add columns description

README.md

@@ -18,15 +18,15 @@ The source of the paraphrases are different parallel German / English text corpo
 The English texts were machine translated back into German. This is how the paraphrases were obtained.
 
 ## Columns description
-- `uuid`: xxx
-- `de`: xxx
-- `en_de`: xxx
-- `corpus`: xxx
-- `min_char_len`: xxx
-- `jaccard_similarity`: xxx
-- `de_token_count`: xxx
-- `en_de_token_count`: xxx
-- `cos_sim`: xxx
+- `uuid`: a uuid calculated with Python `uuid.uuid4()`
+- `de`: the original German texts from the corpus
+- `en_de`: the German texts translated back from English
+- `corpus`: the name of the corpus
+- `min_char_len`: the number of characters of the shortest text
+- `jaccard_similarity`: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences
+- `de_token_count`: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
+- `en_de_token_count`: number of tokens of the `de` text, tokenized with [deepset/gbert-large](https://huggingface.co/deepset/gbert-large)
+- `cos_sim`: the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) of both sentences measured with [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2)
 
 ## Load this dataset with Pandas
 If you want to download the csv file and then load it with Pandas you can do it like this:
@@ -48,6 +48,7 @@ df = pd.read_csv("train.csv")
 ## To-do
 - add column description
 - upload dataset
+- add jaccard calculation
 
 ## Back translation
 We have made the back translation from English to German with the help of [Fairseq](https://github.com/facebookresearch/fairseq).