originalankur

{
  "test_data": [
    {
      "topic": "Politics",
      "news_headline": "Congressional committee holds hearings on campaign finance reform"
    },
    {
      "topic": "Politics",
      "news_headline": "Local council votes to approve controversial zoning changes"
    },

originalankur

{
  "training_data": [
    {
      "topic": "Politics",
      "news_headline": "Senate passes sweeping infrastructure bill with bipartisan support"
    },
    {
      "topic": "Politics",
      "news_headline": "Governor announces new tax reform proposal ahead of election"
    },

originalankur

import re
import os
import sys
import tempfile
import subprocess
from pathlib import Path
from typing import List, Tuple

class AsciiDocCodeExtractor:
    def __init__(self, file_path: str):

originalankur

import json
from typing import Literal
import dspy

def configure_ollama_lm(model: str = "ollama/gemma3:4b", 
                        base_url: str = "http://localhost:11434"):
    """
    Configures DSPy to use a local Ollama model.
    """
    print("Connecting to local Ollama model...")

originalankur

import sys

import contextualSpellCheck
import spacy

def filter_words(text):
    text = text.strip()
    if text in nlp.Defaults.stop_words:
        print("in stop word", text)
        return False

originalankur

<script src="https://cdn.safetycop.io/bundle.embed.1982642.min.js" crossorigin="anonymous"></script>

originalankur

"""feedfinder: Find the Web feed for a Web page
http://www.aaronsw.com/2002/feedfinder/

Usage:
  feed(uri) - returns feed found for a URI
  feeds(uri) - returns all feeds found for a URI

    >>> import feedfinder
    >>> feedfinder.feed('scripting.com')
    'http://scripting.com/rss.xml'

originalankur

#!/usr/bin/env python
# -*- coding: utf-8 -*-

"""

pip install networkx distance pattern 

In Flipboard's article[1], they kindly divulge their interpretation
of the summarization technique called LexRank[2].

originalankur

def normalize_line(line):
    return [piece.strip() for piece in line.split("|")[1:-1]]

def is_valid_line(line):
    return "|" in line

def load(text):
    lines =  map(normalize_line,
                filter(is_valid_line,
                        text.strip().splitlines()))