interneuronai/bank_melli_iran_customer_service_chatbot_bart_dataset

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### Bank Melli Iran Customer Service Chatbot

**Description:** Classify customer inquiries into predefined categories and provide automated responses, improving customer service and reducing response time

## How to Use
Here is how to use this model to classify text into different categories:

from transformers import AutoModelForSequenceClassification, AutoTokenizer

model_name = "interneuronai/bank_melli_iran_customer_service_chatbot_bart"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

def classify_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model(**inputs)
predictions = outputs.logits.argmax(-1)
return predictions.item()

text = "Your text here"
print("Category:", classify_text(text))

原始信息汇总

数据集概述

数据集名称

• 名称: Bank Melli Iran Customer Service Chatbot

数据集描述

• 描述: 该数据集用于将客户咨询分类到预定义的类别，并提供自动回复，旨在提升客户服务质量并减少响应时间。

使用方法

• 使用步骤:
  1. 导入必要的库和模型。
  2. 设置模型名称和路径。
  3. 定义文本分类函数 classify_text(text)，该函数使用预训练的模型和标记器对输入文本进行分类。
  4. 调用 classify_text(text) 函数并打印分类结果。