Model Card: RoBERTa-Base Customer Support Analysis Model

Model Overview

This model is a fine-tuned version of facebook/bart-base trained for content generation tasks. It has been optimized for high-quality text generation while maintaining efficiency.

Model Details

Model Architecture: Roberta-base
Base Model: facebook/bart-base
Task: Content Generation
Dataset: cardiffnlp/tweet_eval
Framework: Hugging Face Transformers
Training Hardware: CUDA

Installation

To use the model, install the necessary dependencies:

pip install transformers torch datasets evaluate

Usage

Load the Model and Tokenizer

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

# Load fine-tuned model
model_path = "fine_tuned_model"
device = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForSeq2SeqLM.from_pretrained(model_path).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Define test text
input_text = "Generate a creative story about space exploration."
inputs = tokenizer(input_text, return_tensors="pt").to(device)

# Generate output
with torch.no_grad():
    output_ids = model.generate(**inputs)
    output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]

print(f"Generated Content: {output_text}")

Training Details

Data Preprocessing

The dataset was split into:

Train: 80%
Validation: 10%
Test: 10%

Tokenization was applied using the facebook/bart-base tokenizer with truncation and padding.

Fine-Tuning

Epochs: 3
Batch Size: 16
Learning Rate: 2e-5
Weight Decay: 0.01
Evaluation Strategy: Epoch-wise

Evaluation Metrics

The model was evaluated using the ROUGE metric:

import evaluate
rouge = evaluate.load("rouge")

# Example evaluation
references = ["The generated story was highly creative and engaging."]
predictions = ["The output was imaginative and captivating."]
results = rouge.compute(predictions=predictions, references=references)
print("Evaluation Metrics (ROUGE):", results)

Performance

ROUGE Score: Achieved competitive scores for content generation quality
Inference Speed: Optimized for efficient text generation
Generalization: Works well on diverse text generation tasks but may require domain-specific fine-tuning.

Limitations

May generate slightly verbose or overly detailed content in some cases.
Requires GPU for optimal performance.

Future Improvements

Experiment with larger models like bart-large for enhanced generation quality.
Fine-tune on domain-specific datasets for better adaptation to specific content types.