Renamed to word_count_classifier

Author: LeonardPuettmannKern

classifiers/__init__.py

@@ -10,7 +10,7 @@
 from .lookup_lists import lookup_list
 
 from .reference_quality import (
-    text_length_classifier,
+    word_count_classifier,
     chunked_sentence_complexity,
 )
 
@@ -61,7 +61,7 @@
     workday_classifier,
     deberta_review_classifier, 
     bert_sentiment_german,
-    text_length_classifier,
+    word_count_classifier,
     chunked_sentence_complexity,
 ]:
     module_name = module.__name__.split(".")[-1]

classifiers/reference_quality/text_length_classifier/README.md renamed to classifiers/reference_quality/word_count_classifier/README.md

@@ -1,17 +1,16 @@
 from pydantic import BaseModel
-from textblob import TextBlob
 
 INPUT_EXAMPLE = {"text": "This is too short!"}
 
 
-class TextLengthClassifierModel(BaseModel):
+class WordCountClassifierModel(BaseModel):
     text: str
 
     class Config:
         schema_extra = {"example": INPUT_EXAMPLE}
 
 
-def text_length_classifier(req: TextLengthClassifierModel):
+def word_count_classifier(req: WordCountClassifierModel):
     """Checks the length of a string by counting the number of words in it"""
     words = req.text.split()
     length = len(words)

classifiers/reference_quality/text_length_classifier/__init__.py renamed to classifiers/reference_quality/word_count_classifier/__init__.py

@@ -1,5 +1,5 @@
 ```python
-def text_length_classifier(text: str) -> str:
+def word_count_classifier(text: str) -> str:
     """
     @param text: text to check the length of.
     @return: either 'short', 'medium' or 'long' depending on the counted words.
@@ -20,7 +20,7 @@ def text_length_classifier(text: str) -> str:
 def example_integration():
     texts = ["This is short.", "This is a text with medium length.", "This is a longer text with many more words. There is even a second sentence with extra words. Splendid, what a joyful day!"]
     for text in texts:
-        print(f"\"{text}\" is -> {text_length_classifier(text)}")
+        print(f"\"{text}\" is -> {word_count_classifier(text)}")
 
 example_integration()
 ```

classifiers/reference_quality/text_length_classifier/code_snippet_common.md renamed to classifiers/reference_quality/word_count_classifier/code_snippet_common.md

@@ -1,7 +1,7 @@
 ```python
 ATTRIBUTE: str = "text" # only text attributes
 
-def text_length_classifier(record):
+def word_count_classifier(record):
     words = record[ATTRIBUTE].text.split()
     length = len(words)
     if length < 5:

classifiers/reference_quality/text_length_classifier/code_snippet_refinery.md renamed to classifiers/reference_quality/word_count_classifier/code_snippet_refinery.md

@@ -1,11 +1,11 @@
 from util.configs import build_classifier_function_config
 from util.enums import State, RefineryDataType, SelectionType, BricksVariableType
-from . import text_length_classifier, INPUT_EXAMPLE
+from . import word_count_classifier, INPUT_EXAMPLE
 
 
 def get_config():
     return build_classifier_function_config(
-        function=text_length_classifier,
+        function=word_count_classifier,
         input_example=INPUT_EXAMPLE,
         issue_id=348,
         tabler_icon="RulerMeasure",
@@ -25,7 +25,7 @@ def get_config():
         },
         # bricks integrator information
         integrator_inputs={
-            "name": "text_length_classifier",
+            "name": "word_count_classifier",
             "refineryDataType": RefineryDataType.TEXT.value,
             "outputs": [
                 "short",