CamembertForSequenceClassification not initialized from pretrained model
See original GitHub issueModel I am using CamembertForSequenceClassification for a relation classification task.
I’m trying to adapt the model following the implementation proposed by wang for this paper
When I try to load the pre trained model
loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/camembert-base-pytorch_model.bin
to fine-tune it on my task, I get the following INFO message. I find it weird because it seems that a lot of model’s params are not initialized (contrary to what I see for BERT model) :
04/28/2020 00:56:31 - INFO - transformers.modeling_utils - Weights of CamembertForSequenceClassification not initialized from pretrained model: ['latent_type', 'classifier.weight', 'classifier.bias', 'bert.embeddings.word_embeddings.weight', 'bert.embeddings.position_embeddings.weight', 'bert.embeddings.token_type_embeddings.weight', 'bert.embeddings.LayerNorm.weight', 'bert.embeddings.LayerNorm.bias', 'bert.encoder.layer.0.attention.self.query.weight', 'bert.encoder.layer.0.attention.self.query.bias', 'bert.encoder.layer.0.attention.self.key.weight', 'bert.encoder.layer.0.attention.self.key.bias', 'bert.encoder.layer.0.attention.self.value.weight', 'bert.encoder.layer.0.attention.self.value.bias', 'bert.encoder.layer.0.attention.output.dense.weight', 'bert.encoder.layer.0.attention.output.dense.bias', 'bert.encoder.layer.0.attention.output.LayerNorm.weight', 'bert.encoder.layer.0.attention.output.LayerNorm.bias', 'bert.encoder.layer.0.intermediate.dense.weight', 'bert.encoder.layer.0.intermediate.dense.bias', 'bert.encoder.layer.0.output.dense.weight', 'bert.encoder.layer.0.output.dense.bias', 'bert.encoder.layer.0.output.LayerNorm.weight', 'bert.encoder.layer.0.output.LayerNorm.bias', 'bert.encoder.layer.1.attention.self.query.weight', 'bert.encoder.layer.1.attention.self.query.bias', 'bert.encoder.layer.1.attention.self.key.weight', 'bert.encoder.layer.1.attention.self.key.bias', 'bert.encoder.layer.1.attention.self.value.weight', 'bert.encoder.layer.1.attention.self.value.bias', 'bert.encoder.layer.1.attention.output.dense.weight', 'bert.encoder.layer.1.attention.output.dense.bias', 'bert.encoder.layer.1.attention.output.LayerNorm.weight', 'bert.encoder.layer.1.attention.output.LayerNorm.bias', 'bert.encoder.layer.1.intermediate.dense.weight', 'bert.encoder.layer.1.intermediate.dense.bias', 'bert.encoder.layer.1.output.dense.weight', 'bert.encoder.layer.1.output.dense.bias', 'bert.encoder.layer.1.output.LayerNorm.weight', 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Any thoughts about that ? Thanks in advance
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- Created 3 years ago
- Comments:5 (2 by maintainers)

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That’s because your
CamembertForSequenceClassificationdoes not conform to our model, regarding naming.CamembertForSequenceClassificationinherits fromRobertaForSequenceClassificationand therefore the transformer model should be namedself.roberta, and notself.bert.This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.