Update README.md

README.md

@@ -113,7 +113,7 @@ German, French, Italian and Romansh documents in the [Swissdox@LiRI database](ht
 
 This model was finetuned via unsupervised [SimCSE](http://dx.doi.org/10.18653/v1/2021.emnlp-main.552). The same sequence is passed to the encoder twice and the distance between the two resulting embeddings is minimized.  Because of the drop-out, it will be encoded at slightly different positions in the vector space.
 
-The fine-tuning script can be accessed [here](Link).
+The fine-tuning script can be accessed [here](https://github.com/jgrosjean-mathesis/swissbert-for-sentence-embeddings/tree/main).
 
 #### Training Hyperparameters
 
@@ -143,14 +143,14 @@ The two evaluation tasks make use of the [20 Minuten dataset](https://www.zora.u
 
 Embeddings are computed for the summary and content of each document. Subsequently, the embeddings are matched by minimizing cosine similarity scores betweend each summary and content embedding pair.
 
-The performance is measured via accuracy, i.e. the ratio of correct vs. incorrect matches.
+The performance is measured via accuracy, i.e. the ratio of correct vs. incorrect matches. The script can be found [here](https://github.com/jgrosjean-mathesis/swissbert-for-sentence-embeddings/tree/main).
 
 
 #### Evaluation via Text Classification
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 
-Articles with the topic tags "movies/tv series", "corona" and "football" (or related) are filtered from the corpus and split into training data (80%) and test data (20%). Subsequently, embeddings are set up for the train and test data. The test data is then classified using the training data via a k-nearest neighbor approach.
+Articles with the topic tags "movies/tv series", "corona" and "football" (or related) are filtered from the corpus and split into training data (80%) and test data (20%). Subsequently, embeddings are set up for the train and test data. The test data is then classified using the training data via a k-nearest neighbor approach. The script can be found [here](https://github.com/jgrosjean-mathesis/swissbert-for-sentence-embeddings/tree/main).
 
 Note: For French and Italian, the training data remains in German, while the test data comprises of translations. This provides insights in the model's abilities in cross-lingual transfer.