Update README.md

README.md

@@ -23,37 +23,45 @@ The model has demonstrated robust performance with an accuracy of 0.721459, F1 s
 ## Model Architecture
 The model leverages the BertForSequenceClassification architecture, It has been fine-tuned on the aforementioned dataset, with the following key configuration parameters:
 
-Hidden size: 768
-Number of attention heads: 12
-Number of hidden layers: 12
-Max position embeddings: 512
-Type vocab size: 2
-Vocab size: 30522
-The model uses the GELU activation function in its hidden layers and applies dropout with a probability of 0.1 to the attention probabilities to prevent overfitting.
+* Hidden size: 768
+* Number of attention heads: 12
+* Number of hidden layers: 12
+* Max position embeddings: 512
+* Type vocab size: 2
+* Vocab size: 30522
+* The model uses the GELU activation function in its hidden layers and applies dropout with a probability of 0.1 to the attention probabilities to prevent overfitting.
 
 ## Example 
 
 ```from transformers import AutoModelForSequenceClassification, TFAutoModelForSequenceClassification```
-```from transformers import AutoTokenizer
-```import numpy as np
-```from scipy.special import expit
+```from transformers import AutoTokenizer```
+```import numpy as np```
+```from scipy.special import expit```
 
     
-```MODEL = f"PavanDeepak/Topic_Classification"
-```tokenizer = AutoTokenizer.from_pretrained(MODEL)
+```MODEL = f"PavanDeepak/Topic_Classification"```
+```tokenizer = AutoTokenizer.from_pretrained(MODEL)```
 
-```model = AutoModelForSequenceClassification.from_pretrained(MODEL)
-```class_mapping = model.config.id2label
+```model = AutoModelForSequenceClassification.from_pretrained(MODEL)```
+```class_mapping = model.config.id2label```
 
-```text = "I love chicken manchuria"
-```tokens = tokenizer(text, return_tensors='pt')
-```output = model(**tokens)
+```text = "I love chicken manchuria"```
+```tokens = tokenizer(text, return_tensors='pt')```
+```output = model(**tokens)```
 
-```scores = output[0][0].detach().numpy()
-```scores = expit(scores)
-```predictions = (scores >= 0.5) * 1
+```scores = output[0][0].detach().numpy()```
+```scores = expit(scores)```
+```predictions = (scores >= 0.5) * 1```
 
 
-```for i in range(len(predictions)):
-  ```if predictions[i]:
-    ```print(class_mapping[i])```
+```for i in range(len(predictions)):```
+  ```if predictions[i]:```
+    ```print(class_mapping[i])```
+
+## Output:
+
+* Food
+* Videogames & Shows
+* Homestyle
+* Travel
+* Health