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---
license: apache-2.0
language:
- de
base_model:
- dbmdz/bert-base-german-uncased
pipeline_tag: text-classification
---
## Social Media Style Classifier for Climate Change Text (German)
This model is a fine-tuned bert-base-uncased on a binary classification task to determine whether a German text about Climate Change is written in a social media style.
Social media texts were gathered from [GerCCT](https://github.com/RobinSchaefer/GerCCT) and [r/Klimawandel](https://www.reddit.com/r/Klimawandel/).
Non-social media texts were gathered by tokenizing sentences from 15 Wikipedia articles:
1. [Klimawandel](https://de.wikipedia.org/wiki/Klimawandel),
2. [Globale Erwärmung](https://de.wikipedia.org/wiki/Globale_Erw%C3%A4rmung),
3. [Forschungsgeschichte des Klimawandels](https://de.wikipedia.org/wiki/Forschungsgeschichte_des_Klimawandels),
4. [Klimahysterie](https://de.wikipedia.org/wiki/Klimahysterie),
5. [Klimawandelleugnung](https://de.wikipedia.org/wiki/Klimawandelleugnung),
6. [Folgen der globalen Erwärmung in der Arktis](https://de.wikipedia.org/wiki/Folgen_der_globalen_Erw%C3%A4rmung_in_der_Arktis)
7. [Folgen der globalen Erwärmung](https://de.wikipedia.org/wiki/Folgen_der_globalen_Erw%C3%A4rmung)
8. [Klimamodell](https://de.wikipedia.org/wiki/Klimamodell)
9. [Anpassung an die globale Erwärmung](https://de.wikipedia.org/wiki/Anpassung_an_die_globale_Erw%C3%A4rmung)
10. [Kontroverse um die globale Erwärmung](https://de.wikipedia.org/wiki/Kontroverse_um_die_globale_Erw%C3%A4rmung)
11. [UN-Klimakonferenz in Dubai 2023](https://de.wikipedia.org/wiki/UN-Klimakonferenz_in_Dubai_2023)
12. [Umweltbewegung](https://de.wikipedia.org/wiki/Umweltbewegung#Klimaschutz)
13. [Treibhausgas](https://de.wikipedia.org/wiki/Treibhausgas)
14. [Treibhauseffekt](https://de.wikipedia.org/wiki/Treibhauseffekt)
15. [Klimaschutz](https://de.wikipedia.org/wiki/Klimaschutz)
The dataset contained about 8K instances, with a 50/50 distribution between the two classes. It was shuffled with a random seed of 42 and split into 80/20 for training/testing.
The V100-16GB GPU was used for training three epochs with a batch size of 8. Other hyperparameters were default values from the HuggingFace Trainer.
The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets.
### How to use
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline
model_name = "rabuahmad/cc-tweets-classifier-de"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, truncation=True, max_length=512)
text = "Gestern war ein schöner Tag!"
result = classifier(text)
```
Label 1 indicates that the text is predicted to be a tweet.
### Evaluation
Evaluation results on the test set:
| Metric |Score |
|----------|-----------|
| Accuracy | 0.96494 |
| Precision| 0.97552 |
| Recall | 0.95564 |
| F1 | 0.96547 | |