Create README.md

README.md

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+# Usage
+
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+model_path = "KameronB/sitcc-t5-base-v3.0"
+
+# Load the model
+model = T5ForConditionalGeneration.from_pretrained(model_path, trust_remote_code=True, use_safetensors=True)
+
+# Load the tokenizer (if applicable)
+tokenizer = T5Tokenizer.from_pretrained(model_path)
+
+
+def summarize_ticket(ticket_text):
+  # Tokenize the input text
+  input_ids = tokenizer.encode("Summarize: " + ticket_text, return_tensors="pt")
+
+  # Generate the summary
+  summary_ids = model.generate(input_ids, min_length=10, max_length=100)
+
+  # Decode and return the summary
+  summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+  return summary
+
+```