metadata
license: creativeml-openrail-m
datasets:
- princeton-nlp/SWE-bench_Verified
language:
- aa
metrics:
- character
library_name: asteroid
pipeline_tag: translation
tags:
- medical
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