Update README.md

README.md

@@ -117,10 +117,19 @@ print(sentence_embeddings)
 
 ## Evaluation Results
 
-<!--- Describe how your model was evaluated -->
+#### Similarity Evaluation on STS.en-el.txt (translated manually for evaluation purposes)
+We measure the semantic textual similarity (STS) between sentence pairs in different languages:
 
-For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+| cosine_pearson | cosine_spearman | euclidean_pearson | euclidean_spearman | manhattan_pearson | manhattan_spearman | dot_pearson | dot_spearman |
+| ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- | ----------- |
+0.834474802920369 | 0.845687403828107 | 0.815895882192263 | 0.81084300966291 | 0.816333562677654 | 0.813879742416394 | 0.7945167996031 | 0.802604238383742 |
 
+#### Translation
+We measure the translation accuracy. Given a list with source sentences, for example, 1000 English sentences. And a list with matching target (translated) sentences, for example, 1000 Greek sentences. For each sentence pair, we check if their embeddings are the closest using cosine similarity. I.e., for each src_sentences[i] we check if trg_sentences[i] has the highest similarity out of all target sentences. If this is the case, we have a hit, otherwise an error. This evaluator reports accuracy (higher = better).
+
+| src2trg | trg2src |
+| ----------- | ----------- |
+| 0.981 |	0.9775 | 
 
 ## Training
 The model was trained with the parameters: