diff --git a/README.md b/README.md
index 5440dc1..468a872 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,19 @@
 # Hedonodon
-I'm using [vaderSentiment](https://pypi.org/project/vaderSentiment/) to calculate the compounds.
+## Prerequisites
+Install the dependencies with `python -m pip install -r requirements.txt`.
+Install SpaCys nlp model with `python -m spacy download en_core_web_lg`.
+If the automatic download of the twitter-roberta-base-sentiment model and tokenizer fail, go to the model pages on hugging face (see models section) and download the to the respective folder (cardiffnlp/twitter-roberta-base-sentiment)
 
-More Documentation coming soon!
\ No newline at end of file
+## Purpose
+Hedonodon fetched toots from fedihum.org and calculates the sentiments, sentiment mean and word frequencies of each day, and creates fancy diagrams from the data.
+
+## Motivation
+This tool was created to understand how sentiment analyses and nlp methods works, so it may lacks of proper use of models etc...
+
+## Models
+It uses "germansentiment"](https://huggingface.co/oliverguhr/german-sentiment-bert) for german toots, []"twitter-roberta-base-sentiment"](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) for
+english toots, and ["vaderSentiment"](https://pypi.org/project/vaderSentiment/)  for other languages.
+For the word counts I translate the toots to english with the GoogleTranslator from [deep_translater](https://pypi.org/project/deep-translator/) first and then use SpaCys nlp model ["en_core_web_lg"](https://spacy.io/models/en/) to calculate the word frequencies.
+
+## Weaknesses
+Since some moduls do not return sentiment compounds I have to use the nominal sentiment values (positive, neutral, negative) to calculate the mean of the day, which is statisticaly not okay (;-_-).
\ No newline at end of file