Slide 1:

Introduction to Fine-tuning T5-small for RAG (Retrieval-Augmented Generation)

Fine-tuning is the process of adapting a pre-trained language model to a specific task or domain. In this case, we will fine-tune the T5-small model, a variant of the T5 (Text-to-Text Transfer Transformer) model, to excel at RAG, which combines retrieval and generation for open-domain question answering.

Ready for some cool stuff? Here’s how we can tackle this:

# No code for this slide

Slide 2:

Setting up the Environment

Before we start, we need to set up our environment by installing the required libraries and dependencies. We will use the DSPy (Distributed Semantic Precomputing) library, which is a Python library for efficient text retrieval and encoding.

Let me walk you through this step by step! Here’s how we can tackle this:

!pip install dspy-ml

Slide 3:

Importing Libraries

Import the necessary libraries and modules for fine-tuning and working with the T5-small model and DSPy.

Here’s where it gets exciting! Here’s how we can tackle this:

import dspy
import transformers
from transformers import T5ForConditionalGeneration, T5Tokenizer

Slide 4:

Loading the T5-small Model and Tokenizer

Load the pre-trained T5-small model and tokenizer from the Hugging Face Transformers library.

This next part is really neat! Here’s how we can tackle this:

model = T5ForConditionalGeneration.from_pretrained('t5-small')
tokenizer = T5Tokenizer.from_pretrained('t5-small')

Slide 5:

Preparing the Dataset

Prepare your dataset for fine-tuning. The dataset should be in a specific format, with input and output sequences. Here’s an example of how to prepare a dataset for question-answering tasks.

Here’s where it gets exciting! Here’s how we can tackle this:

dataset = [
    {"input": "Question: What is the capital of France?", "output": "The capital of France is Paris."},
    {"input": "Question: How many planets are in our solar system?", "output": "There are 8 planets in our solar system."},
    # Add more examples
]

Slide 6:

Encoding the Dataset

Encode the input and output sequences using the T5 tokenizer.

Let’s break this down together! Here’s how we can tackle this:

def encode_dataset(dataset, tokenizer):
    encoded_dataset = []
    for example in dataset:
        input_encoding = tokenizer.encode_plus(example["input"], return_tensors="pt", padding="max_length", max_length=512, truncation=True)
        output_encoding = tokenizer.encode_plus(example["output"], return_tensors="pt", padding="max_length", max_length=512, truncation=True)
        encoded_dataset.append({"input_ids": input_encoding["input_ids"], "attention_mask": input_encoding["attention_mask"], "labels": output_encoding["input_ids"]})
    return encoded_dataset

Slide 7:

Setting up the Semantic Retriever

Set up the semantic retriever using DSPy for efficient text retrieval.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

semantic_retriever = dspy.SemanticRetriever(model_path="facebook/dpr-ctx_encoder-single-nq-base")

Slide 8:

Retrieval-Augmented Generation (RAG) Function

Define a function that combines retrieval and generation for open-domain question answering.

Let’s make this super clear! Here’s how we can tackle this:

def rag(question, tokenizer, model, retriever):
    input_ids = tokenizer.encode(question, return_tensors="pt")
    retrieved_docs = retriever.retrieve(question, top_k=5)
    retrieved_text = "\n".join([doc.text for doc in retrieved_docs])
    combined_input = "Question: " + question + " Context: " + retrieved_text
    combined_input_ids = tokenizer.encode(combined_input, return_tensors="pt")
    outputs = model.generate(combined_input_ids, max_length=512, early_stopping=True)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

Slide 9:

Fine-tuning the T5-small Model

Fine-tune the T5-small model on your encoded dataset using the Hugging Face Trainer.

Here’s where it gets exciting! Here’s how we can tackle this:

from transformers import Trainer, TrainingArguments

training_args = TrainingArguments(output_dir="output", num_train_epochs=3, per_device_train_batch_size=4, warmup_steps=500, weight_decay=0.01, logging_dir="logs")

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

trainer.train()

Slide 10:

Saving the Fine-tuned Model

Save the fine-tuned model for future use.

Here’s a handy trick you’ll love! Here’s how we can tackle this:

model.save_pretrained("fine-tuned-t5-small")

Slide 11:

Using the Fine-tuned Model for RAG

Use the fine-tuned T5-small model with the RAG function for open-domain question answering.

This next part is really neat! Here’s how we can tackle this:

question = "What is the largest planet in our solar system?"
answer = rag(question, tokenizer, model, semantic_retriever)
print(answer)

Slide 12:

Evaluation and Testing

Evaluate the performance of your fine-tuned model on a test dataset or benchmark. You can use metrics like ROUGE, BLEU, or exact match accuracy.

Here’s where it gets exciting! Here’s how we can tackle this:

# Code for evaluation and testing will depend on the specific dataset and metrics

Slide 13:

Additional Resources

For further learning and exploration, you can refer to the following resources:

• ArXiv Reference: “Longform Question Answering with RAG” (https://arxiv.org/abs/2105.03774)
• ArXiv Reference: “Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks” (https://arxiv.org/abs/2005.11401)

🎊 Awesome Work!

You’ve just learned some really powerful techniques! Don’t worry if everything doesn’t click immediately - that’s totally normal. The best way to master these concepts is to practice with your own data.

What’s next? Try implementing these examples with your own datasets. Start small, experiment, and most importantly, have fun with it! Remember, every data science expert started exactly where you are right now.

Keep coding, keep learning, and keep being awesome! 🚀