Update README.md

README.md

@@ -1,3 +1,7 @@
+---
+tags:
+- intent, topic-discovery
+---
 # Model Card for Model ID
 
 Intent classification is the act of classifying customer's in to different pre defined categories. 
@@ -30,41 +34,41 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ## How to Get Started with the Model
 
-class IntentClassifier:
-    def __init__(self, model_name="serj/intent-classifier", device="cuda"):
-        self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
-        self.tokenizer = T5Tokenizer.from_pretrained(model_name)
-        self.device = device
+  class IntentClassifier:
+      def __init__(self, model_name="serj/intent-classifier", device="cuda"):
+          self.model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
+          self.tokenizer = T5Tokenizer.from_pretrained(model_name)
+          self.device = device
 
 
-def build_prompt(text, prompt="", company_name="", company_specific=""):
-    if company_name == "Pizza Mia":
-        company_specific = "This company is a pizzeria place."
-    if company_name == "Online Banking":
-        company_specific = "This company is an online banking."
+  def build_prompt(text, prompt="", company_name="", company_specific=""):
+      if company_name == "Pizza Mia":
+          company_specific = "This company is a pizzeria place."
+      if company_name == "Online Banking":
+          company_specific = "This company is an online banking."
+  
+      return f"Company name: {company_name} is doing: {company_specific}\nCustomer: {text}.\nEND MESSAGE\nChoose one topic that matches customer's issue.\n{prompt}\nClass name: "
 
-    return f"Company name: {company_name} is doing: {company_specific}\nCustomer: {text}.\nEND MESSAGE\nChoose one topic that matches customer's issue.\n{prompt}\nClass name: "
 
+  def predict(self, text, prompt_options, company_name, company_portion) -> str:
+      input_text = build_prompt(text, prompt_options, company_name, company_portion)
+      # print(input_text)
+      # Tokenize the concatenated inp_ut text
+      input_ids = self.tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True).to(self.device)
+  
+      # Generate the output
+      output = self.model.generate(input_ids)
+  
+      # Decode the output tokens
+      decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
+  
+      return decoded_output
 
-def predict(self, text, prompt_options, company_name, company_portion) -> str:
-    input_text = build_prompt(text, prompt_options, company_name, company_portion)
-    # print(input_text)
-    # Tokenize the concatenated inp_ut text
-    input_ids = self.tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True).to(self.device)
 
-    # Generate the output
-    output = self.model.generate(input_ids)
-
-    # Decode the output tokens
-    decoded_output = self.tokenizer.decode(output[0], skip_special_tokens=True)
-
-    return decoded_output
-
-
-m = IntentClassifier("serj/intent-classifier")
-print(m.predict("Hey, after recent changes, I want to cancel subscription, please help.",
-                "OPTIONS:\n refund\n cancel subscription\n damaged item\n return item\n", "Company",
-                "Products and subscriptions"))
+  m = IntentClassifier("serj/intent-classifier")
+  print(m.predict("Hey, after recent changes, I want to cancel subscription, please help.",
+                  "OPTIONS:\n refund\n cancel subscription\n damaged item\n return item\n", "Company",
+                  "Products and subscriptions"))
 
 
 [More Information Needed]
@@ -122,5 +126,4 @@ F1 AVG on the train set is 0.69
 
 #### Hardware
 
-Nvidia RTX3060 12Gb
-
+Nvidia RTX3060 12Gb