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@@ -89,7 +89,6 @@ def insert_accents(text, model, tokenizer):
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  text = "Nhin nhung mua thu di, em nghe sau len trong nang."
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-
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  tokens, predictions = insert_accents(text, model, tokenizer)
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  ```
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@@ -171,7 +170,10 @@ Obtained:
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  [('Nhin', {217}), ('nhung', {388}), ('mua', {407}), ('thu', {378}), ('di,', {120, 0}), ('em', {185}), ('nghe', {185}), ('sau', {41}), ('len', {188}), ('trong', {302}), ('nang.', {0, 14})]
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  ```
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- Then our final part:
 
 
 
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  ```python
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  def get_accented_words(merged_tokens_preds, label_list):
@@ -211,7 +213,8 @@ In this example, the model made 1 mistake with the word "sầu" (but predicted "
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  More info can be found <a href="https://peterhung.org/tech/insert-vietnamese-accent-transformer-model/#vs-hmm" target="_blank">here</a>.
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  ## Live Demo
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  There is a live demo of this model available <a href="https://ai.vietnameseaccent.com/" target="_blank">here</a>.
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- This demo is run on CPU so the speed is quite slow.
 
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  text = "Nhin nhung mua thu di, em nghe sau len trong nang."
 
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  tokens, predictions = insert_accents(text, model, tokenizer)
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  ```
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  [('Nhin', {217}), ('nhung', {388}), ('mua', {407}), ('thu', {378}), ('di,', {120, 0}), ('em', {185}), ('nghe', {185}), ('sau', {41}), ('len', {188}), ('trong', {302}), ('nang.', {0, 14})]
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  ```
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+ For each word, we now have a set of tag indexes to apply to it.
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+ For ex, for the first word "Nhin" above, we'd apply the tag at index `217` in our tags set.
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+
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+ The following is our final part:
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  ```python
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  def get_accented_words(merged_tokens_preds, label_list):
 
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  More info can be found <a href="https://peterhung.org/tech/insert-vietnamese-accent-transformer-model/#vs-hmm" target="_blank">here</a>.
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+
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  ## Live Demo
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  There is a live demo of this model available <a href="https://ai.vietnameseaccent.com/" target="_blank">here</a>.
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+ This demo is run on CPU so the speed is expected to be not as fast as when run on GPU.