SpacyEntityExtractor is not extracting

I have been working with Rasa for months and i have an unexpected bug found. When rasa is trying to extract entities but the Spacy Extractor is not doing its job. I have another extractor in my config file.

Someone have an idea why Spacy is not extracting?

config.yml

# The config recipe.
# https://rasa.com/docs/rasa/model-configuration/
recipe: default.v1

# The assistant project unique identifier
# This default value must be replaced with a unique assistant name within your deployment
assistant_id: 20240104-111410-factorial-skunk

# Configuration for Rasa NLU.
# https://rasa.com/docs/rasa/nlu/components/
language: es
  
pipeline:
  - name: SpacyNLP
    model: "es_core_news_lg"
    case_sensitive: false
  - name: SpacyTokenizer
  - name: SpacyFeaturizer
    pooling: max
  - name: LexicalSyntacticFeaturizer
  - name: CountVectorsFeaturizer
  - name: CountVectorsFeaturizer
    analyzer: "char_wb"
    min_ngram: 1
    max_ngram: 4
    OOV_token: "_oov_"
  - name: RegexFeaturizer
  - name: LanguageModelFeaturizer
    model_weights: "dccuchile/bert-base-spanish-wwm-cased"
    model_name: "bert"
    cache_dir: null 
  - name: "SklearnIntentClassifier"
    # Specifies the list of regularization values to
    # cross-validate over for C-SVM.
    # This is used with the ``kernel`` hyperparameter in GridSearchCV.
    C: [1, 2, 5, 10, 20, 100]
    # Specifies the kernel to use with C-SVM.
    # This is used with the ``C`` hyperparameter in GridSearchCV.
    kernels: ["linear"]
    # Gamma parameter of the C-SVM.
    "gamma": [0.1]
    # We try to find a good number of cross folds to use during
    # intent training, this specifies the max number of folds.
    "max_cross_validation_folds": 5
    # Scoring function used for evaluating the hyper parameters.
    # This can be a name or a function.
    "scoring_function": "f1_weighted"
    intent_classification:: false
  - name: RegexEntityExtractor
    # text will be processed with case insensitive as default
    case_sensitive: False
    # use lookup tables to extract entities
    use_lookup_tables: True
    # use regexes to extract entities
    use_regexes: True
    # use match word boundaries for lookup table
    use_word_boundaries: True
  - name: EntitySynonymMapper
  - name: SpacyEntityExtractor
    dimensions: [
      "user_name",
      "product",
      "mood",
      "symptom",
      "allergy",
      "product_selected",
      "search_term",
      "vitamin",
      "bottle",
      "protein",
      "electrolytes",
      "omega",
      "coffee",
      "collagen",
      "cream",
      "body_oil",
      "candle",
      "DATE",
      "GPE",
      "want",
      "how",
      "can",
      "create",
      "acquire",
      "cost",
      "procedure",
      "order",
      "order_number",
      "buy_word",
      "shipping_word",
      "help_word",
      "done_word",
      "time_word",
      "status_word",
      "services_word",
      "what_do_you_know_about",
      "when_does_arrive",
      "how_long_does_it_take",
      "have_problem",
      "where_is",
      "order_no_info",
      "shipping_tracking_number",
      "track_the_order",
      "payment_methods",
      "invoice",
      "payment_captured",
      "promo",
      "promo_value",
    ]
  - name: DIETClassifier
    random_seed: 42
    number_of_transformer_layers: 4
    transformer_size: 256
    drop_rate: 0.2
    weight_sparsity: 0.7
    batch_size: [64, 256]
    epochs: 20
    intent_classification:: false
  - name: EntitySynonymMapper
  - name: CRFEntityExtractor
    features: [
      ["low", "title", "upper"],
      [
        "bias",
        "low",
        "title",
        "prefix5",
        "prefix2",
        "suffix5",
        "suffix3",
        "suffix2",
        "upper",
        "digit",
        "pos",
        "pos2",
        "pattern",
        "text_dense_features"
      ],
      ["low", "title", "upper"]
    ]
    max_iterations: 50
    L1_c: 0.1
    L2_c: 0.1
    featurizers: []
    embedding_dimension: 30
  - name: ResponseSelector
    epochs: 100
    constrain_similarities: true
    model_confidence: softmax


