title: how to use XLMRoberta in fine-tuning ,
body: <p>There are two problems i met when i fine-tuning my code.
  And i was trying to use X_1 and X_2 to regress.
  There are different languages in the corpus.</p>
  <pre><code>HTTPError: 404 Client Error: Not Found for url: https://huggingface.co/xlm-roberta-base/resolve/main/tf_model.h5
  
  During handling of the above exception, another exception occurred:
  
  OSError                                   Traceback (most recent call last)
  /tmp/ipykernel_33/2123064688.py in &lt;module&gt;
       55 # )
       56 
  ---&gt; 57 model = TFXLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-base',num_labels=1)
  
  OSError: Can't load weights for 'xlm-roberta-base'. Make sure that:
  
  - 'xlm-roberta-base' is a correct model identifier listed on 'https://huggingface.co/models'
    (make sure 'xlm-roberta-base' is not a path to a local directory with something else, in that case)
  
  - or 'xlm-roberta-base' is the correct path to a directory containing a file named one of tf_model.h5, pytorch_model.bin.
  </code></pre>
  <p>This is my code:</p>
  <pre><code>tokenizer = XLMRobertaTokenizerFast.from_pretrained('xlm-roberta-base')
  
  train_encoding = tokenizer(X_train_1,X_train_2,truncation=True,padding=True)
  val_encoding = tokenizer(X_val_1,X_val_2,truncation=True,padding=True)
  
  train_dataset = tf.data.Dataset.from_tensor_slices(
      (dict(train_encoding),y_train)
  )
  val_dataset = tf.data.Dataset.from_tensor_slices(
      (dict(val_encoding),y_val)
  )
  
  model = TFXLMRobertaForSequenceClassification.from_pretrained('xlm-roberta-base',num_labels=1)
  </code></pre>