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--- |
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license: apache-2.0 |
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datasets: |
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- Alienmaster/SB10k |
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- cardiffnlp/tweet_sentiment_multilingual |
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- legacy-datasets/wikipedia |
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- community-datasets/gnad10 |
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language: |
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- de |
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base_model: dbmdz/bert-base-german-uncased |
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pipeline_tag: text-classification |
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--- |
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## Tweet Style Classifier (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 is a tweet or not. |
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The dataset contained about 20K 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 NVIDIA RTX A6000 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/tweet-style-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 | 0.99988 | |
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| Precision| 0.99901 | |
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| Recall | 0.99901 | |
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| F1 | 0.99901 | |
