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import random
import numpy as np
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from sklearn.preprocessing import LabelEncoder
# Initialize the model and tokenizer
model_name = "xlm-roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)
# Define action space: [0 = Incorrect, 1 = Correct]
action_space = [0, 1]
# Define environment (simplified): classify text
text_data = [
("Water freezes at 0°C.", 1), # Correct
("The sun rises in the west.", 0), # Incorrect
("Dogs can fly in the sky.", 0), # Incorrect
("Birds lay eggs.", 1), # Correct
("The Earth is flat.", 0), # Incorrect
]
# Q-learning parameters
alpha = 0.1 # Learning rate
gamma = 0.9 # Discount factor
epsilon = 0.1 # Exploration rate
n_episodes = 1000 # Number of episodes for training
q_table = np.zeros((len(text_data), len(action_space))) # Q-table (state x action)
# Reward system (simplified)
def get_reward(text, predicted_label):
correct_label = next((label for t, label in text_data if t == text), None)
if predicted_label == correct_label:
return 1 # Reward for correct classification
return -1 # Penalty for incorrect classification
# Q-learning training loop
for episode in range(n_episodes):
state = random.choice(text_data) # Random initial text input
state_idx = text_data.index(state)
# Exploration vs Exploitation
if random.uniform(0, 1) < epsilon:
action = random.choice(action_space) # Exploration: random action
else:
action = np.argmax(q_table[state_idx]) # Exploitation: choose best action based on Q-table
# Make a prediction based on action
inputs = tokenizer(state[0], return_tensors="pt")
with torch.no_grad():
output = model(**inputs)
predicted_label = torch.argmax(output.logits, dim=1).item()
# Get reward from environment
reward = get_reward(state[0], predicted_label)
# Update Q-table using Q-learning update rule
next_state_idx = random.choice(range(len(text_data))) # Random next state (simplified)
best_next_action = np.argmax(q_table[next_state_idx])
q_table[state_idx, action] = (1 - alpha) * q_table[state_idx, action] + alpha * (reward + gamma * q_table[next_state_idx, best_next_action])
if episode % 100 == 0:
print(f"Episode {episode + 1}/{n_episodes}, Reward: {reward}")
# After training, the model should have learned which actions (labels) to take
print("Training complete.")
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