 Unable to load weights .from_pretrained() for XLMRoberta Model
###Defined a class that inherits from RobertaPretrainedModel

This class is defined to get from_pretrained()

    class XLMRobertaPreTrainedModel(RobertaPreTrainedModel):
        """
        An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained
        models.
        """
    
        config_class = XLMRobertaConfig
        base_model_prefix = "xlm-roberta-base"
    
This class is defined to concat image and text embedding

    class XLMRobertaWithImageConcatenationMultiOutputClassifier(XLMRobertaPreTrainedModel):
    
            def __init__(self, config_bert, num_labels=11, num_another_labels=4):
                print ('indise')
                super(XLMRobertaWithImageConcatenationMultiOutputClassifier, self).__init__(config_bert)
                configuration = XLMRobertaConfig(config_bert)
                self.num_labels = num_labels
                self.num_another_labels = num_another_labels
                self.bert = XLMRobertaModel(config_bert)
    
                self.backbone = models.resnet50(pretrained = True)
    
                num_features = self.backbone.fc.in_features
    
                self.img_hidden_size = 512
                self.backbone.fc = torch.nn.Linear(num_features, self.img_hidden_size)
    
                self.dropout = torch.nn.Dropout(configuration.hidden_dropout_prob)        
                self.pre_classify_hidden = 512
    
                self.pre_classify_fc = torch.nn.ReLU(torch.nn.Linear(configuration.hidden_size + self.img_hidden_size, self.pre_classify_hidden))
                self.classifier = torch.nn.Linear(self.pre_classify_hidden, num_labels)
                self.another_classifier = torch.nn.Linear(self.pre_classify_hidden, num_another_labels)
    
                self.apply(self._init_weights)

Trying to call XLMRobertaWithImageConcatenationMultiOutputClassifier from pretrained

                model = XLMRobertaWithImageConcatenationMultiOutputClassifier.from_pretrained('xlm-roberta-base')

The above code gives me error that some weights are not loaded. It lists all the weight layers. Not sure where I am wrong. Any help would be very helpful.
Can you provide the error traceback!?<|||||>No error. But below thing clearly suggests that weights are not loaded

