The error happens because the model "nickwong64/bert-base-uncased-poems-sentiment" isn’t correctly registered as a SequenceClassification model in Hugging Face. You can try:

1. Use AutoModelForSequenceClassification explicitly:

    

    

   from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline model = AutoModelForSequenceClassification.from_pretrained( "nickwong64/bert-base-uncased-poems-sentiment", cache_dir="/Volumes/dsa_development/belgium_data/model_dir/hf_cache" ) tokenizer = AutoTokenizer.from_pretrained( "nickwong64/bert-base-uncased-poems-sentiment", cache_dir="/Volumes/dsa_development/belgium_data/model_dir/hf_cache" ) sentiment_classifier = pipeline( "text-classification", model=model, tokenizer=tokenizer )

   1. Check model card: Make sure the model actually supports "text-classification"/SequenceClassification. Some HF models are only trained as AutoModel and need a wrapper for classification.

   2. Environment path: Ensure Databricks can access the specified cache_dir and it’s mounted correctly.

      This approach explicitly loads the model and tokenizer and usually resolves the “Could not load model” issue in Databricks.

   3.