originalankur

question,answer,context,ticker,filing
What area did NVIDIA initially focus on before expanding to other computationally intensive fields?,NVIDIA initially focused on PC graphics.,"Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.",NVDA,2023_10K
What are some of the recent applications of GPU-powered deep learning as mentioned by NVIDIA?,"Recent applications of GPU-powered deep learning include recommendation systems, large language models, and generative AI.","Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large language models, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets, and generative AI, which uses algorithms that creat

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()))