      Some weights of the model checkpoint at xlm-roberta-base were not used when initializing XLMRobertaWithImageConcatenationMultiOutputClassifier: ['roberta.encoder.layer.3.output.dense.bias', 'roberta.encoder.layer.10.attention.self.query.weight', 'roberta.embeddings.LayerNorm.bias', 'roberta.encoder.layer.11.intermediate.dense.bias', 'roberta.encoder.layer.7.attention.output.dense.weight', 'roberta.encoder.layer.6.attention.self.value.bias', 'roberta.encoder.layer.10.intermediate.dense.bias', 'roberta.encoder.layer.4.attention.self.value.bias', 'roberta.encoder.layer.9.attention.self.key.bias', 'roberta.encoder.layer.2.intermediate.dense.bias', 'roberta.encoder.layer.9.output.dense.bias', 'roberta.embeddings.LayerNorm.weight', 'roberta.encoder.layer.0.output.dense.weight', 'roberta.encoder.layer.4.attention.self.key.weight', 'roberta.encoder.layer.8.output.LayerNorm.bias', 'roberta.encoder.layer.10.output.dense.bias', 'roberta.encoder.layer.1.attention.self.key.weight', 'roberta.encoder.layer.4.attention.output.LayerNorm.weight', 'roberta.encoder.layer.10.output.dense.weight', 'roberta.encoder.layer.3.attention.self.value.weight', 'roberta.encoder.layer.3.attention.output.dense.weight', 'roberta.encoder.layer.4.attention.self.value.weight', 'roberta.encoder.layer.8.output.dense.bias', 'roberta.encoder.layer.3.intermediate.dense.weight', 'roberta.encoder.layer.2.attention.self.value.bias', 'roberta.encoder.layer.1.attention.self.query.weight', 'roberta.encoder.layer.2.attention.output.dense.bias', 'roberta.encoder.layer.5.intermediate.dense.bias', 'roberta.encoder.layer.4.attention.self.key.bias', 'roberta.encoder.layer.5.attention.self.value.bias', 'roberta.embeddings.token_type_embeddings.weight', 'roberta.encoder.layer.7.attention.self.query.weight', 'roberta.encoder.layer.2.attention.self.query.weight', 'roberta.encoder.layer.11.output.LayerNorm.bias', 'roberta.encoder.layer.11.attention.self.key.weight', 'roberta.encoder.layer.4.intermediate.dense.bias', 'roberta.encoder.layer.6.output.LayerNorm.bias', 'roberta.encoder.layer.7.attention.output.LayerNorm.weight', 'roberta.encoder.layer.6.attention.self.query.bias', 'roberta.encoder.layer.5.output.dense.bias', 'roberta.encoder.layer.9.attention.output.dense.weight', 'roberta.encoder.layer.8.intermediate.dense.weight', 'roberta.encoder.layer.1.output.dense.bias', 'roberta.encoder.layer.5.output.dense.weight', 'roberta.encoder.layer.8.attention.output.dense.weight', 'roberta.encoder.layer.10.output.LayerNorm.weight', 'roberta.encoder.layer.5.attention.output.dense.weight', 'roberta.encoder.layer.0.attention.self.query.weight', 'roberta.encoder.layer.4.output.LayerNorm.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.bias', 'roberta.encoder.layer.3.attention.output.LayerNorm.bias', 'roberta.encoder.layer.11.attention.output.dense.bias', 'roberta.encoder.layer.2.intermediate.dense.weight', 'roberta.encoder.layer.6.attention.output.LayerNorm.bias', 'roberta.encoder.layer.7.attention.self.query.bias', 'roberta.encoder.layer.7.intermediate.dense.weight', 'roberta.encoder.layer.4.attention.self.query.weight', 'roberta.encoder.layer.2.attention.output.dense.weight', 'roberta.encoder.layer.3.attention.output.dense.bias', 'roberta.encoder.layer.4.attention.self.query.bias', 'roberta.encoder.layer.3.attention.self.query.weight', 'roberta.encoder.layer.2.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.self.query.bias', 'roberta.encoder.layer.9.intermediate.dense.bias', 'roberta.encoder.layer.2.attention.output.LayerNorm.weight', 'roberta.encoder.layer.9.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.output.LayerNorm.weight', 'roberta.encoder.layer.11.attention.self.key.bias', 'roberta.encoder.layer.1.intermediate.dense.bias', 'roberta.encoder.layer.6.output.dense.bias', 'roberta.encoder.layer.4.output.dense.bias', 'roberta.pooler.dense.weight', 'roberta.encoder.layer.2.output.LayerNorm.weight', 'roberta.encoder.layer.5.intermediate.dense.weight', 'roberta.encoder.layer.7.output.dense.bias', 'roberta.encoder.layer.1.attention.output.dense.weight', 'roberta.encoder.layer.4.intermediate.dense.weight', 'roberta.encoder.layer.5.attention.self.query.bias', 'lm_head.dense.bias', 'roberta.encoder.layer.6.attention.self.key.weight', 'roberta.encoder.layer.10.attention.self.key.weight', 'roberta.encoder.layer.2.attention.self.query.bias', 'roberta.encoder.layer.11.attention.output.dense.weight', 'roberta.encoder.layer.1.intermediate.dense.weight', 'roberta.encoder.layer.7.attention.self.key.bias', 'roberta.encoder.layer.2.attention.self.key.bias', 'roberta.encoder.layer.4.attention.