|
--- |
|
library_name: transformers |
|
tags: |
|
- Sales |
|
- FAQ |
|
- ECommerce |
|
license: apache-2.0 |
|
language: |
|
- en |
|
metrics: |
|
- accuracy |
|
pipeline_tag: text-generation |
|
--- |
|
|
|
# Model Card for Model ID |
|
|
|
# FAQ Chatbot for Online Orders and Website Queries |
|
|
|
This model is a large language model (LLM) based on the LLaMA 3 architecture, fine-tuned to handle frequently asked questions (FAQ) related to online orders and website queries. It is designed to provide accurate and helpful responses to common customer inquiries. |
|
|
|
## Model Details |
|
|
|
- **Model Name:** FAQ Chatbot for Online Orders and Website Queries |
|
- **Architecture:** LLaMA 3 |
|
- **Training Data:** This model was trained on a dataset consisting of typical customer queries related to online orders, such as order status, payment issues, returns and refunds, shipping information, and general website navigation. |
|
- **Usage:** The model is intended to be used as a customer support assistant, capable of addressing a wide range of questions about online shopping and website functionality. |
|
|
|
## Features |
|
|
|
- **Natural Language Understanding:** The model can understand and process natural language input, making it user-friendly for customers. |
|
- **Contextual Responses:** Provides responses that are contextually relevant to the user's query. |
|
- **Scalable Support:** Can handle a high volume of queries simultaneously, improving customer service efficiency. |
|
|
|
## Example Queries |
|
|
|
Here are some example queries that the model can handle: |
|
|
|
1. **Order Status:** "Can you tell me the status of my order #12345?" |
|
2. **Payment Issues:** "I'm having trouble processing my payment. Can you help?" |
|
3. **Returns and Refunds:** "How can I return a product I bought?" |
|
4. **Shipping Information:** "When will my order be delivered?" |
|
5. **Website Navigation:** "How do I find the size chart on your website?" |
|
|
|
## How to Use |
|
|
|
To use this model, you can integrate it into your customer support system or chatbot framework. Here's a basic example using the Hugging Face `transformers` library: |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
|
|
# Load the model and tokenizer |
|
model_name = "your-hugging-face-username/faq-chatbot-online-orders" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForCausalLM.from_pretrained(model_name) |
|
|
|
# Example query |
|
query = "Can you tell me the status of my order #12345?" |
|
|
|
# Tokenize the input |
|
inputs = tokenizer(query, return_tensors="pt") |
|
|
|
# Generate response |
|
outputs = model.generate(**inputs) |
|
response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
|
print(response) |
|
python``` |
|
|
|
|
|
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
|
|
|
- **Developed by:** Satwik Kishore |
|
- **Model type:** Text Generation |
|
- **Language(s) (NLP):** English |
|
|
