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@@ -56,7 +56,7 @@ ROUGEL : 0.154
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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  You can use this model for text summarization.
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
@@ -96,10 +96,6 @@ The model was pre-trained continuously on a single A10G GPU in an AWS instance f
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  <br>Thus, hurts the performance of the Abstractive Summarization task.
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  <br>This case is not present in the decoder-only model as all the predicted next token is not seen by the model at all.
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- Note:
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- Could be used as an encoder-only model using BartForSequenceClasssification.
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  #### Authors:
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  <a href="https://www.linkedin.com/in/bijaya-bhatta-69536018a/">Vijaya Bhatta</a>
 
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  <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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  You can use this model for text summarization.
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+ <br>Could be used as an encoder-only model using BartForSequenceClasssification.
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  ## How to Get Started with the Model
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  Use the code below to get started with the model.
 
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  <br>Thus, hurts the performance of the Abstractive Summarization task.
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  <br>This case is not present in the decoder-only model as all the predicted next token is not seen by the model at all.
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  #### Authors:
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  <a href="https://www.linkedin.com/in/bijaya-bhatta-69536018a/">Vijaya Bhatta</a>