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.output.LayerNorm.weight', 'lm_head.decoder.weight', 'roberta.encoder.layer.7.output.dense.weight', 'roberta.encoder.layer.6.attention.output.dense.weight', 'roberta.encoder.layer.7.attention.output.dense.bias', 'roberta.encoder.layer.4.attention.output.dense.bias', 'roberta.encoder.layer.10.attention.output.LayerNorm.weight', 'roberta.encoder.layer.11.output.LayerNorm.weight', 'roberta.encoder.layer.1.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.dense.weight', 'roberta.encoder.layer.6.attention.self.value.weight', 'roberta.encoder.layer.4.output.LayerNorm.weight', 'roberta.encoder.layer.8.attention.output.dense.bias', 'roberta.encoder.layer.11.attention.self.query.weight', 'roberta.encoder.layer.11.attention.self.query.bias', 'roberta.encoder.layer.0.attention.self.value.bias', 'roberta.encoder.layer.0.attention.self.query.bias', 'roberta.encoder.layer.9.attention.self.key.weight', 'roberta.encoder.layer.8.attention.self.value.weight', 'roberta.encoder.layer.1.attention.self.query.bias', 'roberta.encoder.layer.10.attention.output.dense.bias', 'roberta.encoder.layer.0.output.dense.bias', 'roberta.encoder.layer.3.attention.self.query.bias', 'roberta.encoder.layer.0.attention.self.value.weight', 'roberta.encoder.layer.8.attention.self.key.bias', 'roberta.encoder.layer.3.output.dense.weight', 'roberta.encoder.layer.11.intermediate.dense.weight', 'roberta.encoder.layer.5.output.LayerNorm.weight', 'roberta.encoder.layer.0.attention.self.key.bias', 'roberta.encoder.layer.3.attention.self.key.weight', 'roberta.encoder.layer.3.output.LayerNorm.bias', 'lm_head.layer_norm.bias', 'lm_head.layer_norm.weight', 'roberta.encoder.layer.6.intermediate.dense.bias', 'roberta.encoder.layer.9.output.LayerNorm.bias', 'roberta.encoder.layer.1.output.LayerNorm.bias', 'roberta.encoder.layer.7.output.LayerNorm.bias', 'roberta.encoder.layer.9.intermediate.dense.weight', 'roberta.encoder.layer.4.output.dense.weight', 'roberta.encoder.layer.8.output.dense.weight', 'roberta.encoder.layer.11.attention.output.LayerNorm.bias', 'roberta.embeddings.word_embeddings.weight', 'roberta.encoder.layer.0.intermediate.dense.weight', 'roberta.encoder.layer.5.attention.self.query.weight', 'roberta.encoder.layer.11.output.dense.bias', 'roberta.encoder.layer.10.attention.self.value.weight', 'roberta.encoder.layer.6.attention.self.query.weight', 'roberta.encoder.layer.8.intermediate.dense.bias', 'roberta.encoder.layer.11.output.dense.weight', 'roberta.encoder.layer.5.attention.output.LayerNorm.weight', 'roberta.encoder.layer.6.output.dense.weight', 'roberta.encoder.layer.2.attention.self.key.weight', 'roberta.encoder.layer.2.attention.self.value.weight', 'roberta.encoder.layer.3.attention.self.value.bias', 'roberta.encoder.layer.8.output.LayerNorm.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.weight', 'roberta.encoder.layer.7.attention.output.LayerNorm.bias', 'roberta.encoder.layer.6.intermediate.dense.weight', 'roberta.encoder.layer.9.output.dense.weight', 'roberta.encoder.layer.7.attention.self.value.bias', 'lm_head.bias', 'roberta.encoder.layer.3.intermediate.dense.bias', 'roberta.encoder.layer.5.output.LayerNorm.bias', 'roberta.encoder.layer.0.attention.output.dense.bias', 'roberta.encoder.layer.8.attention.output.LayerNorm.weight', 'roberta.encoder.layer.2.output.dense.bias', 'roberta.encoder.layer.6.attention.self.key.bias', 'roberta.encoder.layer.1.attention.self.key.bias', 'roberta.encoder.layer.0.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.self.value.weight', 'roberta.encoder.layer.0.attention.self.key.weight', 'roberta.encoder.layer.6.attention.output.dense.bias', 'roberta.pooler.dense.bias', 'roberta.encoder.layer.8.attention.self.key.weight', 'roberta.encoder.layer.5.attention.self.value.weight', 'roberta.encoder.layer.10.attention.self.key.bias', 'roberta.encoder.layer.10.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.output.LayerNorm.weight', 'roberta.encoder.layer.5.attention.output.LayerNorm.bias', 'roberta.encoder.layer.7.intermediate.dense.bias', 'roberta.encoder.layer.0.attention.output.LayerNorm.bias', 'roberta.encoder.layer.0.intermediate.dense.bias', 'roberta.encoder.layer.11.attention.self.value.bias', 'lm_head.dense.weight', 'roberta.encoder.layer.5.attention.self.key.bias', 'roberta.encoder.layer.5.attention.output.dense.bias', 'roberta.encoder.layer.6.attention.output.LayerNorm.weight', 'roberta.encoder.layer.10.attention.self.value.bias', 'roberta.encoder.layer.8.attention.self.value.bias', 'roberta.encoder.layer.8.attention.