# Configuration for Rasa Core.
# https://rasa.com/docs/rasa/core/policies/
policies:
  - name: MemoizationPolicy
  - name: TEDPolicy
    max_history: 5
    epochs: 100
    constrain_similarities: true
    model_confidence: softmax
  - name: RulePolicy
    restrict_rules: False
    core_fallback_threshold: 0.3
    core_fallback_action_name: "action_default_fallback"
    enable_fallback_prediction: True

domain.yml

version: '3.1'
intents:
  - greet
  - goodbye
  - affirm
  - deny
  - mood_great
  - mood_unhappy
  - bot_challenge
  - supply_name:
      use_entities:
        - user_name
  - supply_email:
      use_entities:
        - email
  - final_user_scenarios:
      use_entities:
        - search_term
        - symptom
        - product
        - category
        - mood
        - allergy
        - vitamin
        - bottle
        - protein
        - electrolytes
        - omega
        - coffee
        - collagen
        - cream
        - body_oil
        - candle
        - catalog
        - help
  - type_product:
      use_entities:
        - product_selected
  - ask_how_to_order:
      use_entities:
        - want
        - how
        - can
        - create
        - acquire
        - cost
        - procedure
        - order
        - buy_word
        - services_word
  - ask_how_long_will_take_to_deliver_an_order:
      use_entities:
        - how_long_does_it_take
  - ask_payment_methods:
      use_entities:
        - payment_methods
  - info_about_order:
      use_entities:
        - buy_word
        - shipping_word
        - help_word
        - done_word
        - time_word
        - status_word
        - search_term
        - what_do_you_know_about
        - when_does_arrive
        - how_long_does_it_take
        - have_problem
        - where_is
        - order_no_info
  - search_order:
      use_entities:
        - order_number
  - no_info_about_shipment_tracking:
      use_entities:
        - order_number
        - track_the_order
        - shipping_tracking_number
  - ask_for_invoice:
      use_entities:
        - invoice
  - ask_in_how_many_time_payment_will_be_captured:
      use_entities:
        - payment_captured
  - ask_for_promotions:
      use_entities:
        promo
        promo_value


entities:
  - session_created
  - session_restarted
  - user_name
  - email
  - product
  - category
  - mood
  - list_products
  - total_of_products
  - product_selected_details
  - product_selected
  - allergy
  - symptom
  - search_term
  - vitamin
  - bottle
  - protein
  - electrolytes
  - omega
  - coffee
  - collagen
  - cream
  - body_oil
  - candle
  - want
  - how
  - can
  - create
  - acquire
  - cost
  - procedure
  - order
  - order_number
  - buy_word
  - shipping_word
  - help_word
  - done_word
  - time_word
  - status_word
  - services_word
  - what_do_you_know_about
  - shipping_tracking_number
  - track_the_order
  - payment_methods
  - invoice
  - payment_captured
  - catalog
  - promo
  - promo_value

slots:
  session_created:
    type: bool
    mappings:
    - type: from_entity
      entity: session_created
  session_restarted:
    type: bool
    mappings:
    - type: from_entity
      entity: session_restarted
  user_name:
    type: text
    mappings:
    - type: from_entity
      entity: user_name
  email:
    type: text
    mappings:
    - type: from_entity
      entity: email
  product:
    type: text
    mappings:
    - type: from_entity
      entity: product
      intent: final_user_scenarios
    - type: from_entity
      entity: vitamin
      intent: final_user_scenarios
    - type: from_entity
      entity: bottle
      intent: final_user_scenarios
    - type: from_entity
      entity: protein
      intent: final_user_scenarios
    - type: from_entity
      entity: electrolytes
      intent: final_user_scenarios
    - type: from_entity
      entity: omega
      intent: final_user_scenarios
    - type: from_entity
      entity: coffee
      intent: final_user_scenarios
    - type: from_entity
      entity: collagen
      intent: final_user_scenarios
    - type: from_entity
      entity: cream
      intent: final_user_scenarios
    - type: from_entity
      entity: body_oil
      intent: final_user_scenarios
    - type: from_entity
      entity: candle
      intent: final_user_scenarios
  category:
    type: text
    mappings:
    - type: from_entity
      entity: category
  mood:
    type: text
    mappings:
    - type: from_entity
      entity: mood
  allergy:
    type: text
    mappings:
    - type: from_entity
      entity: allergy
  list_products:
    type: list
    mappings:
    - type: from_entity
      entity: list_products
  total_of_products:
    type: float
    min_value: 0
    mappings:
    - type: from_entity
      entity: total_of_products
  product_selected:
    type: text
    mappings:
    - type: from_entity
      entity: product_selected
  product_selected_details:
    type: text
    mappings:
    - type: from_entity
      entity: product_selected_details
  order_number:
    type: text
    mappings:
    - type: from_entity
      entity: order_number
  promo_value:
    type: text
    mappings:
    - type: from_entity
      entity: promo_value
  client_agreed_information:
    type: bool
    mappings:
    - type: from_intent
      value: true
      intent: affirm
    - type: from_intent
      value: false
      intent: deny
  client_agreed_open_page:
    type: bool
    mappings:
    - type: from_intent
      value: true
      intent: affirm
    - type: from_intent
      value: false
      intent: deny


# Note: Only I share the entity and slots definition