Fine-tuning untuk klasifikasi ulasan

Proyek Anda berlanjut dan sekarang Anda ingin mengklasifikasikan ulasan sebagai positif, netral, atau negatif. Anda akan menggunakan model yang telah di-fine-tune untuk mengategorikan ulasan secara otomatis.

Kelas TrainingArguments dan Trainer telah diimpor dari transformers. training_args, model, dan tokenized_datasets juga telah diimpor sebelumnya.

Latihan ini adalah bagian dari kursus

Reinforcement Learning from Human Feedback (RLHF)

Petunjuk latihan

Buat himpunan data train dan test menggunakan subhimpunan "train" dan "test" dari himpunan data tokenized_datasets.
Inisialisasi kelas trainer.
Tambahkan argumen pelatihan ke kelas.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define the train and test datasets
training_dataset = ____
testing_dataset = ____

# Initialize the trainer class
trainer = ____(
# Add arguments to the class
    model=model,
    args=training_args,
    train_dataset=____,
    eval_dataset=____
)

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: Pengantar RLHF Exercise 2: Generasi teks dengan RLHF Exercise 3: Mengklasifikasikan teks yang dihasilkan untuk RLHF Exercise 4: RL vs. RLHF Exercise 5: Menjelajahi LLM pralatih Exercise 6: Tokenisasi himpunan data teks Exercise 7: Fine-tuning untuk klasifikasi ulasan

Latihan Saat Ini

Exercise 8: Menyiapkan data untuk RLHF Exercise 9: Menyiapkan himpunan data preferensi Exercise 10: Mengekstrak prompt

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: 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