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.self.query.weight', 'roberta.encoder.layer.10.output.LayerNorm.bias', 'roberta.encoder.layer.9.attention.self.value.bias', 'roberta.encoder.layer.3.attention.self.key.bias', 'roberta.encoder.layer.7.output.LayerNorm.weight', 'roberta.encoder.layer.8.attention.self.query.weight', 'roberta.encoder.layer.8.attention.self.query.bias', 'roberta.encoder.layer.10.attention.self.query.bias', 'roberta.encoder.layer.5.attention.self.key.weight', 'roberta.encoder.layer.7.attention.self.key.weight', 'roberta.encoder.layer.10.intermediate.dense.weight', 'roberta.encoder.layer.9.attention.output.dense.bias', 'roberta.encoder.layer.11.attention.self.value.weight', 'roberta.encoder.layer.11.attention.output.LayerNorm.weight', 'roberta.encoder.layer.3.attention.output.LayerNorm.weight', 'roberta.encoder.layer.9.attention.self.value.weight', 'roberta.encoder.layer.1.attention.self.value.bias', 'roberta.encoder.layer.1.output.dense.weight', 'roberta.encoder.layer.7.attention.self.value.weight', 'roberta.encoder.layer.6.output.LayerNorm.weight', 'roberta.encoder.layer.3.output.LayerNorm.weight', 'roberta.encoder.layer.10.attention.output.dense.weight', 'roberta.encoder.layer.9.attention.output.LayerNorm.bias', 'roberta.encoder.layer.1.attention.output.dense.bias', 'roberta.encoder.layer.0.attention.output.dense.weight', 'roberta.encoder.layer.4.attention.output.dense.weight', 'roberta.encoder.layer.1.attention.output.LayerNorm.bias', 'roberta.embeddings.position_embeddings.weight']
      - This IS expected if you are initializing XLMRobertaWithImageConcatenationMultiOutputClassifier from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).
      - This IS NOT expected if you are initializing XLMRobertaWithImageConcatenationMultiOutputClassifier from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).
      Some weights of XLMRobertaWithImageConcatenationMultiOutputClassifier were not initialized from the model checkpoint at xlm-roberta-base and are newly initialized: ['backbone.layer2.0.downsample.1.weight', 'backbone.layer2.2.bn1.running_mean', 'backbone.layer4.1.bn3.num_batches_tracked', 'bert.encoder.layer.11.intermediate.dense.bias', 'backbone.layer2.3.bn3.weight', 'backbone.layer3.5.conv1.weight', 'bert.encoder.layer.10.output.LayerNorm.bias', 'bert.encoder.layer.0.output.LayerNorm.bias', 'bert.encoder.layer.10.intermediate.dense.weight', 'bert.encoder.layer.10.attention.self.key.bias', 'backbone.layer2.2.bn1.bias', 'backbone.layer2.1.conv2.weight', 'backbone.layer2.1.bn2.num_batches_tracked', 'bert.encoder.layer.2.attention.output.LayerNorm.weight', 'backbone.layer3.5.bn1.num_batches_tracked', 'backbone.layer2.2.bn2.num_batches_tracked', 'backbone.bn1.bias', 'bert.pooler.dense.bias', 'backbone.layer3.0.bn1.weight', 'another_classifier.bias', 'bert.encoder.layer.4.attention.self.key.bias', 'backbone.layer3.2.bn1.bias', 'bert.encoder.layer.7.output.LayerNorm.weight', 'backbone.layer4.0.bn3.num_batches_tracked', 'backbone.layer1.1.bn1.running_var', 'backbone.layer3.0.bn3.num_batches_tracked', 'backbone.layer4.0.conv1.weight', 'backbone.layer4.0.bn3.bias', 'bert.encoder.layer.9.attention.output.LayerNorm.bias', 'bert.encoder.layer.9.attention.self.value.weight', 'bert.encoder.layer.8.attention.output.LayerNorm.weight', 'bert.encoder.layer.0.intermediate.dense.weight', 'backbone.layer2.3.bn2.num_batches_tracked', 'bert.encoder.layer.8.attention.self.query.weight', 'backbone.layer1.1.bn3.running_mean', 'bert.encoder.layer.5.output.LayerNorm.weight', 'backbone.layer3.5.bn1.weight', 'backbone.layer2.1.conv1.weight', 'backbone.layer4.1.bn1.num_batches_tracked', 'bert.encoder.layer.7.attention.self.key.bias', 'backbone.layer3.3.bn1.bias', 'backbone.layer2.3.bn3.running_var', 'bert.encoder.layer.0.output.dense.weight', 'backbone.layer2.2.conv2.weight', 'backbone.layer3.4.bn3.running_mean', 'backbone.layer4.0.downsample.0.weight', 'bert.encoder.layer.9.output.LayerNorm.weight', 'backbone.layer3.0.bn1.bias', 'backbone.layer1.0.conv3.weight', 'backbone.layer1.1.bn2.bias', 'backbone.layer2.0.downsample.1.num_batches_tracked', 'backbone.layer2.2.bn3.num_batches_tracked', 'backbone.layer3.2.bn3.weight', 'backbone.layer1.0.bn2.running_mean', 'backbone.layer1.0.bn3.running_var', 'bert.encoder.layer.6.output.LayerNorm.bias', 'backbone.layer3.3.conv3.weight', 'backbone.layer4.1.bn2.weight', 'classifier.weight', 'classifier.bias', 'bert.encoder.layer.6.attention.self.key.weight', 