K-means untuk pengelompokan umpan balik

Anda memiliki himpunan data berisi respons umpan balik, dan Anda telah menggunakan model GPT untuk menghitung skor keyakinan untuk setiap respons. Untuk mengidentifikasi umpan balik yang tidak biasa atau outlier, Anda menerapkan pengelompokan k-means pada respons dengan keyakinan rendah.

Algoritma KMeans, variabel reviews dan confidences, serta pustaka np telah dimuat sebelumnya.

Latihan ini adalah bagian dari kursus

Reinforcement Learning from Human Feedback (RLHF)

Petunjuk latihan

Inisialisasi algoritma k-means. Atur random_state ke 42 untuk reprodusibilitas kode.
Hitung jarak dari pusat klaster untuk mengidentifikasi outlier sebagai selisih antara data dan pusat klaster yang bersesuaian.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

def detect_anomalies(data, n_clusters=3):
    # Initialize k-means
    ____
    clusters = kmeans.fit_predict(data)
    centers = kmeans.cluster_centers_

    # Calculate distances from cluster centers
    ____
    return distances
  
anomalies = detect_anomalies(confidences)
print(anomalies)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Reinforcement Learning from Human Feedback (RLHF)

SkillTag.level.advancedSkillTag.label

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: Metode untuk Mengumpulkan Umpan Balik Berkualitas Tinggi Exercise 2: Memahami perbandingan dan pemberian rating dalam RLHF Exercise 3: Membandingkan slogan untuk kampanye gym Exercise 4: Mengukur kualitas dan relevansi umpan balik Exercise 5: Keyakinan rendah Exercise 6: K-means untuk pengelompokan umpan balik

Latihan Saat Ini

Exercise 7: Active learning Exercise 8: Mengimplementasikan pipeline active learning Exercise 9: Siklus active learning

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: Model metrics and adjustments Exercise 2: Mitigating negative KL divergence Exercise 3: Checking the reward model Exercise 4: Incorporating diverse feedback sources Exercise 5: Majority voting on multiple data sources Exercise 6: Unreliable data source identification Exercise 7: Evaluating RLHF models Exercise 8: Interpreting curves Exercise 9: Evaluating RLHF with metrics Exercise 10: Wrapping up your RLHF journey