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from typing import Optional
from transformers import AutoTokenizer
import re
BASE_MODEL = "meta-llama/Meta-Llama-3.1-8B"
MIN_TOKENS = 150
MAX_TOKENS = 160
MIN_CHARS = 300
CEILING_CHARS = MAX_TOKENS * 7
class Item:
"""
An Item is a cleaned, curated datapoint of a Product with a Price
"""
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
PREFIX = "Price is $"
QUESTION = "How much does this cost to the nearest dollar?"
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 "]
title: str
price: float
category: str
token_count: int = 0
details: Optional[str]
prompt: Optional[str] = None
include = False
def __init__(self, data, price):
self.title = data['title']
self.price = price
self.parse(data)
def scrub_details(self):
"""
Clean up the details string by removing common text that doesn't add value
"""
details = self.details
for remove in self.REMOVALS:
details = details.replace(remove, "")
return details
def scrub(self, stuff):
"""
Clean up the provided text by removing unnecessary characters and whitespace
Also remove words that are 7+ chars and contain numbers, as these are likely irrelevant product numbers
"""
stuff = re.sub(r'[:\[\]"{}【】\s]+', ' ', stuff).strip()
stuff = stuff.replace(" ,", ",").replace(",,,",",").replace(",,",",")
words = stuff.split(' ')
select = [word for word in words if len(word)<7 or not any(char.isdigit() for char in word)]
return " ".join(select)
def parse(self, data):
"""
Parse this datapoint and if it fits within the allowed Token range,
then set include to True
"""
contents = '\n'.join(data['description'])
if contents:
contents += '\n'
features = '\n'.join(data['features'])
if features:
contents += features + '\n'
self.details = data['details']
if self.details:
contents += self.scrub_details() + '\n'
if len(contents) > MIN_CHARS:
contents = contents[:CEILING_CHARS]
text = f"{self.scrub(self.title)}\n{self.scrub(contents)}"
tokens = self.tokenizer.encode(text, add_special_tokens=False)
if len(tokens) > MIN_TOKENS:
tokens = tokens[:MAX_TOKENS]
text = self.tokenizer.decode(tokens)
self.make_prompt(text)
self.include = True
def make_prompt(self, text):
"""
Set the prompt instance variable to be a prompt appropriate for training
"""
self.prompt = f"{self.QUESTION}\n\n{text}\n\n"
self.prompt += f"{self.PREFIX}{str(round(self.price))}.00"
self.token_count = len(self.tokenizer.encode(self.prompt, add_special_tokens=False))
def test_prompt(self):
"""
Return a prompt suitable for testing, with the actual price removed
"""
return self.prompt.split(self.PREFIX)[0] + self.PREFIX
def __repr__(self):
"""
Return a String version of this Item
"""
return f"<{self.title} = ${self.price}>"
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