bhaskarvilles/CSM · Hugging Face

Model Card for Model ID

This model card provides essential details for the model developed by Kerdos Infrasoft Private Limited, designed for customer service applications. This model can run locally as well as be deployed on cloud platforms such as AWS, GCC, and Linode.

Model Details

Model Description

Developed by: Kerdos Infrasoft Private Limited, Meta Llc and Open AI
Funded by: Kerdos Infrasoft Private Limited
Shared by: Kerdos Infrasoft Private Limited
Model type: Transformer-based language model for customer service automation
Language(s) (NLP): English, with potential support for other languages via fine-tuning
License: Apache 2.0
Finetuned from model: GPT-based model

Model Sources

Repository: [Contact for Access]
Paper: N/A
Demo: [Available upon request]

Uses

Direct Use

AI-based customer service for automating responses, handling customer queries, and providing support for businesses.

Downstream Use

Integration into existing customer service platforms as a plug-and-play solution for improving response times and accuracy.

Out-of-Scope Use

Handling sensitive or highly regulated data without proper oversight or additional security measures.
Usage in scenarios requiring deep emotional understanding or psychological support.

Bias, Risks, and Limitations

Bias: The model may carry inherent biases present in the training data, leading to less accurate or fair responses for certain demographic groups.
Risks: Misinterpretation of customer queries or inappropriate responses due to lack of context or nuanced understanding.
Limitations: Limited performance in languages other than English unless fine-tuned; may struggle with highly specialized or niche queries.

Recommendations

Users should implement regular audits and bias checks on model outputs.
Use additional layers of human oversight for critical or sensitive interactions.

How to Get Started with the Model

To get started with the model, install the necessary dependencies and load the model using the following code snippet:

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("path_to_model")
model = AutoModelForSeq2SeqLM.from_pretrained("path_to_model")

input_text = "How can I help you today?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Details

Training Data

The model was trained on a large corpus of customer service interactions, including support tickets, chat logs, and FAQ documents.

Training Procedure

Preprocessing: Text normalization, tokenization, and removal of personally identifiable information (PII) were performed to prepare the data.
Training Hyperparameters:
- Batch size: 32
- Learning rate: 5e-5
- Epochs: 3
- Optimizer: AdamW

Training regime: The model was trained on a mix of on-premise and cloud infrastructure, with periodic validation against a hold-out set to prevent overfitting.

Speeds, Sizes, Times

Training time: Approximately 72 hours on an 8-GPU cluster.
Model size: 1.5 billion parameters.

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated on a dataset of unseen customer service interactions, balanced across various industries and query types.

Factors

Domain-specific performance: Evaluated in contexts such as e-commerce, tech support, and financial services.
Language handling: Tested for proficiency in conversational English.

Metrics

Accuracy: 85% on intent recognition.
F1 Score: 0.78 for response generation.
BLEU Score: 30 for fluency in generated responses.

Results

The model performs reliably in general customer service tasks but may require fine-tuning for industry-specific terminology.

Summary

Model Examination

The model exhibits strong performance in general customer service tasks, with some room for improvement in handling complex, multi-turn dialogues.

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: NVIDIA V100 GPUs
Hours used: Approximately 72 hours
Cloud Provider: AWS
Compute Region: US East (N. Virginia)
Carbon Emitted: Estimated at 150 kg CO2e

Technical Specifications

Model Architecture and Objective

The model uses a transformer-based architecture optimized for sequence-to-sequence tasks, aiming to generate accurate and contextually appropriate responses in customer service scenarios.

Compute Infrastructure

Trained on a mix of local servers with NVIDIA GPUs and cloud-based resources from AWS.

Hardware

NVIDIA V100 GPUs, 16 GB RAM per GPU.

Software

PyTorch 1.7, Transformers 4.3 library.

Citation

BibTeX:

@article{kerdos_customer_service_ai,
  author = {Bhaskar},
  title = {Customer Service AI Model},
  institution = {Kerdos Infrasoft Private Limited},
  year = {2024},
  note = {Available upon request},
}

APA:

Bhaskar. (2024). Customer Service AI Model. Kerdos Infrasoft Private Limited.

Glossary

NLP (Natural Language Processing): The branch of AI focused on the interaction between computers and humans through natural language.
Transformer: A deep learning model architecture designed for handling sequential data, commonly used in NLP tasks.

More Information

For further inquiries, contact Kerdos Infrasoft Private Limited via [ai@kerdos.xyz]

Model Card Authors

Bhaskar, Kerdos Infrasoft Private Limited

Model Card Contact

+91 11 69269337

bhaskarvilles
/

CSM