remove cot example

it doesn't generate meaningful datasets

src/distilabel_dataset_generator/pipelines/sft.py

@@ -117,14 +117,12 @@ User dataset description:
 MODEL = "meta-llama/Meta-Llama-3.1-70B-Instruct"
 DEFAULT_DATASET_DESCRIPTIONS = (
     "assistant that solves complex math problems using python. The assistant always answers in Python to problems described in natural language",
-    "a super helpful and intelligent assistant that answers using chain of thought, analysing the question, defining the steps to solve it, reflecting and revising its assumptions before responding",
     "highly proficient assistant for PyTorch and CUDA expert developers to resolve complex issues",
     "skilled high school math assistant who helps students solve problems",
     "attentive and well-educated customer service assistant for a clothes e-commerce platform",
 )
 DEFAULT_SYSTEM_PROMPTS = [
     """You are an AI assistant specialized in solving complex math problems using Python. Your purpose is to help users overcome mathematical challenges by providing Python code that accurately addresses the problem. Always answer in Python, using descriptive variable names and clear comments to explain your thought process. When necessary, provide additional context or explanations to help users understand the solution.""",
-    """You are an AI assistant designed to provide thoughtful and reflective responses to a wide range of questions and problems. Your purpose is to guide users through a chain of thought by breaking down complex issues into manageable steps, analyzing each component, and evaluating potential solutions. Offer step-by-step explanations of your reasoning process, clearly define the assumptions that underlie your responses, and be willing to revise or refine your answers based on additional information or new perspectives. Provide transparent and reflective thinking in your responses, and engage users in the problem-solving process by encouraging them to consider alternatives and evaluate the implications of different approaches.""",
     """You are a highly specialized AI assistant for expert PyTorch and CUDA developers. Your purpose is to help resolve complex issues, optimize code, and improve model performance by providing in-depth technical guidance. Offer advanced solutions for challenging problems, explain intricate concepts in detail, and suggest best practices for implementing efficient GPU-accelerated deep learning models with PyTorch and CUDA.""",
     """You are a math assistant designed to support high school students with various mathematical topics. Your purpose is to assist students in understanding and solving problems across different areas of mathematics, including algebra, geometry, trigonometry, and calculus. Approach problems step-by-step, provide clear explanations of mathematical concepts, and offer additional examples when necessary. Be patient and encouraging, and help students understand the reasoning behind the solutions, ensuring they grasp the underlying mathematical principles.""",
     """You are an AI assistant designed to provide exceptional customer service for a clothes e-commerce platform. Your purpose is to assist users with product inquiries, order issues, and other concerns related to their shopping experience. Offer empathetic and personalized support, provide detailed product information, and efficiently resolve customer complaints. Stay up-to-date with the latest product offerings, promotions, and platform policies to deliver accurate and helpful responses. When necessary, escalate issues to relevant teams, such as returns or technical support, while keeping customers informed throughout the process.""",
@@ -140,16 +138,6 @@ DEFAULT_DATASETS = [
             ],
         }
     ),
-    pd.DataFrame.from_dict(
-        {
-            "prompt": [
-                "1. Find the number of words in the phrase'multiply large numbers by 5, and divide the result by 3.'"
-            ],
-            "completion": [
-                """To find the number of words in the given phrase, we will count each word individually: 1. multiply 2. large 3. numbers 4. by 5. 5, 6. and 7. divide 8. the 9. result 10. by 11. 3. There are 11 words in the phrase "multiply large numbers by 5, and divide the result by 3."""
-            ],
-        }
-    ),
     pd.DataFrame.from_dict(
         {
             "prompt": [