Update README.md

README.md

@@ -1,12 +1,11 @@
 ---
 license: mit
 tags:
-- generated_from_trainer
-- natural-language-understanding
-- nlu
 - machine translation
 - iva
 - virtual assistants
+- natural-language-understanding
+- nlu
 metrics:
 - bleu
 model-index:
@@ -16,6 +15,7 @@ datasets:
 - cartesinus/iva_mt_wslot
 language:
 - pl
+- en
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -47,8 +47,8 @@ BLEU was measured with [sacreBLEU](https://github.com/mjpost/sacrebleu) library.
 
 ## Model description, intended uses & limitations
 
-Model is biased towards virtual assistant (IVA) sentences in prediction/translation. These sentences are short, most of them are short, imperatives. It can be observed in
-above results where WMT results are very low while in-domain test is very high.
+Model is biased towards virtual assistant (IVA) sentences in prediction/translation. These sentences are short, imperatives with a lot of name entities (slots) and 
+particular vocabulary (for example settings name). It can be observed in above results where WMT results are very low while in-domain test is very high.
 
 This model will most probably force IVA translations on your text. As long as sentences that you are translating are more or less similar to massive and leyzer domains it
 will be ok. If you will translate out-of-domain sentenences (such as for example News, Medical) that are not very similar then results will drop significantly up to the