Update README.md

README.md

@@ -1,3 +1,52 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- Alienmaster/SB10k
+- cardiffnlp/tweet_sentiment_multilingual
+- legacy-datasets/wikipedia
+- community-datasets/gnad10
+language:
+- de
+base_model: dbmdz/bert-base-german-uncased
+pipeline_tag: text-classification
+---
+
+## Tweet Style Classifier (German)
+
+
+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. 
+
+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.
+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.
+
+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/tweet-style-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 | 0.99988   |
+| Precision| 0.99901   |
+| Recall   | 0.99901   |
+| F1       | 0.99901   |