机器翻译数据集.ipynb

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    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import os\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading ..\\data\\fra-eng.zip from http://d2l-data.s3-accelerate.amazonaws.com/fra-eng.zip...\n",
      "Go.\tVa !\n",
      "Hi.\tSalut !\n",
      "Run!\tCours !\n",
      "Run!\tCourez !\n",
      "Who?\tQui ?\n",
      "Wow!\tÇa alors !\n",
      "\n"
     ]
    }
   ],
   "source": [
    "d2l.DATA_HUB['fra-eng'] = (d2l.DATA_URL + 'fra-eng.zip', '94646ad1522d915e7b0f9296181140edcf86a4f5')\n",
    "def read_data_nmt():\n",
    "    data_dir = d2l.download_extract('fra-eng')\n",
    "    with open(os.path.join(data_dir, 'fra.txt'), 'r', encoding='utf-8') as f:\n",
    "        return f.read()\n",
    "\n",
    "raw_text = read_data_nmt()\n",
    "print(raw_text[:75])"
   ],
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     "name": "#%%\n"
    }
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  },
  {
   "cell_type": "markdown",
   "source": [
    "下载数据集后，原始文本数据需要经过几个预处理步骤。 例如，我们用空格代替不间断空格（non-breaking space）， 使用小写字母替换大写字母，并在单词和标点符号之间插入空格。"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go .\tva !\n",
      "hi .\tsalut !\n",
      "run !\tcours !\n",
      "run !\tcourez !\n",
      "who ?\tqui ?\n",
      "wow !\tça alors !\n"
     ]
    }
   ],
   "source": [
    "# 注意：raw_text的类型是字符串，而不是列表！\n",
    "def preprocess_nmt(text):\n",
    "    def no_space(char, prev_char):\n",
    "        return char in set(',.!?') and prev_char != ' '\n",
    "    # 使用空格代替不间断空格，因为不同类型的空白字符在文本处理中可能引起问题，而将它们替换为普通空格有助于简化文本，提高一致性和可处理性\n",
    "    text = text.replace('\\u202f', ' ').replace('\\xa0', ' ').lower()\n",
    "    out = [' ' + char if i > 0 and no_space(char, text[i - 1]) else char for i, char in enumerate(text)]\n",
    "    return ''.join(out)\n",
    "\n",
    "text = preprocess_nmt(raw_text)\n",
    "print(text[:80])"
   ],
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     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": "([['go', '.'],\n  ['hi', '.'],\n  ['run', '!'],\n  ['run', '!'],\n  ['who', '?'],\n  ['wow', '!']],\n [['va', '!'],\n  ['salut', '!'],\n  ['cours', '!'],\n  ['courez', '!'],\n  ['qui', '?'],\n  ['ça', 'alors', '!']])"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "def tokenize_nmt(text, num_examples=None):\n",
    "    source, target = [], []\n",
    "    for i, line in enumerate(text.split('\\n')):\n",
    "        if num_examples and i > num_examples:\n",
    "            break\n",
    "        parts = line.split('\\t')\n",
    "        if len(parts) == 2:\n",
    "            source.append(parts[0].split(' '))\n",
    "            target.append(parts[1].split(' '))\n",
    "    return source, target\n",
    "\n",
    "source, target = tokenize_nmt(text)\n",
    "source[:6], target[:6]"
   ],
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   "source": [
    "def show_list_len_pair_hist(legend, xlabel, ylabel, xlist, ylist):\n",
    "    d2l.set_figsize()\n",
    "    _, _, patches = d2l.plt.hist(\n",
    "        [[len(l) for l in xlist], [len(l) for l in ylist]]\n",
    "    )\n",
    "    d2l.plt.xlabel(xlabel)\n",
    "    d2l.plt.ylabel(ylabel)\n",
    "    for patch in patches[1].patches:\n",
    "        patch.set_hatch('/')\n",
    "    d2l.plt.legend(legend)\n",
    "\n",
    "show_list_len_pair_hist(['source', 'target'], '# tokens per sequence', 'count', source, target)"
   ],
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    "pycharm": {
     "name": "#%%\n"
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   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "10012"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 转换成词表\n",
    "src_vocab = d2l.Vocab(source, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
    "len(src_vocab)"
   ],
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    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "[47, 4, 1, 1, 1, 1, 1, 1, 1, 1]"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 加载数据集\n",
    "def truncate_pad(line, num_steps, padding_token):\n",
    "    if len(line) > num_steps:\n",
    "        return line[:num_steps]\n",
    "    return line + [padding_token] * (num_steps - len(line))\n",
    "\n",
    "truncate_pad(src_vocab[source[0]], 10, src_vocab['<pad>'])"
   ],
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  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "def build_array_nmt(lines, vocab, num_steps):\n",
    "    lines = [vocab[l] for l in lines]\n",
    "    lines = [l + [vocab['<eos>']] for l in lines]\n",
    "    array = torch.tensor([truncate_pad(l, num_steps, vocab['<pad>']) for l in lines])\n",
    "    valid_len = (array != vocab['<pad>']).type(torch.int32).sum(1)\n",
    "    return array, valid_len"
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   "execution_count": 12,
   "outputs": [],
   "source": [
    "def load_data_nmt(batch_size, num_steps, num_examples=600):\n",
    "    text = preprocess_nmt(read_data_nmt())\n",
    "    source, target = tokenize_nmt(text, num_examples)\n",
    "    src_vocab = d2l.Vocab(source, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
    "    tgt_vocab = d2l.Vocab(target, min_freq=2, reserved_tokens=['<pad>', '<bos>', '<eos>'])\n",
    "    src_array, src_valid_len = build_array_nmt(source, src_vocab, num_steps)\n",
    "    tgt_array, tgt_valid_len = build_array_nmt(target, tgt_vocab, num_steps)\n",
    "    data_arrays = (src_array, src_valid_len, tgt_array, tgt_valid_len)\n",
    "    data_iter = d2l.load_array(data_arrays, batch_size)\n",
    "    return data_iter, src_vocab, tgt_vocab"
   ],
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     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X:  tensor([[136,  15,   4,   3,   1,   1,   1,   1],\n",
      "        [  6,   0,   4,   3,   1,   1,   1,   1]], dtype=torch.int32)\n",
      "X的有效长度:  tensor([4, 4])\n",
      "Y:  tensor([[ 0,  5,  3,  1,  1,  1,  1,  1],\n",
      "        [21,  0,  4,  3,  1,  1,  1,  1]], dtype=torch.int32)\n",
      "Y的有效长度:  tensor([3, 4])\n"
     ]
    }
   ],
   "source": [
    "train_iter, src_vocab, tgt_vocab = load_data_nmt(batch_size=2, num_steps=8)\n",
    "for X, X_valid_len, Y, Y_valid_len in train_iter:\n",
    "    print('X: ', X.type(torch.int32))\n",
    "    print('X的有效长度: ', X_valid_len)\n",
    "    print('Y: ', Y.type(torch.int32))\n",
    "    print('Y的有效长度: ', Y_valid_len)\n",
    "    break"
   ],
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     "name": "#%%\n"
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