goendalf666/sales-conversations-instruction-base

---
dataset_info:
features:
- name: '0'
dtype: string
splits:
- name: train
num_bytes: 28036745
num_examples: 20940
download_size: 4782593
dataset_size: 28036745
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "sales-conversations-instruction"

Modification of https://huggingface.co/datasets/goendalf666/sales-conversations-2
The following script was used to transform the sales-conversations-2 dataset to this instruction based dataset:

See the main model or github for more information

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

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

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

# 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/conversation2conversation_instruction.py

```
import pandas as pd
from datasets import load_dataset, Dataset

data = load_dataset("goendalf666/sales-conversations-2", split="train")
df = data.to_pandas()
df_dict = df.to_dict(orient='list')
df = df.fillna('')

conversations = []
for i in df.iterrows():
current_conversation = ""
try:
for j in i[1]:
if "Customer:" in j:
current_conversation += j + " "
elif "Salesman:" in j:

prompt = f"""You are a in the role of a Salesman. Here is a conversation:
{current_conversation}

Answer as a Salesman to the previous Statement to convince the person to buy the product or service.
{j}"""

conversations.append(prompt)
current_conversation += j + " "
else:
break
except Exception as e:
print(e)

print(len(conversations))

df = pd.DataFrame(conversations)
ds = Dataset.from_pandas(df)
ds.push_to_hub("goendalf666/sales-conversations-instruction")
```

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

原始信息汇总

数据集概述

数据集信息

• 特征名称: 0
• 特征类型: 字符串
• 数据分割:
  • 名称: train
  • 字节数: 28036745
  • 样本数: 20940
• 下载大小: 4782593
• 数据集大小: 28036745

配置信息

• 配置名称: default
• 数据文件:
  • 分割: train
  • 路径: data/train-*

数据集结构

• 对话角色: 客户和销售员，顺序随机交替出现。
• 对话开始: 客户总是开始对话。
• 对话结束: 未定义谁结束对话。

数据生成

• 依赖数据集: 需要一个教科书数据集，示例依赖于以下数据集:
  • goendalf666/sales-textbook_for_convincing_and_selling
• 数据生成代码: 可在以下链接找到:
  • conversation2conversation_instruction.py