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import torch
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from datasets import load_dataset
from transformers import BertTokenizer
def load_chatbot_data(file_path, sample_frac=0.05):
df = pd.read_csv(file_path).sample(frac=sample_frac, random_state=42)
labels = np.argmax(df[['winner_model_a', 'winner_model_b', 'winner_tie']].values, axis=1)
return train_test_split(df, labels, test_size=0.2, random_state=42)
def load_personas():
datasets = ["instruction", "npc", "math", "tool", "reasoning", "knowledge"]
all_personas = []
for dataset in datasets:
data = load_dataset("proj-persona/PersonaHub", dataset, split="train")
all_personas.extend([(p['input persona'], p['synthesized text']) for p in data])
return all_personas
class ChatbotDataset(torch.utils.data.Dataset):
def __init__(self, prompts, responses_a, responses_b, labels, tokenizer, max_length=128):
self.tokenizer = tokenizer
self.max_length = max_length
self.prompts = prompts
self.responses_a = responses_a
self.responses_b = responses_b
self.labels = labels
def __len__(self):
return len(self.labels)
def __getitem__(self, idx):
prompt = self.prompts[idx]
response_a = self.responses_a[idx]
response_b = self.responses_b[idx]
encoded_prompt = self.tokenizer.encode_plus(
prompt,
add_special_tokens=True,
max_length=self.max_length,
padding='max_length',
truncation=True,
return_tensors='pt'
)
encoded_response_a = self.tokenizer.encode_plus(
response_a,
add_special_tokens=True,
max_length=self.max_length,
padding='max_length',
truncation=True,
return_tensors='pt'
)
encoded_response_b = self.tokenizer.encode_plus(
response_b,
add_special_tokens=True,
max_length=self.max_length,
padding='max_length',
truncation=True,
return_tensors='pt'
)
return {
'prompt': encoded_prompt,
'response_a': encoded_response_a,
'response_b': encoded_response_b,
'label': torch.tensor(self.labels[idx], dtype=torch.long)
}
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