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When I do this I get the following message indicating unused weights:
`Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']
This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).`
I do not get a similar error when using an earlier version of Transformers (i.e. 2.something).
The text was updated successfully, but these errors were encountered:
I am using the newest version of Transformers (i.e. version 3.0) and try to load a Scibert model using the following code
`scibert_tokenizer = AutoTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')
scibert_model = BertForMaskedLM.from_pretrained('allenai/scibert_scivocab_uncased')
`
When I do this I get the following message indicating unused weights:
`Some weights of the model checkpoint at allenai/scibert_scivocab_uncased were not used when initializing BertForMaskedLM: ['cls.seq_relationship.weight', 'cls.seq_relationship.bias']
This IS expected if you are initializing BertForMaskedLM 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 BertForMaskedLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).`
I do not get a similar error when using an earlier version of Transformers (i.e. 2.something).
The text was updated successfully, but these errors were encountered: