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@@ -7,6 +7,10 @@ metrics:
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  model-index:
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  - name: iva_mt_wslot-m2m100_418M-0.1.0
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  results: []
 
 
 
 
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -34,7 +38,7 @@ It achieves the following results:
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  - Bleu (plain text): 70.5597
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  - Bleu (with slots): 93.8200
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- Bleu was measured with (sacrebleu)[https://github.com/mjpost/sacrebleu] library.
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  ## Model description, intended uses & limitations
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@@ -50,7 +54,7 @@ between 3 and 5% of all cases. When WMT20 was translated it happened in % cases
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  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).
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  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.
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- 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
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  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.
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  ## How to use
@@ -80,9 +84,9 @@ or you can translate with slot annotations that will be restored in tgt language
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  print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "pl")) #translation: obudź mnie o <a>piątej rano<a> <b>w tym tygodniu<b>
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  ```
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  Limitations of translation with slot transfer:
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- 1) Annotated words must be placed between semi-xml tags like this "this is <a>example<a>"
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- 2) There is no closing tag for example "<\a>" in above example - this is done on purpose to ommit problems with backslash escape
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- 3) If sentence consists of more than one slot then simply use next alphabet letter. For example "this is <a>example<a> with more than <b>one<b> slot"
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  4) Please do not add space before first or last annotated word because this particular model was trained this way and it most probably will lower it's results
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@@ -135,4 +139,4 @@ The following hyperparameters were used during training:
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  - Transformers 4.26.1
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  - Pytorch 1.13.1+cu116
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  - Datasets 2.10.1
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- - Tokenizers 0.13.2
 
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  model-index:
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  - name: iva_mt_wslot-m2m100_418M-0.1.0
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  results: []
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+ datasets:
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+ - cartesinus/iva_mt_wslot
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+ language:
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+ - pl
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  ---
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  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 
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  - Bleu (plain text): 70.5597
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  - Bleu (with slots): 93.8200
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+ Bleu was measured with [sacrebleu](https://github.com/mjpost/sacrebleu) library.
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  ## Model description, intended uses & limitations
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  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).
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  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.
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+ 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
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  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.
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  ## How to use
 
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  print(translate("wake me up at <a>nine am<a> on <b>friday<b>", "pl")) #translation: obudź mnie o <a>piątej rano<a> <b>w tym tygodniu<b>
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  ```
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  Limitations of translation with slot transfer:
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+ 1) Annotated words must be placed between semi-xml tags like this "this is \<a\>example\<a\>"
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+ 2) There is no closing tag for example "\<\a\>" in above example - this is done on purpose to ommit problems with backslash escape
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+ 3) If sentence consists of more than one slot then simply use next alphabet letter. For example "this is \<a\>example\<a\> with more than \<b\>one\<b\> slot"
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  4) Please do not add space before first or last annotated word because this particular model was trained this way and it most probably will lower it's results
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  - Transformers 4.26.1
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  - Pytorch 1.13.1+cu116
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  - Datasets 2.10.1
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+ - Tokenizers 0.13.2