BERT Error – Some weights of the model checkpoint at were not used when initializing BertModel occurs when you try to load a pre-trained BERT model and face an error related to mismatched weights.
Causes of the error
- Model mismatch
- Configuration mismatch
- Outdated or corrupted model files
- Library version issue
- Partial model loading
How to fix it?
Verify the pre-trained model checkpoint
Ensure you are using the correct pre-trained model checkpoint for the BERT model you want to use.
Import the correct BERT model class
Depending on the task you are working on, you might need a different BERT model class. For instance, you should use BertForSequenceClassification for text classification, BertForTokenClassification for named entity recognition, etc. Make sure you import and initialize the correct BERT model class.
from transformers import BertTokenizer, BertForSequenceClassification
# Load pre-trained model and tokenizer
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(model_name)
Check the weights
If you still encounter the error after verifying the pre-trained model checkpoint and importing the correct BERT model class, ensure the weights in the checkpoint match the model architecture.
You need to modify your model architecture to match the pre-trained model or fine-tune the model with your specific dataset to create a custom checkpoint.
Update the transformers library to its latest version
pip install --upgrade transformers
Double-check the configuration and ensure it matches the pre-trained model.
Double-checking the configuration to ensure it matches the pre-trained model is a crucial step when working with models from libraries like Hugging Face’s Transformers.