SDG-classifier.md

@@ -11,9 +11,6 @@ _class_ SDGDetector.SDG_classifier(pretrained_model_path,pretrained_model_name,t
 |------------|-------------|------------|
 | `pretrained_model_name` | The name of the pre-trained model | {'RoBERTa','XLNet'} |
 | `pretrained_model_path` | The path of the fine-tuned model | str |
-| `top_keywords` | The number of the top keywords of texts | int |
-| `diversity` | The diversity (\[0,1\]) of the top produced keywords. A high diversity score will create very diverse keyphrases/keywords compared with low diversity, which will produce very similar keywords with the respective ones using the cosine similarity method: | float |
-| `n_gram_range` | The different lengths of keyphrases | tuple of shape (range_x,range_y) |
 | `sentence_model_name` | The name of the pre-trained sentence model | {'all-mpnet-base-v2','distilbert-base-nli-mean-tokens','all-MiniLM-L6-v2'} |
 
 ### Methods
@@ -36,6 +33,9 @@ The above formula has two branches, because the OSDG community dataset, which ha
 | Parameters | Description | Type |
 |------------|-------------|------|
 | `text` | Input texts | `list` |
+| `top_keywords` | The number of the top keywords of texts | `int` |
+| `diversity` | The diversity (\[0,1\]) of the top produced keywords. A high diversity score will create very diverse keyphrases/keywords compared with low diversity, which will produce very similar keywords with the respective ones using the cosine similarity method | `float` |
+| `n_gram_range` | The different lengths of keyphrases | tuple of shape (range_x,range_y)  |
 | `return_association` | If set to True then returns the association of each text with the SDGs | `Boolean` |
 
 Returns:
@@ -68,9 +68,9 @@ text = ['Europe has always been the home of industry. For centuries, it has been
 from SDGDetector import SDGDetector
 
 combo = SDGDetector.SDG_classifier(pretrained_model_name='XLNet',pretrained_model_path=<the path of downloaded model>,
-                                sentence_model_name='all-mpnet-base-v2',top_keywords=10,diversity=0.3,n_gram_range=(1,2))
+                                sentence_model_name='all-mpnet-base-v2')
 
-sdg,sdg_name,association = combo.predict(text,return_association=True)
+sdg,sdg_name,association = combo.predict(text,top_keywords=10,diversity=0.3,n_gram_range=(1,2),return_association=True)
 for i in range(len(text)):
     print('The most relevant SDG of the provided text {}: {}'.format(i+1,sdg_name[i]))
     print('======== The the association of the text with the SDGs: ========')