'backbone.layer2.3.bn3.num_batches_tracked', 'bert.encoder.layer.4.attention.self.query.bias', 'bert.encoder.layer.11.output.dense.bias', 'backbone.layer1.2.bn1.running_var', 'backbone.layer2.3.bn1.running_var', 'bert.encoder.layer.3.attention.self.key.bias', 'bert.encoder.layer.3.attention.self.key.weight', 'backbone.layer3.0.conv3.weight', 'backbone.layer4.1.bn2.running_mean', 'backbone.layer3.1.conv2.weight', 'backbone.layer2.0.bn3.running_mean', 'backbone.layer3.0.downsample.1.running_var', 'bert.encoder.layer.0.attention.self.key.bias', 'bert.encoder.layer.1.attention.self.query.bias', 'backbone.layer4.1.bn2.running_var', 'backbone.layer4.2.bn1.running_var', 'bert.encoder.layer.10.output.dense.weight', 'bert.encoder.layer.3.output.dense.bias', 'bert.encoder.layer.0.attention.self.query.bias', 'backbone.layer4.2.bn3.bias', 'bert.encoder.layer.2.output.LayerNorm.weight', 'backbone.layer2.2.bn3.weight', 'backbone.layer2.3.bn3.bias', 'bert.encoder.layer.9.attention.output.dense.bias', 'backbone.layer3.0.bn3.running_mean', 'bert.encoder.layer.9.intermediate.dense.weight', 'backbone.layer1.0.bn1.num_batches_tracked', 'backbone.layer1.0.downsample.1.bias', 'backbone.layer1.1.bn2.running_var', 'backbone.layer3.5.bn3.num_batches_tracked', 'bert.encoder.layer.1.attention.output.dense.bias', 'backbone.layer3.0.bn1.num_batches_tracked', 'backbone.layer3.1.bn3.running_mean', 'backbone.layer3.4.bn1.weight', 'backbone.layer1.0.bn1.weight', 'backbone.bn1.running_var', 'backbone.layer4.1.bn1.weight', 'bert.embeddings.position_embeddings.weight', 'backbone.layer1.0.bn2.weight', 'backbone.layer3.1.bn1.num_batches_tracked', 'backbone.layer2.1.bn2.running_mean', 'backbone.bn1.num_batches_tracked', 'bert.encoder.layer.2.attention.self.query.bias', 'bert.encoder.layer.1.intermediate.dense.weight', 'backbone.layer3.2.conv1.weight', 'backbone.layer4.1.conv2.weight', 'bert.encoder.layer.7.attention.self.key.weight', 'bert.encoder.layer.1.attention.self.value.weight', 'bert.encoder.layer.8.attention.self.value.weight', 'backbone.layer3.0.conv1.weight', 'backbone.layer3.2.bn2.running_mean', 'bert.encoder.layer.1.output.LayerNorm.weight', 'bert.encoder.layer.4.attention.self.key.weight', 'bert.encoder.layer.2.attention.self.key.weight', 'backbone.layer1.2.bn2.running_mean', 'backbone.layer1.1.bn3.weight', 'backbone.layer1.1.bn2.weight', 'backbone.layer2.1.bn1.running_mean', 'bert.encoder.layer.4.intermediate.dense.bias', 'bert.encoder.layer.5.intermediate.dense.bias', 'backbone.layer3.3.conv2.weight', 'bert.encoder.layer.5.attention.output.dense.bias', 'backbone.layer2.3.bn1.bias', 'backbone.layer2.0.bn1.num_batches_tracked', 'bert.encoder.layer.11.attention.output.LayerNorm.bias', 'backbone.layer1.2.conv1.weight', 'bert.encoder.layer.2.attention.output.LayerNorm.bias', 'backbone.layer3.3.bn2.running_mean', 'bert.encoder.layer.3.attention.self.query.weight', 'backbone.layer3.2.bn2.bias', 'bert.encoder.layer.10.attention.self.value.weight', 'backbone.layer3.4.bn1.running_mean', 'bert.encoder.layer.10.attention.self.value.bias', 'backbone.layer3.5.bn2.running_var', 'bert.encoder.layer.5.attention.output.dense.weight', 'backbone.layer2.1.conv3.weight', 'bert.encoder.layer.11.attention.self.key.weight', 'backbone.layer1.0.bn2.bias', 'bert.encoder.layer.8.attention.output.dense.weight', 'backbone.layer2.0.bn2.running_mean', 'backbone.layer1.2.bn2.weight', 'bert.encoder.layer.1.attention.self.key.bias', 'backbone.layer2.0.downsample.1.bias', 'backbone.layer3.2.bn2.num_batches_tracked', 'backbone.layer3.5.bn3.bias', 'backbone.layer2.0.bn1.bias', 'backbone.layer1.2.bn2.bias', 'bert.encoder.layer.0.output.dense.bias', 'bert.encoder.layer.5.attention.output.LayerNorm.weight', 'backbone.layer3.0.bn2.running_var', 'backbone.layer1.0.bn3.running_mean', 'backbone.layer4.0.downsample.1.running_var', 'bert.encoder.layer.11.attention.self.value.bias', 'backbone.layer3.5.bn1.bias', 'bert.encoder.layer.2.output.dense.bias', 'backbone.layer4.0.downsample.1.weight', 'backbone.layer3.0.bn3.bias', 'bert.encoder.layer.7.attention.self.value.bias', 'backbone.layer4.0.bn1.running_mean', 'bert.encoder.layer.10.output.LayerNorm.weight', 'bert.encoder.layer.8.intermediate.dense.weight', 'backbone.layer2.0.bn3.bias', 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      You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.<|||||>The name scopes you define in your model should be the same as the pretrained model you're trying to load, so that these weights can be correctly loaded into variables.

