add jaccard calculation

README.md

@@ -23,7 +23,7 @@ The English texts were machine translated back into German. This is how the para
 - `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
+- `jaccard_similarity`: the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index) of both sentences - see below for more details
 - `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)
@@ -48,7 +48,6 @@ 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).
@@ -64,6 +63,33 @@ en2de = torch.hub.load(
 )
 ```
 
+## How the Jaccard similarity was calculated
+To calculate the [Jaccard similarity coefficient](https://en.wikipedia.org/wiki/Jaccard_index)
+we are using the [SoMaJo tokenizer](https://github.com/tsproisl/SoMaJo)
+to split the texts into tokens.
+We then `lower()` the tokens so that upper and lower case letters no longer make a difference. Below you can find a code snippet with the details:
+
+```python
+from somajo import SoMaJo
+
+LANGUAGE = "de_CMC"
+somajo_tokenizer = SoMaJo(LANGUAGE)
+
+def get_token_set(text, somajo_tokenizer):
+    sentences = somajo_tokenizer.tokenize_text([text])
+    tokens = [t.text.lower() for sentence in sentences for t in sentence]
+    token_set = set(tokens)
+    return token_set
+
+def jaccard_similarity(text1, text2, somajo_tokenizer):
+    token_set1 = get_token_set(text1, somajo_tokenizer=somajo_tokenizer)
+    token_set2 = get_token_set(text2, somajo_tokenizer=somajo_tokenizer)
+    intersection = token_set1.intersection(token_set2)
+    union = token_set1.union(token_set2)
+    jaccard_similarity = float(len(intersection)) / len(union)
+    return jaccard_similarity
+```
+
 ## Citations & Acknowledgements
 
 **OpenSubtitles**