Update README.md

README.md

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 ---
 license: mit
+language:
+- en
+tags:
+- text-2-text
+- natural-language
+- nlp
+- classification
+- call center
+- IT
+- summarization
+- text-generation
 ---
+# SITCC-T5-Classifier Model Card
+
+## Model Description
+The SITCC-T5-Classifier model is a fine-tuned version of the google/flan-t5-base model. It has been specifically trained to process IT ticket descriptions and extract the request/issue and the software/system that the ticket is about. The model was fine-tuned using 5716 synthetically generated input/output pairs generated with OpenAI GPT-4 Turbo.
+
+## Model Details
+- Base Model: google/flan-t5-base
+- Fine-tuning Data: 5716 synthetic IT ticket description pairs generated by OpenAI GPT-4 Turbo
+
+## Intended Use
+The SITCC-T5-Classifier model is designed to be used for IT ticket classification and information extraction tasks. It can be used to automatically identify the request/issue and the software/system mentioned in an IT ticket description.
+
+## Limitations and Known Issues
+- The model's performance may vary depending on the quality and diversity of the input IT ticket descriptions.
+- The model may struggle with understanding complex or ambiguous ticket descriptions.
+- The model may not perform well on ticket descriptions that are significantly different from the training data.
+
+## Example Usage
+This example is running on cpu
+``` python
+import re
+import pandas as pd
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+from time import perf_counter
+
+class SITCC_T5_Classifier:
+  """
+  A class for classifying text using the SITCC T5 model.
+
+  Attributes:
+  tokenizer (T5Tokenizer): The tokenizer for the T5 model.
+  model (T5ForConditionalGeneration): The T5 model for classification.
+  """
+
+  def __init__(self):
+    # Load the tokenizer and model from the fine-tuned model directory
+    self.tokenizer = T5Tokenizer.from_pretrained("KameronB/sitcc-t5-classifier")
+    self.model = T5ForConditionalGeneration.from_pretrained("KameronB/sitcc-t5-classifier", device_map="cpu")
+
+  def process_response(self, response:str) -> dict:
+    """
+    Process the response and extract the software/system and issue/request.
+
+    Args:
+    response (str): The response text.
+
+    Returns:
+    dict: A dictionary containing the software/system and issue/request.
+    """
+    matches = re.search(r'Software/System: (.*) Issue/Request: (.*)</s>', response, re.DOTALL)
+    return {
+      "Software/System": matches.group(1),
+      "Issue/Request": matches.group(2)
+    }
+  
+  def classify_entry(self, entry:str, max_new_tokens=60) -> dict:
+    """
+    Classify the input text and return the classification results.
+
+    Args:
+    entry (str): The input text to be classified.
+    max_new_tokens (int): The maximum number of tokens to generate.
+
+    Returns:
+    dict: The classification results.
+    """
+    # Tokenize the input text
+    input_ids = self.tokenizer(entry, return_tensors="pt").input_ids.to("cpu")
+
+    # Generate the output text
+    outputs = self.model.generate(input_ids, max_new_tokens=max_new_tokens)
+
+    # Decode and return the output text
+    return self.process_response(self.tokenizer.decode(outputs[0]))
+
+# Create the SITCC T5 Classifier wrapper class for the fine-tuned T5 model
+sitcc_t5 = SITCC_T5_Classifier()
+
+# Define the input text
+
+input_text = [
+  "The customer is getting the following error when using rSATS:\nERROR: 'Failed to connect'. \nI have tried restarting the application and the computer, but the issue persists. \nEscalating to Team",
+  "The customer is experiencing issues with their network connectivity, which is causing slow internet speeds and frequent disconnections.",
+  "The customer is unable to access the shared drive on the network. They receive an error message stating 'Network path not found'. \nEscalating to Network Team",
+  "The customer is unable to print from their computer. They have checked the printer connections and restarted the printer, but the issue persists. \nEscalating to Printer Support Team",
+  ]
+
+# measure the time performance of the model
+start = perf_counter()
+for i in range(len(input_text)):
+  # Classify the input text
+  print(sitcc_t5.classify_entry(input_text[i]))
+
+# measure the time performance of the model
+end = perf_counter()
+print(f"Time taken: {end - start} seconds")
+
+
+
+
+
+```