Nat Taylor — Blog, AI, Product Management & Tinkering

Test Drive: Gemma APS

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Today I’m test driving Gemma APS “models for text-to-propositions segmentation.” “Abstractive proposition segmentation” (aka claim extraction) is a new concept to me aimed at solving problems including fact-checking. Amazon’s RefChecker is another research here. The idea is break a passage down into simple, individual claims with minimal changes to the text so that they can be processed indepdently. The process is simple:

  1. Accept the Gemma terms and login to HuggingFace
  2. Implement :)

The output is a list of claims

I just used the implementation straight from Google

import nltk
import re

nltk.download('punkt')

start_marker = '<s>'
end_marker = '</s>'
separator = '\n'

def create_propositions_input(text: str) -> str:
    input_sents = nltk.tokenize.sent_tokenize(text)
    propositions_input = ''
    for sent in input_sents:
        propositions_input += f'{start_marker} ' + sent + f' {end_marker}{separator}'
    propositions_input = propositions_input.strip(f'{separator}')
    return propositions_input

def process_propositions_output(text):
    pattern = re.compile(f'{re.escape(start_marker)}(.*?){re.escape(end_marker)}', re.DOTALL)
    output_grouped_strs = re.findall(pattern, text)
    predicted_grouped_propositions = []
    for grouped_str in output_grouped_strs:
        grouped_str = grouped_str.strip(separator)
        props = [x[2:] for x in grouped_str.split(separator)]
        predicted_grouped_propositions.append(props)
    return predicted_grouped_propositions

from transformers import pipeline
import torch

generator = pipeline('text-generation', 'google/gemma-2b-aps-it', device_map='auto', torch_dtype=torch.bfloat16)

passage = 'Sarah Stage, 30, welcomed James Hunter into the world on Tuesday.\nThe baby boy weighed eight pounds seven ounces and was 22 inches long.'
messages = [{'role': 'user', 'content': create_propositions_input(passage)}]
output = generator(messages, max_new_tokens=4096, return_full_text=False)
result = process_propositions_output(output[0]['generated_text'])
print(result)

passage = 'Dante de Blasio, 17, to make his decision by the end of the month. His father has said that despite his six-figure salary the family will struggle to meet cost to send son to Ivy League school.'
messages = [{'role': 'user', 'content': create_propositions_input(passage)}]
output = generator(messages, max_new_tokens=4096, return_full_text=False)
result = process_propositions_output(output[0]['generated_text'])
print(result)

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