Update README.md

README.md

@@ -27,27 +27,39 @@ Fine-tuned model in seven languages on texts from nine countries, based on [bert
 
 ## Model description
 
-tbs
+The PARTYPRESS multilingual model builds on [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) but has a supervised component. This means, it was fine-tuned using texts labeled by humans. The labels indicate 23 different political issue categories derived from the Comparative Agendas Project (CAP). 
+
 
 ## Model variations
 
-tbd (monolingual)
+We plan to release monolingual models for each of the languages covered by this multilingual model.
 
 ## Intended uses & limitations
 
-tbd
+The main use of the model is for text classification of press releases from political parties. It may also be useful for other political texts.
 
 ### How to use
 
-tbd
+This model can be used directly with a pipeline for text classification:
+
+```python
+>>> from transformers import pipeline
+>>> partypress = pipeline("text-classification", model = "cornelius/partypress-multilingual", tokenizer = "cornelius/partypress-multilingual")
+>>> partypress("We urgently need to fight climate change and reduce carbon emissions. This is what our party stands for.")
+
+```
 
 ### Limitations and bias
 
-tbd
+The model was trained with data from parties in nine countries. For use in other countries, the model may be further fine-tuned. Without further fine-tuning, the performance of the model may be lower.
+
+The model may have biased predictions. We discuss some biases by country, party, and over time in the release paper for the PARTYPRESS database.
 
 ## Training data
 
-For the training data, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
+The PARTYPRESS multilingual model was fine-tuned with 27,243 press releases in seven languages on texts from 68 European parties in nine countries. The press releases were labeled by two expert human coders per country.
+
+For the training data of the underlying model, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
 
 ## Training procedure
 
@@ -59,10 +71,16 @@ For the preprocessing, please refer to [bert-base-multilingual-cased](https://hu
 
 For the pretraining, please refer to [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)
 
+### Fine-tuning
+
+
 ## Evaluation results
 
-Fine-tuned on our downstream task, this model achieves the following results:
+Fine-tuned on our downstream task, this model achieves the following results in a five-fold cross validation:
 
+| Accuracy | Precision | Recall  | F1 score |
+|:--------:|:---------:|:-------:|:--------:|
+|    69.52 |   67.99   | 67.60   |   66.77  |
 
 ### BibTeX entry and citation info