| | --- |
| | 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 |
| |
|
