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@@ -105,16 +105,16 @@ tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
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  Because this model uses language adapters, you need to specify the language of your input so that the correct adapter can be activated:
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  ```python
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- from transformers import XMODModel
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- model = XMODModel.from_pretrained("jvamvas/xmod-base")
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  model.set_default_language("en_XX")
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  ```
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  A directory of the language adapters in this model is found at the bottom of this model card.
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  ## Fine-tuning
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- The paper recommends that the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided in the code:
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  ```python
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  model.freeze_embeddings_and_language_adapters()
 
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  Because this model uses language adapters, you need to specify the language of your input so that the correct adapter can be activated:
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  ```python
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+ from transformers import XmodModel
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+ model = XmodModel.from_pretrained("jvamvas/xmod-base")
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  model.set_default_language("en_XX")
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  ```
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  A directory of the language adapters in this model is found at the bottom of this model card.
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  ## Fine-tuning
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+ In the experiments in the original paper, the embedding layer and the language adapters are frozen during fine-tuning. A method for doing this is provided in the code:
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  ```python
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  model.freeze_embeddings_and_language_adapters()