 0.0           | -               |
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| 2.8823 | 152950 | 0.0           | -               |
| 2.8832 | 153000 | 0.0           | -               |
| 2.8841 | 153050 | 0.0           | -               |
| 2.8851 | 153100 | 0.0           | -               |
| 2.8860 | 153150 | 0.0           | -               |
| 2.8870 | 153200 | 0.0           | -               |
| 2.8879 | 153250 | 0.0           | -               |
| 2.8889 | 153300 | 0.0           | -               |
| 2.8898 | 153350 | 0.0           | -               |
| 2.8907 | 153400 | 0.0           | -               |
| 2.8917 | 153450 | 0.0           | -               |
| 2.8926 | 153500 | 0.0           | -               |
| 2.8936 | 153550 | 0.0           | -               |
| 2.8945 | 153600 | 0.0           | -               |
| 2.8955 | 153650 | 0.0           | -               |
| 2.8964 | 153700 | 0.0           | -               |
| 2.8973 | 153750 | 0.0           | -               |
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| 2.8992 | 153850 | 0.0           | -               |
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| 2.9020 | 154000 | 0.0           | -               |
| 2.9030 | 154050 | 0.0           | -               |
| 2.9039 | 154100 | 0.0           | -               |
| 2.9049 | 154150 | 0.0           | -               |
| 2.9058 | 154200 | 0.0           | -               |
| 2.9068 | 154250 | 0.0           | -               |
| 2.9077 | 154300 | 0.0           | -               |
| 2.9086 | 154350 | 0.0           | -               |
| 2.9096 | 154400 | 0.0           | -               |
| 2.9105 | 154450 | 0.0           | -               |
| 2.9115 | 154500 | 0.0           | -               |
| 2.9124 | 154550 | 0.0           | -               |
| 2.9134 | 154600 | 0.0           | -               |
| 2.9143 | 154650 | 0.0           | -               |
| 2.9152 | 154700 | 0.0           | -               |
| 2.9162 | 154750 | 0.0           | -               |
| 2.9171 | 154800 | 0.0           | -               |
| 2.9181 | 154850 | 0.0           | -               |
| 2.9190 | 154900 | 0.0           | -               |
| 2.9199 | 154950 | 0.0           | -               |
| 2.9209 | 155000 | 0.0           | -               |
| 2.9218 | 155050 | 0.0           | -               |
| 2.9228 | 155100 | 0.0           | -               |
| 2.9237 | 155150 | 0.0           | -               |
| 2.9247 | 155200 | 0.0           | -               |
| 2.9256 | 155250 | 0.0           | -               |
| 2.9265 | 155300 | 0.0           | -               |
| 2.9275 | 155350 | 0.0           | -               |
| 2.9284 | 155400 | 0.0           | -               |
| 2.9294 | 155450 | 0.0           | -               |
| 2.9303 | 155500 | 0.0           | -               |
| 2.9313 | 155550 | 0.0           | -               |
| 2.9322 | 155600 | 0.0           | -               |
| 2.9331 | 155650 | 0.0           | -               |
| 2.9341 | 155700 | 0.0           | -               |
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| 2.9360 | 155800 | 0.0           | -               |
| 2.9369 | 155850 | 0.0           | -               |
| 2.9379 | 155900 | 0.0           | -               |
| 2.9388 | 155950 | 0.0           | -               |
| 2.9397 | 156000 | 0.0           | -               |
| 2.9407 | 156050 | 0.0           | -               |
| 2.9416 | 156100 | 0.0           | -               |
| 2.9426 | 156150 | 0.0           | -               |
| 2.9435 | 156200 | 0.0           | -               |
| 2.9444 | 156250 | 0.0           | -               |
| 2.9454 | 156300 | 0.0           | -               |
| 2.9463 | 156350 | 0.0           | -               |
| 2.9473 | 156400 | 0.0           | -               |
| 2.9482 | 156450 | 0.0           | -               |
| 2.9492 | 156500 | 0.0           | -               |
| 2.9501 | 156550 | 0.0           | -               |
| 2.9510 | 156600 | 0.0           | -               |
| 2.9520 | 156650 | 0.0           | -               |
| 2.9529 | 156700 | 0.0           | -               |
| 2.9539 | 156750 | 0.0           | -               |
| 2.9548 | 156800 | 0.0           | -               |
| 2.9558 | 156850 | 0.0           | -               |
| 2.9567 | 156900 | 0.0           | -               |
| 2.9576 | 156950 | 0.0           | -               |
| 2.9586 | 157000 | 0.0           | -               |
| 2.9595 | 157050 | 0.0           | -               |
| 2.9605 | 157100 | 0.0           | -               |
| 2.9614 | 157150 | 0.0           | -               |
| 2.9623 | 157200 | 0.0           | -               |
| 2.9633 | 157250 | 0.0           | -               |
| 2.9642 | 157300 | 0.0           | -               |
| 2.9652 | 157350 | 0.0           | -               |
| 2.9661 | 157400 | 0.0           | -               |
| 2.9671 | 157450 | 0.0           | -               |
| 2.9680 | 157500 | 0.0           | -               |
| 2.9689 | 157550 | 0.0           | -               |
| 2.9699 | 157600 | 0.0           | -               |
| 2.9708 | 157650 | 0.0           | -               |
| 2.9718 | 157700 | 0.0           | -               |
| 2.9727 | 157750 | 0.0           | -               |
| 2.9737 | 157800 | 0.0           | -               |
| 2.9746 | 157850 | 0.0           | -               |
| 2.9755 | 157900 | 0.0           | -               |
| 2.9765 | 157950 | 0.0           | -               |
| 2.9774 | 158000 | 0.0           | -               |
| 2.9784 | 158050 | 0.0           | -               |
| 2.9793 | 158100 | 0.0           | -               |
| 2.9803 | 158150 | 0.0           | -               |
| 2.9812 | 158200 | 0.0           | -               |
| 2.9821 | 158250 | 0.0           | -               |
| 2.9831 | 158300 | 0.0           | -               |
| 2.9840 | 158350 | 0.0           | -               |
| 2.9850 | 158400 | 0.0           | -               |
| 2.9859 | 158450 | 0.0           | -               |
| 2.9868 | 158500 | 0.0           | -               |
| 2.9878 | 158550 | 0.0           | -               |
| 2.9887 | 158600 | 0.0           | -               |
| 2.9897 | 158650 | 0.0           | -               |
| 2.9906 | 158700 | 0.0           | -               |
| 2.9916 | 158750 | 0.0           | -               |
| 2.9925 | 158800 | 0.0           | -               |
| 2.9934 | 158850 | 0.0           | -               |
| 2.9944 | 158900 | 0.0           | -               |
| 2.9953 | 158950 | 0.0           | -               |
| 2.9963 | 159000 | 0.0           | -               |
| 2.9972 | 159050 | 0.0           | -               |
| 2.9982 | 159100 | 0.0           | -               |
| 2.9991 | 159150 | 0.0           | -               |

### Framework Versions
- Python: 3.11.11
- SetFit: 1.1.1
- 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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