Update README.md

README.md

@@ -16,8 +16,8 @@ In this library we have sequences and analyzers.
 
         + The level in a sequence is used like a path in a directory, to access the different subsequences in analyzers or filter funtions. In our example we have:
             - Text
-                -Phrases
-                    -Words 
+                - Phrases
+                    - Words 
 
         So, to access children of level Words we can use "Phrases/Words" in filter or analyze. As the same mode, we can use "Phrases/Words/text" to access a text(value of metadata dictionary) at the Words level in functions like filterMetadata or analyze.    
 
@@ -553,10 +553,12 @@ The steps to create an analyzer are:
 
     + For example, if we want to do a ironity analyzer, we use a pipeline that need a task, a model, a maximum of Embedding and if return all the scores of only the label with the maximum score of the text. The analyzer must be as flexible as possible, so all of the parameters that need the pipeline are passed like params at the init function.
 
-        + Look that inside the __init__ function we modified the labels of the model, this is because the model by defect dont have the labels clearly defined. (NI = Non-Ironic and I = Ironic)   
+        + Look that inside the init function we modified the labels of the model, this is because the model by defect dont have the labels clearly defined. (NI = Non-Ironic and I = Ironic)   
+
 
 
                 def __init__(self, task = "text-classification",modelIronity = 'dtomas/roberta-base-bne-irony', allScores = True, maxEmbedding = 514):
+                
                 """
                 Create an ironic analyzer.
     
@@ -566,6 +568,7 @@ The steps to create an analyzer are:
                     allScores: True, if we want that the classifier returns all scores. False, in other case.
                     maxEmbedding: The number of max_position_embeddings in the config.json of the model selected.
                 """
+                
                 if modelIronity == 'dtomas/roberta-base-bne-irony':
                     model = AutoModelForSequenceClassification.from_pretrained(modelIronity)
                     model.config.id2label = {0: 'NI', 1: 'I'}