Update README.md

README.md

@@ -121,8 +121,8 @@ This model is built by [Nima Zahedinameghi](https://www.linkedin.com/in/nima-zah
 It achieves the following results on the evaluation set:
 - Loss: 0.5867, after 3 epochs.
 
-## Dependencies
-Please pip install all the required dependencies
+## How to use the model
+1. **Pip install the required dependencies**
 ``` txt
 transformers==4.36.2
 datasets==2.15.0
@@ -135,6 +135,46 @@ sentencepiece==0.1.99
 protobuf==4.23.4 --upgrade
 ```
 
+
+2. **Load the Model and Tokenizer:**
+   ```python
+   from peft import AutoPeftModelForCausalLM
+   from transformers import AutoTokenizer
+
+   model_id = 'NimaZahedinameghi/source_of_injury'
+   model = AutoPeftModelForCausalLM.from_pretrained(model_id).cuda()
+   tokenizer = AutoTokenizer.from_pretrained(model_id)
+   tokenizer.pad_token = tokenizer.eos_token
+   ```
+
+3. **Define the Prompt Function:**
+   Create a function to structure your prompt correctly:
+   ```python
+   def prompt(incident_description):
+       return f"""[INST] <<SYS>>
+       Workers Compensation Board of Manitoba manages claims by reviewing incident descriptions submitted by workers. Claim coders review the incident description and populate a database with reasoning towards determining the source of injury (InjurySource).
+       <</SYS>>
+
+       IncidentDescription: {incident_description}
+       [/INST]
+       """
+
+   def prompt_tok(incident_description):
+       _p = prompt(incident_description)
+       input_ids = tokenizer(_p, return_tensors="pt", truncation=True).input_ids.cuda()
+       out_ids = model.generate(input_ids=input_ids, max_new_tokens=500, do_sample=False)
+       return tokenizer.batch_decode(out_ids.detach().cpu().numpy(), skip_special_tokens=True)[0][len(_p):]
+   ```
+
+4. **Make Predictions:**
+   Use the function to get predictions from your model:
+   ```python
+   incident_description = "While working on a vehicle repair, I had to contort my body to access hard-to-reach areas. This position caused severe discomfort and pain in my neck and shoulders."
+   output = prompt_tok(incident_description)
+   print(output)
+   ```
+
+This function will take an incident description and return the reasoning and injury source as determined by your fine-tuned model. Ensure you follow the specific prompt format that matches your training setup.
 ## Model description
 
 the model is fine tuned on a small dataset with 4bit precision.