Voting mayoritas pada beberapa sumber data

Tim Anda sedang mengembangkan model AI untuk secara otomatis menghasilkan laporan quality control (QC) smartphone. Untuk itu, Anda telah mengumpulkan data preferensi dari tiga sumber quality control yang berbeda — "Automated Vision System", "Human Inspector", dan "Customer Feedback". Masing-masing telah memberi label pasangan cuplikan teks sebagai 'chosen' dan 'rejected'. Setiap pasangan memiliki 'id' unik, dan setiap entri menampilkan ulasan QC yang dipilih.

quality_df adalah DataFrame gabungan yang dimuat menggunakan pandas. Himpunan data ini memuat data dari ketiga sumber tersebut. Selain itu, kelas Counter telah diimpor sebelumnya dari modul collections.

Latihan ini merupakan bagian dari kursus

Reinforcement Learning from Human Feedback (RLHF)

Instruksi latihan

Hitung frekuensi kemunculan setiap pasangan (chosen, rejected) dalam fungsi vote.
Temukan pasangan (chosen, rejected) dengan jumlah suara tertinggi.

Latihan interaktif langsung praktik

Cobalah latihan ini dengan melengkapi kode contoh ini.

def majority_vote(df):
  	# Count occurrences of each (chosen, rejected) pair
    votes = ____
    # Find the (chosen, rejected) pair with the highest vote count
    winner = ____
    return winner

final_preferences = quality_df.groupby(['id']).apply(majority_vote)

print(final_preferences)

Edit dan Jalankan Kode

Latihan ini merupakan bagian dari kursus

Reinforcement Learning from Human Feedback (RLHF)

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4.8+

Mulai Kursus Gratis

This chapter introduces the basics of Reinforcement Learning with Human Feedback (RLHF), a technique that uses human input to help AI models learn more effectively. Get started with RLHF by understanding how it differs from traditional reinforcement learning and why human feedback can enhance AI performance in various domains.

Exercise 1: Introduction to RLHF Exercise 2: Text generation with RLHF Exercise 3: Classifying generated text for RLHF Exercise 4: RL vs. RLHF Exercise 5: Exploring pre-trained LLMs Exercise 6: Tokenize a text dataset Exercise 7: Fine-tuning for review classification Exercise 8: Preparing data for RLHF Exercise 9: Preparing the preference dataset Exercise 10: Extracting prompts

Discover how to set up systems for gathering human feedback in this Chapter. Learn best practices for collecting high-quality data, from pairwise comparisons to uncertainty sampling, and explore strategies for enhancing your data collection.

Exercise 1: Methods for high-quality feedback gathering Exercise 2: Understanding comparison and rating in RLHF Exercise 3: Comparing slogans for a gym campaign Exercise 4: Measuring feedback quality and relevance Exercise 5: Low confidence Exercise 6: K-means for feedback clustering Exercise 7: Active learning Exercise 8: Implementing an active learning pipeline Exercise 9: Active learning loop

In this Chapter, you'll get into the core of Reinforcement Learning from Human Feedback training. This includes exploring fine-tuning with PPO, techniques to train efficiently, and handling potential divergences from your metrics' objectives.

Exercise 1: Reward models explored Exercise 2: Initializing the reward Exercise 3: Setting up the reward trainer Exercise 4: Training with PPO Exercise 5: Initialize the PPO trainer Exercise 6: PPO fine-tuning Exercise 7: Efficient fine-tuning in RLHF Exercise 8: Prepare for 8-bit Training Exercise 9: Train with LoRA

Explore key techniques for assessing and improving model performance in this last Chapter of Reinforcement Learning from Human Feedback (RLHF): from fine-tuning metrics to incorporating diverse feedback sources, you'll be provided with a comprehensive toolkit to refine your models effectively.

Exercise 1: Metrik dan penyesuaian model Exercise 2: Mengurangi dampak KL divergence negatif Exercise 3: Memeriksa reward model Exercise 4: Mengintegrasikan beragam sumber umpan balik Exercise 5: Voting mayoritas pada beberapa sumber data

Latihan Saat Ini

Exercise 6: Identifikasi sumber data yang tidak andal Exercise 7: Evaluasi model RLHF Exercise 8: Menafsirkan kurva Exercise 9: Mengevaluasi RLHF dengan metrik Exercise 10: Menutup perjalanan RLHF Anda