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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.
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