Precision and F-score related errors from sklearn during cross-validation

Hello all, I was trying to run an evaluation using cross-validation, but I’m getting the following errors during each fold. Has someone run into this before and how does one resolve it?

/opt/venv/lib/python3.7/site-packages/sklearn/metrics/ UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use zero_division parameter to control this behavior.

For additional information, I’ve got an NLU-only model and I’m only using it for intent classification (no entities yet). I’m using DIET in my config (see below) -

# Current pipeline configuration for the NLU
language: "en"

  - name: ConveRTTokenizer
    intent_tokenization_flag: true
    # Set to true to spot multiple intents
    case_sensitive: False
    intent_split_symbol: "+"
  - name: ConveRTFeaturizer
  - name: RegexFeaturizer
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
  - name: DIETClassifier
    epochs: 100
  - name: EntitySynonymMapper
  - name: ResponseSelector
    epochs: 100

# Configuration for Rasa Core.
  - name: MemoizationPolicy
  - name: TEDPolicy
  - name: MappingPolicy

Finally, this error appears before generating the confusion matrix. Would also appreciate some help here -

/opt/venv/lib/python3.7/site-packages/numpy/core/ VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray

Many thanks for your help!