update readme for rev1 model

README.md

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+---
+license: mit
+language:
+- de
+pipeline_tag: text-classification
+metrics:
+- f1
+library_name: transformers
+---
+
+# PopBERT
+
+PopBERT is a model for German-language populism detection in political speeches within the German Bundestag, based on the deepset/gbert-large model: https://huggingface.co/deepset/gbert-large
+
+It is a multilabel model trained on a manually curated dataset of sentences from the 18th and 19th legislative periods.
+In addition to capturing the foundational dimensions of populism, namely "anti-elitism" and "people-centrism," the model was also fine-tuned to identify the underlying ideological orientation as either "left-wing" or "right-wing."
+
+# Prediction
+
+The model outputs a Tensor of length 4.
+The table connects the position of the predicted probability to its dimension.
+
+| **Index** | **Dimension**            |
+|-----------|--------------------------|
+| 0         | Anti-Elitism             |
+| 1         | People-Centrism          |
+| 2         | Left-Wing Host-Ideology  |
+| 3         | Right-Wing Host-Ideology |
+
+# Usage Example
+
+```python
+from transformers import AutoModel
+from transformers import AutoTokenizer
+
+# load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("luerhard/PopBERT")
+
+# load model
+model = AutoModel.from_pretrained("luerhard/PopBERT")
+
+# define text to be predicted
+text = (
+    "Das ist Klassenkampf von oben, das ist Klassenkampf im Interesse von "
+    "Vermögenden und Besitzenden gegen die Mehrheit der Steuerzahlerinnen und "
+    "Steuerzahler auf dieser Erde."
+)
+
+# encode text with tokenizer
+encodings = tokenizer(text)
+
+# predict
+with torch.inference_mode():
+    out = model(**encodings)
+
+# get probabilties
+probs = torch.nn.functional.sigmoid(out.logits)
+print(probs.detach().numpy())
+```
+
+```
+array([[0.87651485, 0.34838045, 0.983123  , 0.02148381]], dtype=float32)
+```
+
+
+# Performance
+
+To maximize performance, it is recommended to use the following thresholds per dimension:
+
+```
+[0.415961, 0.295400, 0.429109, 0.302714]
+```
+
+Using these thresholds, the model achieves the follwing performance on the test set:
+
+| Dimension           | Precision     | Recall        | F1            |
+|---------------------|---------------|---------------|---------------|
+| Anti-Elitism        | 0.81          | 0.88          | 0.84          |
+| People-Centrism     | 0.70          | 0.73          | 0.71          |
+| Left-Wing Ideology  | 0.69          | 0.77          | 0.73          |
+| Right-Wing Ideology | 0.68          | 0.66          | 0.67          |
+| ---                 | ---           | ---           | ---           |
+| micro avg           | 0.75          | 0.80          | 0.77          |
+| macro avg           | 0.72          | 0.76          | 0.74          |