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3.5 KiB

from typing import Optional
from tqdm import tqdm
from datasets import load_dataset
from transformers import AutoTokenizer
import re
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B-Instruct"
MIN_TOKENS = 100
MAX_TOKENS = 141
class Item:
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
PREFIX = "Price is $"
title: str
price: float
category: str
token_count: int = 0
text: Optional[str]
details: Optional[str]
prompt: Optional[str] = None
include = False
def __init__(self, data, price, category):
self.title = data['title']
self.price = price
self.category = category
self.parse(data)
def scrub_details(self):
details = self.details
removals = ['"Batteries Included?": "No"', '"Batteries Included?": "Yes"', '"Batteries Required?": "No"', '"Batteries Required?": "Yes"', "By Manufacturer", "Item", "Date First", "Package", ":", "Number of", "Best Sellers", "Number", "Product "]
for remove in removals:
details = details.replace(remove, "")
return details
def parse(self, data):
self.text = self.title + '\n'
self.text += '\n'.join(data['description'])+ '\n'
self.details = data['details']
if self.details:
self.text += self.scrub_details() + '\n'
features = '\n'.join(data['features'])
if features:
self.text += '\n' + features
self.text = re.sub(r'[:\[\]"{}【】\s]+', ' ', self.text).strip()
self.text = self.text.replace(" ,", ",").replace(",,,",",").replace(",,",",")
tokens = self.tokenizer.encode(self.text, add_special_tokens=False)
if len(tokens) > MIN_TOKENS:
tokens = tokens[:MAX_TOKENS]
self.text = self.tokenizer.decode(tokens)
self.make_prompt()
self.count_tokens()
self.include = True
def question(self):
prompt = "How much is this?\n"
prompt += f"{self.text}\n"
return prompt
def messages(self):
return [
{"role":"system", "content": "You estimate prices to the nearest dollar"},
{"role":"user", "content": self.question()},
{"role":"assistant", "content": f"{self.PREFIX}{str(round(self.price))}.00"}
]
def make_prompt(self):
prompt = self.tokenizer.apply_chat_template(self.messages(), tokenize=False, add_generation_prompt=False)
groups = prompt.split('\n\n')
self.prompt = groups[0]+'\n\n'+'\n\n'.join(groups[2:])
def count_tokens(self):
self.token_count = len(self.tokenizer.encode(self.prompt))
def tokens_between(self, low, high):
return self.token_count >= low and self.token_count < high
def test_prompt(self):
return self.prompt.split(self.PREFIX)[0] + self.PREFIX
def read_dataset(name):
print(f"Loading dataset {name}", flush=True)
dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", f"raw_meta_{name}", split="full", trust_remote_code=True)
results = []
for data in dataset:
try:
price_str = data['price']
if price_str:
price = float(price_str)
if price >= 0.5 and price <= 999.49:
item = Item(data, price, name)
if item.include:
results.append(item)
except ValueError:
pass
print(f"Completed loading {name} with {len(results):,} datapoints", flush=True)
del dataset
return results