Synthetic Training Data for Fine-tuning Multi-Label Sentiment Classification Transformers

Download: Synthetic Data

sentiment_labels = {
0: "very positive",
1: "positive",
2: "somewhat positive",
3: "neutral",
4: "somewhat negative",
5: "negative",
6: "very negative"
}

Data Sample

Title: “”The Miraculous Benefits of Morning Sunlight””
As the world slowly awakens, the morning sun rises, bathing the earth in its warm, golden light. This magical moment is more than just a beautiful sight; it’s a gift to our well-being, our health, and our very existence. The miraculous benefits of morning sunlight are simply astounding.
Research has shown that exposure to morning sunlight has a profound impact on our circadian rhythms, regulating our sleep patterns and boosting our energy levels. It’s as if the sun is awakening our bodies, signaling to our brains that it’s time to start the day. This natural wake-up call is far more effective than any alarm clock, and it sets the”,0
“However, please provide the writing style and topic below.
**Topic:** The benefits of forest bathing
**Writing Style:** Informative and persuasive, with a hint of poetic language
As the sun casts its warm rays upon the forest floor, the trees stand tall, their leaves rustling softly in the gentle breeze. The air is alive with the sweet scent of blooming flowers, and the soft chirping of birds fills the air. This is the realm of forest bathing, a practice that has been revered for centuries for its profound benefits to both body and mind.
Studies have shown that spending time in nature can lower blood pressure, reduce stress levels, and even boost the immune system. But the benefits of forest bathing go far beyond mere physical health. As we immerse ourselves in the tranquility of the forest”,1
“The random topic is: “”The Benefits of Forgetting””
—
Forgetting is often viewed as a curse, a sign of decline or a failing memory. However, the truth is that forgetting can be a powerful tool in our lives. By letting go of unnecessary information, we free up mental space to focus on what truly matters.
One of the most significant benefits of forgetting is the ability to simplify our lives. When we’re no longer bogged down by trivial details, we’re able to see the bigger picture and make more informed decisions. It’s a liberating feeling, knowing that we can let go of the weight of unnecessary knowledge and focus on what truly drives us.
Another benefit of forgetting is the ability to create new connections. When we’re not bound by the constraints of our past experiences, we”,2

The following text is generative, based on instruction and source code.

This guide provides a detailed overview of the steps necessary to generate and utilize synthetic training data for fine-tuning transformers aimed at multi-label sentiment classification tasks. It emphasizes a practical approach, tailored for researchers and engineers working with natural language processing and machine learning.

1. Overview of Synthetic Data Generation

Synthetic data generation involves creating artificial datasets programmatically. For sentiment classification, this data needs to reflect the nuances of natural language as closely as possible. By using transformers pre-trained on vast corpora, you can generate text that mimics human-written content, ensuring diversity and richness in language usage necessary for robust model training.

2. Pre-requisites

Hardware Requirements: Adequate computing resources (GPU recommended for model training and data generation).
Software Requirements: Python, PyTorch, Hugging Face’s Transformers library.
Model Selection: Choose a pre-trained model from Hugging Face's Model Hub that is suitable for text generation, such as GPT-2, GPT-3, or a fine-tuned variant.

3. Setting Up Your Environment

Install necessary libraries:

pip install torch transformers

Download and set up the chosen pre-trained model:

from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "gpt-2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

4. Generating Synthetic Data

Define the Data Generation Function

Define prompts that include instructions for writing in specific sentiment tones (e.g., positive, negative, neutral).

Configure generation parameters such as max_length, num_return_sequences, temperature to control creativity and diversity.

def generate_text(prompt, num_samples):
    input_ids = tokenizer.encode(prompt, return_tensors='pt')
    outputs = model.generate(input_ids, max_length=512, num_return_sequences=num_samples, temperature=1.0, top_p=0.92, do_sample=True)
    return [tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

Run the Generation Loop

Cycle through predefined sentiment labels.

Generate and store the output for each label.

sentiments = ["positive", "negative", "neutral"]
for sentiment in sentiments:
    prompt = f"Write a review that expresses a {sentiment} sentiment about a product."
    texts = generate_text(prompt, 100)  # Generate 100 examples per sentiment
    with open(f"{sentiment}_data.txt", 'a') as file:
        file.write("\n".join(texts))

5. Post-Processing Generated Data

Cleaning: Remove any irrelevant content, correct obvious errors, and ensure that the generated text adheres strictly to the intended sentiment.

Splitting: Divide the dataset into training, validation, and test sets.

6. Fine-Tuning the Transformer

Data Loading

from torch.utils.data import Dataset, DataLoader
class SentimentDataset(Dataset):
    def __init__(self, texts, labels):
        self.texts = texts
        self.labels = labels
    def __len__(self):
        return len(self.texts)
    def __getitem__(self, idx):
        return {"text": self.texts[idx], "label": self.labels[idx]}

Model Training

Adjust the hyperparameters and train the model on the synthetic dataset.

Utilize appropriate loss functions and metrics for multi-label classification.

7. Evaluation and Model Deployment

Testing: Evaluate the model on a held-out test set to measure generalization.

Deployment: Deploy the fine-tuned model in a production environment, potentially with API endpoints for real-time inference.