PaxMeter

This model is designed to quantitatively convert how the world has changed based on news articles.

Based on world news articles from 2000 to 2011, the data has been processed, and the cumulative results are as follows.

Model Description

PaxMeter quantifies how the world has changed based on the countries mentioned in news articles and the sentiment of those articles.

• Developed by: SangHyeon Jeon
• License: Gemma Terms of Use
• Finetuned from model [optional]: Gemma2-2b-it

Uses

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from huggingface_hub import hf_hub_download

model = AutoModelForSequenceClassification.from_pretrained("shyeon5898/paxmeter")
tokenizer = AutoTokenizer.from_pretrained("shyeon5898/paxmeter")

rep=hf_hub_download(repo_id="shyeon5898/paxmeter", filename="id2label.json")
with open(rep, "r") as json_file:
    i2l = json.load(json_file)
f_pipe = pipeline("text-classification", model=f_model, tokenizer=tokenizer)
input_text = "The South's unification minister, Jeong Se-hyun, told Kim Yong-nam in Pyongyang that the revelation had darkened the mood, and urged the North to end its programme"
outputs = f_pipe(a)
outputs[0]['label']
print(i2l[outputs[0]['label'].split('_')[1]])

Result

-0.108

Positive results indicate that the world has become more peaceful, while negative results signify that the world has become more chaotic.