Update README.md

README.md

@@ -13,7 +13,9 @@ This model fine-tunes google/t5-base to convert job descriptions into structured
 This model is based on the T5-base architecture fine-tuned on a dataset of 10,000 job description and resume pairs. It takes a job description as input and generates a JSON representation of a resume tailored to that job.
 
 **Base model:** google/t5-base
+
 **Fine-tuning task:** Text-to-JSON conversion
+
 **Training data:** 10,000 job description and resume pairs
 
 ## Intended uses & limitations
@@ -27,6 +29,7 @@ This model is based on the T5-base architecture fine-tuned on a dataset of 10,00
 - The model's output quality depends on the input job description's detail and clarity
 - Generated resumes may require human review and editing
 - The model may not capture nuanced or industry-specific requirements
+- The model is not tokenized to output "{" or "}", and instead uses "RB>" and "LB>" respectively
 
 ## Training data
 
@@ -36,9 +39,58 @@ The model was trained on 10,000 pairs of job descriptions and corresponding resu
 
 The model was fine-tuned using the standard T5 text-to-text framework. Specific hyperparameters and training details are not provided.
 
-## Evaluation results
-
-Evaluation metrics and results are not specified. Users are encouraged to evaluate the model's performance on their specific use cases.
+# How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+<details>
+<summary> Click to expand </summary>
+
+```python
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+def load_model_and_tokenizer(model_path):
+    """
+    Load the tokenizer and model from the specified path.
+    """
+    tokenizer = T5Tokenizer.from_pretrained("google-t5/t5-base")
+    model = T5ForConditionalGeneration.from_pretrained(model_path)
+    return tokenizer, model
+
+def generate_text(prompt, tokenizer, model):
+    """
+    Generate text using the model based on the given prompt.
+    """
+    # Encode the input prompt to get the tensor
+    input_ids = tokenizer(prompt, return_tensors="pt", padding=True).input_ids
+
+    # Generate the output using the model
+    outputs = model.generate(input_ids, max_length=512, num_return_sequences=1)
+
+    # Decode the output tensor to human-readable text
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
+
+def main():
+    model_path = "nakamoto-yama/t5-resume-generation"
+    print(f"Loading model and tokenizer from {model_path}")
+    tokenizer, model = load_model_and_tokenizer(model_path)
+    
+    # Test the model with a prompt
+    while True:
+        prompt = input("Enter a job description or title: ")
+        if prompt.lower() == 'exit':
+            break
+        response = generate_text(f"generate resume JSON for the following job: {prompt}", tokenizer, model)
+        response = response.replace("LB>", "{").replace("RB>", "}")
+        print(f"Generated Response: {response}")
+
+if __name__ == "__main__":
+    main()
+```
+
+See the [Hugging Face T5](https://huggingface.co/docs/transformers/model_doc/t5#transformers.T5Model) docs and a [Colab Notebook](https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/main/notebooks/t5-trivia.ipynb) created by the model developers for more examples.
+</details>
 
 ## Ethical considerations