Motoki Wu tokestermw

Supplementary Materials for Generating Wikipedia by Summarizing Long Sequences

Knowledge Bases (KBs) are effective tools for Question Answering (QA) but are often too restrictive (due to fixed schema) and too sparse (due to limitations of Information Extraction (IE) systems).
The paper proposes Key-Value Memory Networks, a neural network architecture based on Memory Networks that can leverage both KBs and raw data for QA.
The paper also introduces MOVIEQA, a new QA dataset that can be answered by a perfect KB, by Wikipedia pages and by an imperfect KB obtained using IE techniques thereby allowing a comparison between systems using any of the three sources.
Link to the paper.

	'system':
	[
	{
	'type': 'text',
	'text': "You are Claude Code, Anthropic's official CLI for Claude.",
	'cache_control': {'type': 'ephemeral'}
	},
	{
	'type': 'text',
	'text': 'You are an interactive CLI tool that helps users with software engineering tasks.

	# train_grpo.py
	#
	# See https://github.com/willccbb/verifiers for ongoing developments
	#
	import re
	import torch
	from datasets import load_dataset, Dataset
	from transformers import AutoTokenizer, AutoModelForCausalLM
	from peft import LoraConfig
	from trl import GRPOConfig, GRPOTrainer

	import torch
	import torch.nn as nn
	import torch.nn.functional as F

	# helpers

	def make_unit_length(x, epsilon=1e-6):
	norm = x.norm(p=2, dim=-1, keepdim=True)
	return x.div(norm + epsilon)

	import random


	class TicTacToe:
	def __init__(self, playerX, playerO):
	self.board = [' ']*9
	self.playerX, self.playerO = playerX, playerO
	self.playerX_turn = random.choice([True, False])

	def play_game(self):

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#!/usr/bin/env python
	# coding: utf-8
	"""Sampling Sequence Data from model"""

	import numpy as np
	import tensorflow as tf
	import json
	import cPickle as pickle
	import itertools as it
	from rnnlib import PTBModel