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@@ -124,8 +124,8 @@ Finally, we surround each entity with entity indicators (`<e>`, `</e>`).
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  ### Supported Tasks and Leaderboards
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- The dataset was developped for intermediate pre-training of language models and can be used on any downstream task.
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- In the paper it's effectiveness is proven on entity-centric tasks, such as NER.
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  ### Languages
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@@ -149,10 +149,12 @@ The dataset covers 93 languages in total, including English.
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  ### Data Fields
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  Each instance contains 3 fields:
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- - id: Unique ID of each sentence
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- - language: The language of choice for entity code-switching of the given sentence
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- - en_sentence: The original English sentence
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- - cs_sentence: The code-switched sentence
 
 
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  An example of what a data instance looks like:
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  ```
@@ -166,7 +168,7 @@ An example of what a data instance looks like:
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  ### Data Splits
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- There is a single data split for each language. You can randomly select a few examples to serve as validation set.
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  ### Limitations
 
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  ### Supported Tasks and Leaderboards
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+ The dataset was developped for intermediate pre-training of language models.
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+ In the paper we further fine-tune models on entity-centric downstream tasks, such as NER.
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  ### Languages
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  ### Data Fields
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  Each instance contains 3 fields:
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+ - `id`: Unique ID of each sentence
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+ - `language`: The language of choice for entity code-switching of the given sentence
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+ - `en_sentence`: The original English sentence
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+ - `cs_sentence`: The code-switched sentence
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+
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+ In the case of the English subset, the `cs_sentence` field does not exist.
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  An example of what a data instance looks like:
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  ```
 
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  ### Data Splits
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+ There is a single data split for each language. You can randomly select a few examples from each language to serve as validation set.
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  ### Limitations