Update README.md

README.md

@@ -23,39 +23,42 @@ This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface
 
 It achieves the following results:
 1) On the test set (iva_mt):
-- Bleu (plain text): 39.1560
-- Bleu (with slots): 63.8767
-- Baseline m2m100-418M (plain text): TBD
+- BLEU (plain text): 39.1560
+- BLEU (with slots): 63.8767
+- BLEU for baseline m2m100-418M (plain text): TBD
 
 2) WMT20 (en2pl):
-- Bleu (lowercased, tags removed): 15.0863 (for reference WMT20 submission systems in en-pl direction had between 25 and 30 bleu)
-- Baseline m2m100-418M (plain text): TBD
+- BLEU (lowercased, tags removed): 15.0863 (for reference WMT20 submission systems in en-pl direction had between 25 and 30 bleu)
+- BLEU for baseline m2m100-418M (plain text): 20.2750
    
 4) On the evaluation set:
-- Bleu: 61.6249
+- BLEU: 61.6249
 
 3) On the training set (to see how it adjusted to train):
-- Bleu (plain text): 70.5597
-- Bleu (with slots): 93.8200
+- BLEU (plain text): 70.5597
+- BLEU (with slots): 93.8200
 
-Bleu was measured with [sacrebleu](https://github.com/mjpost/sacrebleu) library.
+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. Both of this facts
-can be observed in above results. WMT results are very low while in-domain test is very high. One thing that needs to be mentioned is that BLEU is not particulary good
-metric to evaluate IVA sentences due to their length and it should be evalued with other metrices (e.g. [GLEU](https://aclanthology.org/P15-2097.pdf)).
+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.
 
 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
-can work. If you will translate domains unseen in either of them results might drop significantly to the point where baseline m2m100-418M will be better than this model.
+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
+point where baseline m2m100-418M will be better than this model.
 
-This model will generate tags in output even if there is not tag in input sentence. Frequency of this depends on input text. When testing IVA utterances this occurs 
-between 3 and 5% of all cases. When WMT20 was translated it happened in % cases (input text was from News domain).
-This is not very severe and can be fixed easily in post-processing (something like `sed 's/<[a-z]>//g'` should be enough in most cases).
+This model will generate tags in output even if there is not tag in input sentence. Frequency of this depends on input text origin. When testing IVA utterances this occurs 
+between 3 and 5%. When WMT20 was translated it happened in 40% cases (input text was from News domain).
+This is not very severe problem and it can be fixed easily in post-processing (simple `sed 's/<[a-z]>//g'` should be enough in most cases).
 
-Translations with slots very often differ from same sentences when slots are removed. This is quite frequent and it happens between 30 and 50% of translated utterances.
-For example there will be a difference between "is it raining in barcelona" and "is it raining in \<a\>barcelona\<a\>". In second case model will more likely localize name of
-city to some Polish name (here Lublin, because such city was given in Massive train set). This might be useful if you want to generate more variants.
+Translations with slot annotation very often differ from same sentences when slots are removed. This is quite frequent and it happens between 30 and 50% of translated
+utterances. For example there will be a difference between "is it raining in barcelona" and "is it raining in \<a\>barcelona\<a\>". In second case model will more likely
+localize name of city to some Polish name (here Lublin, because such city was given in Massive train set). This might be useful if you want to generate more variants.
+
+One last thing that needs to be mentioned is that BLEU is not particulary good metric to evaluate IVA sentences due to their length and it should be evalued with other
+metrices (e.g. [GLEU](https://aclanthology.org/P15-2097.pdf)).
 
 ## How to use
 
@@ -120,7 +123,7 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-| Training Loss | Epoch | Step  | Validation Loss | Bleu    | Gen Len |
+| Training Loss | Epoch | Step  | Validation Loss | BLEU    | Gen Len |
 |:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
 | 0.0178        | 1.0   | 5091  | 0.0171          | 57.4439 | 21.1396 |
 | 0.013         | 2.0   | 10182 | 0.0159          | 58.886  | 21.2285 |