| 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) |
| } |
|
|