Warnings about some weights that were not initialized in Greek BERT
Hello,

in order to use the Greek BERT model, I use `AutoModel` class, and specifically

`greek_bert = AutoModel.from_pretrained("nlpaueb/bert-base-greek-uncased-v1")`

When I load the model with the above command I get this warning: 
```

Some weights of the model checkpoint at nlpaueb/bert-base-greek-uncased-v1 were not used when initializing BertModel: ['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']
- This IS expected if you are initializing BertModel 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 BertModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model). 
```

Why is this warning thrown?
It tells you that you are initializing a `BertModel` without the heads that were used for pre-training (namely next sentence prediction and masked language modeling). That's not a problem, as you probably don't need these weights for a downstream task of interest (such as question answering or sequence classification). <|||||>Thank you @NielsRogge for your quick reply,

Indeed, I don't need those weighs for my task, but how can you tell that these are the weights of the heads used in pre-training?<|||||>You can see it based on their names:

`['cls.predictions.transform.dense.bias', 'cls.predictions.decoder.weight', 'cls.seq_relationship.bias', 'cls.predictions.decoder.bias', 'cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.seq_relationship.weight']`

=> `cls.seq_relationship` refers to the linear layer used for next sentence prediction.
=> `cls.predictions` refers to the masked language modeling head. It consists of a `transform` layer followed by a `decoder` (which maps to the vocabulary). 

The author of Greek BERT probably used the `BertForPreTraining` model for pre-training the BERT model. You can see the definition of the heads [here](https://github.com/huggingface/transformers/blob/bd3b599c12cfcf5ef517c5ffe526afbdbaa92539/src/transformers/models/bert/modeling_bert.py#L1011).<|||||>I see. I saw the names too, but I wasn't sure that these weights correspond to these heads just by reading the names.

Thank you @NielsRogge, you have been very helpful.<|||||>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.

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