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--- |
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license: apache-2.0 |
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language: |
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- de |
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base_model: |
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- dbmdz/bert-base-german-uncased |
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pipeline_tag: text-classification |
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--- |
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## Social Media Style Classifier for Climate Change Text (German) |
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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. |
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Social media texts were gathered from [GerCCT](https://github.com/RobinSchaefer/GerCCT) and [r/Klimawandel](https://www.reddit.com/r/Klimawandel/). |
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Non-social media texts were gathered by tokenizing sentences from 15 Wikipedia articles: |
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1. [Klimawandel](https://de.wikipedia.org/wiki/Klimawandel), |
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2. [Globale Erwärmung](https://de.wikipedia.org/wiki/Globale_Erw%C3%A4rmung), |
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3. [Forschungsgeschichte des Klimawandels](https://de.wikipedia.org/wiki/Forschungsgeschichte_des_Klimawandels), |
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4. [Klimahysterie](https://de.wikipedia.org/wiki/Klimahysterie), |
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5. [Klimawandelleugnung](https://de.wikipedia.org/wiki/Klimawandelleugnung), |
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6. [Folgen der globalen Erwärmung in der Arktis](https://de.wikipedia.org/wiki/Folgen_der_globalen_Erw%C3%A4rmung_in_der_Arktis) |
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7. [Folgen der globalen Erwärmung](https://de.wikipedia.org/wiki/Folgen_der_globalen_Erw%C3%A4rmung) |
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8. [Klimamodell](https://de.wikipedia.org/wiki/Klimamodell) |
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9. [Anpassung an die globale Erwärmung](https://de.wikipedia.org/wiki/Anpassung_an_die_globale_Erw%C3%A4rmung) |
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10. [Kontroverse um die globale Erwärmung](https://de.wikipedia.org/wiki/Kontroverse_um_die_globale_Erw%C3%A4rmung) |
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11. [UN-Klimakonferenz in Dubai 2023](https://de.wikipedia.org/wiki/UN-Klimakonferenz_in_Dubai_2023) |
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12. [Umweltbewegung](https://de.wikipedia.org/wiki/Umweltbewegung#Klimaschutz) |
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13. [Treibhausgas](https://de.wikipedia.org/wiki/Treibhausgas) |
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14. [Treibhauseffekt](https://de.wikipedia.org/wiki/Treibhauseffekt) |
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15. [Klimaschutz](https://de.wikipedia.org/wiki/Klimaschutz) |
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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. |
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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. |
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The model was trained in order to evaluate a text style transfer task, converting formal-language texts to tweets. |
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### How to use |
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```python |
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from transformers import AutoModelForSequenceClassification, AutoTokenizer, TextClassificationPipeline |
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model_name = "rabuahmad/cc-tweets-classifier-de" |
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model = AutoModelForSequenceClassification.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name, max_len=512) |
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classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer, truncation=True, max_length=512) |
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text = "Gestern war ein schöner Tag!" |
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result = classifier(text) |
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``` |
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Label 1 indicates that the text is predicted to be a tweet. |
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### Evaluation |
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Evaluation results on the test set: |
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| Metric |Score | |
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|----------|-----------| |
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| Accuracy | 0.96494 | |
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| Precision| 0.97552 | |
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| Recall | 0.95564 | |
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| F1 | 0.96547 | |
