arxiv:2306.11644 region:us
---
dataset_info:
  features:
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    dtype: string
  - name: '1'
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  - name: '2'
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  splits:
  - name: train
    num_bytes: 6821725
    num_examples: 3412
  download_size: 2644154
  dataset_size: 6821725
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---


# Dataset Card for "sales-conversations-2"

# Dataset Card for "sales-conversations"
This dataset was created for the purpose of training a sales agent chatbot that can convince people.

The initial idea came from: textbooks is all you need https://arxiv.org/abs/2306.11644

gpt-3.5-turbo was used for the generation

See the main model or github for more information

salesGPT_v2: https://huggingface.co/goendalf666/salesGPT_v2

github: https://github.com/tom813/salesGPT_foundation

# Structure
The conversations have a customer and a salesman which appear always in changing order. customer, salesman, customer, salesman, etc. 
The customer always starts the conversation
Who ends the conversation is not defined.

# Generation
Note that a textbook dataset is mandatory for this conversation generation. This examples rely on the following textbook dataset:
https://huggingface.co/datasets/goendalf666/sales-textbook_for_convincing_and_selling

The data generation code can be found here: https://github.com/tom813/salesGPT_foundation/blob/main/data_generation/textbook_and_conversation_gen.py

The following prompt was used to create a conversation
```
def create_random_prompt(chapter, roles=["Customer", "Salesman"], range_vals=(3, 7), industries=None):
    if industries is None:
        industries = ["tech", "health", "finance"]  # default industries; replace with your default list if different
    
    x = random.randint(*range_vals)
    
    y = 0
    for i in reversed(range(3, 9)):  # Generalized loop for range of values
        if i * x < 27:
            y = i
            break

    conversation_structure = ""
    for i in range(1, x+1):
            conversation_structure += f"""
        {roles[0]}: #{i}. sentence of {roles[0].lower()}
        {roles[1]}: #{i}. sentence of {roles[1].lower()}"""

    prompt = f"""Here is a chapter from a textbook about convincing people. 
    The purpose of this data is to use it to fine tune a llm. 
    Generate conversation examples that are based on the chapter that is provided and would help an ai to learn the topic by examples. 
    Focus only on the topic that is given in the chapter when generating the examples. 
    Let the example be in the {random.choice(industries)} industry.

    Follow this structure and put each conversation in a list of objects in json format. Only return the json nothing more:
    {conversation_structure}

    Generate {y} lists of those conversations

    Chapter:{chapter}"""

    return prompt
```

[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)