Update README.md

README.md

@@ -34,7 +34,7 @@ For both generated datasets, I used the Llama-3.2-1B-Instruct model, due to its
 as well as technical answers, which was necessary for the interview questions. I used the job postings dataset with few-shot prompting to create the user profile
 dataset. For each job posting in the dataset, I had the model create a 'great', 'mediocre', and 'bad' user profile. An example of the few shot prompting for this was:
 
-```python
+```
 Job_Title: Software Engineer
 Profile_Type: Great
 Applicant_Profile:
@@ -51,13 +51,14 @@ I used a python selector to randomly choose 5000 rows. With my new subset datase
 data. For each job posting/user profile I had the model create an interview question based on the job description, then an optimal answer to the question based on the 
 user profile. An example of a few shot prompt I used is below. 
 
-```python
+```
 Job Title: Data Scientist
 Job Description: Analyze data and build predictive models.
 Applicant Profile: Experienced in Python, R, and ML models.
 Interview Question: Tell me about a machine learning project you are proud of.
 Optimal Answer: I developed a predictive model using Python and scikit-learn to forecast customer churn, achieving 85% accuracy by carefully preprocessing the data and tuning hyperparameters.
 ```
+
 After creating this dataset, I uploaded it to my project notebook. Then, I modified the dataset to reformat it and make it easier to train. I created an 'Instruct' column with each row's job title,
 description, applicant profile, and the prompt 'Generate a relevant interview question and 
 provide an optimal answer using the information from this applicant's profile. Interview Question and Optimal Answer:'.  Then I combined the interview question/ optimal answer