Hugging Face's logo

Alkyema
/
FAQ_ChatBot

Text Generation
Transformers
Safetensors
English
llama
Sales
FAQ
ECommerce
conversational
text-generation-inference
Model card Files
xet
 Community
FAQ_ChatBot / README.md
Alkyema's picture
Alkyema
Update README.md
7340255 verified
preview code
|
raw
history blame
7.37 kB
metadata 
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:

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)



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
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]

### Model Sources [optional]

<!-- Provide the basic links for the model. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->

[More Information Needed]

### Downstream Use [optional]

<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

[More Information Needed]

### Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

[More Information Needed]

### Compute Infrastructure

[More Information Needed]

#### Hardware

[More Information Needed]

#### Software

[More Information Needed]

## Citation [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]