Embedding Impact Across Model Configurations
Published: 2024-01-28 16:04:07

**Embedding Impact Across Model Configurations**


Fine-tuning a model like ChatGPT with embeddings involves a few steps. Here’s a simplified outline of the process:

    Embedding Generation:

    Use an embedding model (e.g., BERT, Word2Vec) to generate embeddings for your data.

python generate_embeddings.py --data_file <data_file> --embedding_file <embedding_file>

    Prepare Data:

    Combine embeddings with your data in a format suitable for training.

python prepare_data.py --embedding_file <embedding_file> --output_file <training_data>

    Fine-tuning:

    Use the prepared data to fine-tune ChatGPT.

python run_finetuning.py --model_name_or_path gpt-3.5-turbo --train_file <training_data> --output_dir <output_dir>

    Evaluation:

    Evaluate the fine-tuned model on a separate dataset to check the performance.

python evaluate.py --model_name_or_path <output_dir> --eval_file <eval_data>

Example

Below are simplified example scripts and data file content to give you an idea of how this process might be structured.

    Example Content of data_file:

question: What is the capital of France? | answer: Paris
question: What is the capital of Germany? | answer: Berlin
...

    generate_embeddings.py:

from transformers import BertTokenizer, BertModel
import torch

def generate_embeddings(data_file, embedding_file):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertModel.from_pretrained('bert-base-uncased')

    with open(data_file, 'r') as f:
        data = f.readlines()

    embeddings = []
    for line in data:
        inputs = tokenizer(line, return_tensors='pt', truncation=True)
        with torch.no_grad():
            outputs = model(**inputs)
        embeddings.append(outputs.last_hidden_state.mean(dim=1).squeeze().tolist())

    with open(embedding_file, 'w') as f:
        for embedding in embeddings:
            f.write(','.join(map(str, embedding)) + '\n')

if __name__ == "__main__":
    generate_embeddings('<data_file>', '<embedding_file>')

    prepare_data.py:

def prepare_data(embedding_file, training_data):
    with open(embedding_file, 'r') as ef, open(training_data, 'w') as tf:
        for line in ef:
            embedding = line.strip()
            tf.write(embedding + '\n')

if __name__ == "__main__":
    prepare_data('<embedding_file>', '<training_data>')

    run_finetuning.py:

from transformers import GPT2Tokenizer, GPT2LMHeadModel, TextDataset, DataCollatorForLanguageModeling, Trainer, TrainingArguments

def fine_tune(training_data, output_dir):
    tokenizer = GPT2Tokenizer.from_pretrained('gpt-2')
    model = GPT2LMHeadModel.from_pretrained('gpt-2')

    train_dataset = TextDataset(
        tokenizer=tokenizer,
        file_path=training_data,
        block_size=128,
    )

    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer,
        mlm=False,
    )

    training_args = TrainingArguments(
        output_dir=output_dir,
        overwrite_output_dir=True,
        num_train_epochs=1,
        per_device_train_batch_size=32,
        save_steps=10_000,
        save_total_limit=2,
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        data_collator=data_collator,
        train_dataset=train_dataset,
    )

    trainer.train()

if __name__ == "__main__":
    fine_tune('<training_data>', '<output_dir>')

    evaluate.py:

from transformers import GPT2Tokenizer, GPT2LMHeadModel, pipeline
import torch

def evaluate(model_path, eval_file):
    # Load the fine-tuned model and tokenizer
    model = GPT2LMHeadModel.from_pretrained(model_path)
    tokenizer = GPT2Tokenizer.from_pretrained(model_path)
    
    # Create a text generation pipeline
    text_generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
    
    # Read the evaluation data
    with open(eval_file, 'r') as f:
        eval_data = f.readlines()
    
    # Iterate through the evaluation data and generate responses
    for i, prompt in enumerate(eval_data):
        generated_text = text_generator(prompt, max_length=150, do_sample=True, temperature=0.7)
        print(f'{i+1}. Prompt: {prompt.strip()}\n   Generated: {generated_text[0]["generated_text"]}\n')
    
    # Optionally, compute some evaluation metrics (e.g., BLEU, perplexity)
    # ...

if __name__ == "__main__":
    evaluate('<model_path>', '<eval_file>')

Replace placeholders like <data_file>, <embedding_file>, <training_data>, and <output_dir> with your actual file paths. Note: these scripts are simplified examples and may not work out of the box for your specific scenario.

Based on the gathered data, here is a detailed comparison table that incorporates the differences between a Normal Model, Directly Fine-tuned Model, Fine-tuned with Embeddings, and Not Fine-tuned but with Embeddings:



![Image description](https://dev-to-uploads.s3.amazonaws.com/uploads/articles/gkq5ptx0ma52k7jju27b.png)


The accuracy between these configurations may vary based on the specific task and data. Fine-tuning directly and using embeddings are both valid strategies, but their suitability may vary depending on the specific requirements and constraints of your project.