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B = self.igor.batch_size
R = self.igor.rnn_size
S = self.igor.max_sequence_len
V = self.igor.vocab_size
E = self.igor.embedding_size
### loaded from glove
emb_W = self.igor.embeddings.astype(theano.config.floatX)

## dropout parameters
p_emb = self.igor.p_emb_dropout

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#!/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

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""" 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

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Key-Value Memory Networks for Directly Reading Documents

Introduction

• 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.

Related Work

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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):

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Shortlink: goo.gl/wSuuS9

Supplementary Materials for Generating Wikipedia by Summarizing Long Sequences

The github repository will soon be available at github.com/tensorflow/tensor2tensor/tree/master/tensor2tensor/data_generators/wikisum

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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)

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# 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