K-means per il clustering del feedback

Hai un insieme di dati di risposte di feedback e hai usato un modello GPT per calcolare i punteggi di confidenza per ciascuna risposta. Per identificare feedback insoliti o anomali, applichi il clustering k-means alle risposte a bassa confidenza.

L'algoritmo KMeans, le variabili reviews e confidences e la libreria np sono già caricate.

Questo esercizio fa parte del corso

Reinforcement Learning from Human Feedback (RLHF)

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Istruzioni dell'esercizio

Inizializza l'algoritmo k-means. Imposta random_state a 42 per la riproducibilità del codice.
Calcola le distanze dai centri dei cluster per identificare gli outlier come differenza tra data e i rispettivi centri del cluster.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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)

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Questo esercizio fa parte del corso

Reinforcement Learning from Human Feedback (RLHF)

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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: Metodi per raccogliere feedback di alta qualità Exercise 2: Capire confronto e rating nell'RLHF Exercise 3: Confrontare gli slogan per una campagna in palestra Exercise 4: Valutare la qualità e la pertinenza del feedback Exercise 5: Bassa confidenza Exercise 6: K-means per il clustering del feedback

Esercizio in corso

Exercise 7: Active learning Exercise 8: Implementare una pipeline di active learning Exercise 9: Ciclo di 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