For example, according to the warning you pasted, `xlm-roberta-base` uses the name `roberta` as its base model, while in your model it is `bert`, so `from_pretrained` method cannot correspond the names between them.<|||||>@qqaatw Yes, you are right that bert encoder layers are being utilized in `xlm-roberta-base.` That's what i am unable to understand why it is initializing with bert base when `XLMRoberta` is base model in class `XLMRobertaWithImageConcatenationMultiOutputClassifier`<|||||>The model behind `xlm-roberta-base` checkpoint is `RobertaForMaskedLM`, which uses the name `roberta` as its base model, you can regard `roberta` as an alias of `bert` in Roberta-related models.

Here is it's constructor :
```
def __init__(self, config):
        super().__init__(config)

        if config.is_decoder:
            logger.warning(
                "If you want to use `RobertaForMaskedLM` make sure `config.is_decoder=False` for "
                "bi-directional self-attention."
            )

        self.roberta = RobertaModel(config, add_pooling_layer=False)
        self.lm_head = RobertaLMHead(config)

        # The LM head weights require special treatment only when they are tied with the word embeddings
        self.update_keys_to_ignore(config, ["lm_head.decoder.weight"])

        self.init_weights()
```

And therefore, to load the weights from `xlm-roberta-base`, the names of your custom model's variables should be exactly the same as that of Roberta, I.e. the name `self.bert` should be changed to `self.reberta`, and so do other variables.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/master/CONTRIBUTING.md) are likely to be ignored.