Update README.md

README.md

@@ -6,6 +6,59 @@ library_name: transformers
 pipeline_tag: text-classification
 ---
 
+## How to Get Started with the Model
+
+To use the model through Hosted inference API, follow the code snippet provided below:
+
+```python
+from transformers import BertTokenizer, BertForSequenceClassification
+
+def personality_detection(text):
+    tokenizer = BertTokenizer.from_pretrained("Minej/bert-base-personality")
+    model = BertForSequenceClassification.from_pretrained("Minej/bert-base-personality")
+
+    inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
+    outputs = model(**inputs)
+    predictions = outputs.logits.squeeze().detach().numpy()
+
+    label_names = ['Extroversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness']
+    result = {label_names[i]: predictions[i] for i in range(len(label_names))}
+
+    return result
+```
+
+#### Result Format
+
+The personality_detection function returns a dictionary containing the predicted personality traits based on the given input text.
+
+The dictionary contains the following personality traits with their corresponding predicted values:
+
+  Extroversion: A value between 0 and 1 representing the predicted extroversion trait.
+  Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait.
+  Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait.
+  Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait.
+  Openness: A value between 0 and 1 representing the predicted openness trait.
+
+```python
+text_input = "I am feeling excited about the upcoming event."
+personality_prediction = personality_detection(text_input)
+
+print(personality_prediction)
+```
+###### Output:
+```python
+{
+    "Extroversion": 0.535,
+    "Neuroticism": 0.576,
+    "Agreeableness": 0.399,
+    "Conscientiousness": 0.253,
+    "Openness": 0.563
+}
+```
+Note: The values in the example output are just placeholders and may not reflect the actual predictions.
+
+You can modify the example code and the result format to match your specific use case and desired output format.
+
 ### Model Description
 
 Transfer Learning for Big Five Personality Prediction
@@ -80,26 +133,7 @@ To mitigate risks and limitations associated with personality prediction models,
 
 It is important to note that the above recommendations are general guidelines, and further context-specific recommendations should be developed based on the particular use case and ethical considerations.
 
-## How to Get Started with the Model
-
-To use the model through Hosted inference API, follow the code snippet provided below:
-
-```python
-from transformers import BertTokenizer, BertForSequenceClassification
-
-def personality_detection(text):
-    tokenizer = BertTokenizer.from_pretrained("Minej/bert-base-personality")
-    model = BertForSequenceClassification.from_pretrained("Minej/bert-base-personality")
-
-    inputs = tokenizer(text, truncation=True, padding=True, return_tensors="pt")
-    outputs = model(**inputs)
-    predictions = outputs.logits.squeeze().detach().numpy()
-
-    label_names = ['Extroversion', 'Neuroticism', 'Agreeableness', 'Conscientiousness', 'Openness']
-    result = {label_names[i]: predictions[i] for i in range(len(label_names))}
-
-    return result
-```
+## How to Download the Model
 
 If you would like to download the model files and use them instead of the Hosted inference API, then you can follow the code snippet provided below:
 
@@ -174,38 +208,7 @@ You can call the personality_detection function with your input text to obtain t
 Please note that this code assumes you have already downloaded the necessary model files (config.json, pytorch_model.bin, special_tokens_map.json, tokenizer_config.json, vocab.txt
 ) and placed them in the current directory (indicated by "."). Adjust the paths and filenames accordingly if needed.
 
-#### Result Format
-
-The personality_detection function returns a dictionary containing the predicted personality traits based on the given input text.
-
-The dictionary contains the following personality traits with their corresponding predicted values:
-
-  Extroversion: A value between 0 and 1 representing the predicted extroversion trait.
-  Neuroticism: A value between 0 and 1 representing the predicted neuroticism trait.
-  Agreeableness: A value between 0 and 1 representing the predicted agreeableness trait.
-  Conscientiousness: A value between 0 and 1 representing the predicted conscientiousness trait.
-  Openness: A value between 0 and 1 representing the predicted openness trait.
-
-```python
-text_input = "I am feeling excited about the upcoming event."
-personality_prediction = personality_detection(text_input)
-
-print(personality_prediction)
-```
-###### Output:
-```python
-{
-    "Extroversion": 0.535,
-    "Neuroticism": 0.576,
-    "Agreeableness": 0.399,
-    "Conscientiousness": 0.253,
-    "Openness": 0.563
-}
-```
-
-Note: The values in the example output are just placeholders and may not reflect the actual predictions.
 
-You can modify the example code and the result format to match your specific use case and desired output format.
 
 ## Citation