From 73fa4a1b15804fd2aac8ff3fc0a8326653ee582f Mon Sep 17 00:00:00 2001 From: ChenXin Date: Thu, 27 Feb 2020 20:02:24 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E5=A4=8D=E4=BA=86=20tutorial=5F2=20?= =?UTF-8?q?=E7=9A=84=E4=B8=80=E4=BA=9B=E9=94=99=E8=AF=AF=EF=BC=9B=E5=A2=9E?= =?UTF-8?q?=E5=8A=A0=E4=BA=86=20tutorial=5F2=20=E7=9A=84=20ipynb?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/tutorials/tutorial_2_vocabulary.rst | 10 +- docs/source/tutorials/文本分类.rst | 4 + tutorials/tutorial_2_vocabulary.ipynb | 343 ++++++++++++++++++++++++ 3 files changed, 352 insertions(+), 5 deletions(-) create mode 100644 tutorials/tutorial_2_vocabulary.ipynb diff --git a/docs/source/tutorials/tutorial_2_vocabulary.rst b/docs/source/tutorials/tutorial_2_vocabulary.rst index 0b26a419..dd394d04 100644 --- a/docs/source/tutorials/tutorial_2_vocabulary.rst +++ b/docs/source/tutorials/tutorial_2_vocabulary.rst @@ -84,18 +84,18 @@ fastNLP中的Vocabulary # 将验证集或者测试集在建立词表是放入no_create_entry_dataset这个参数中。 vocab.from_dataset(tr_data, field_name='chars', no_create_entry_dataset=[dev_data]) - - :class:`~fastNLP.Vocabulary` 中的 `no_create_entry` , 建议在添加来自于测试集和验证集的词的时候将该参数置为True, 或将验证集和测试集 +:class:`~fastNLP.Vocabulary` 中的 `no_create_entry` , 建议在添加来自于测试集和验证集的词的时候将该参数置为True, 或将验证集和测试集 传入 `no_create_entry_dataset` 参数。它们的意义是在接下来的模型会使用pretrain的embedding(包括glove, word2vec, elmo与bert)且会finetune的 情况下，如果仅使用来自于train的数据建立vocabulary，会导致只出现在test与dev中的词语无法充分利用到来自于预训练embedding的信息(因为他们 -会被认为是unk)，所以在建立词表的时候将test与dev考虑进来会使得最终的结果更好。通过与fastNLP中的各种Embedding配合使用，会有如下的效果， +会被认为是unk)，所以在建立词表的时候将test与dev考虑进来会使得最终的结果更好。 + +通过与fastNLP中的各种Embedding配合使用，会有如下的效果， 如果一个词出现在了train中，但是没在预训练模型中，embedding会为随机初始化，且它单独的一个vector，如果finetune embedding的话， 这个词在更新之后可能会有更好的表示; 而如果这个词仅出现在了dev或test中，那么就不能为它们单独建立vector，而应该让它指向unk这个vector的 值(当unk的值更新时，这个词也使用的是更新之后的vector)。所以被认为是no_create_entry的token，将首先从预训练的词表中寻找它的表示，如 果找到了，就使用该表示; 如果没有找到，则认为该词的表示应该为unk的表示。 -下面我们结合部分 :class:`~fastNLP.embeddings.StaticEmbedding` 的例子来说明下该值造成的影响，如果您对 - :class:`~fastNLP.embeddings.StaticEmbedding` 不太了解，您可以先参考 :doc:`使用Embedding模块将文本转成向量 ` 部分再来阅读该部分 +下面我们结合部分 :class:`~fastNLP.embeddings.StaticEmbedding` 的例子来说明下该值造成的影响，如果您对 :class:`~fastNLP.embeddings.StaticEmbedding` 不太了解，您可以先参考 :doc:`使用Embedding模块将文本转成向量 ` 部分再来阅读该部分 .. code-block:: python diff --git a/docs/source/tutorials/文本分类.rst b/docs/source/tutorials/文本分类.rst index e658917f..29e96c20 100644 --- a/docs/source/tutorials/文本分类.rst +++ b/docs/source/tutorials/文本分类.rst @@ -3,6 +3,10 @@ 文本分类(Text classification)任务是将一句话或一段话划分到某个具体的类别。比如垃圾邮件识别，文本情绪分类等。这篇教程可以带你从零开始了解 fastNLP 的使用 +.. note:: + + 本教程推荐使用 GPU 进行实验 + .. code-block:: text 1, 商务大床房，房间很大，床有2M宽，整体感觉经济实惠不错! diff --git a/tutorials/tutorial_2_vocabulary.ipynb b/tutorials/tutorial_2_vocabulary.ipynb new file mode 100644 index 00000000..50862293 --- /dev/null +++ b/tutorials/tutorial_2_vocabulary.ipynb @@ -0,0 +1,343 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# fastNLP中的 Vocabulary\n", + "## 构建 Vocabulary" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from fastNLP import Vocabulary\n", + "\n", + "vocab = Vocabulary()\n", + "vocab.add_word_lst(['复', '旦', '大', '学']) # 加入新的字\n", + "vocab.add_word('上海') # `上海`会作为一个整体\n", + "vocab.to_index('复') # 应该会为3\n", + "vocab.to_index('我') # 会输出1，Vocabulary中默认pad的index为0, unk(没有找到的词)的index为1\n", + "\n", + "# 在构建target的Vocabulary时，词表中应该用不上pad和unk，可以通过以下的初始化\n", + "vocab = Vocabulary(unknown=None, padding=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Vocabulary(['positive', 'negative']...)" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab.add_word_lst(['positive', 'negative'])" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab.to_index('positive')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 没有设置 unk 的情况" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "ValueError", + "evalue": "word `neutral` not in vocabulary", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvocab\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'neutral'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# 会报错，因为没有unk这种情况\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/core/vocabulary.py\u001b[0m in \u001b[0;36mto_index\u001b[0;34m(self, w)\u001b[0m\n\u001b[1;32m 414\u001b[0m \u001b[0;34m:\u001b[0m\u001b[0;32mreturn\u001b[0m \u001b[0mint\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 415\u001b[0m \"\"\"\n\u001b[0;32m--> 416\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 417\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 418\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/core/vocabulary.py\u001b[0m in \u001b[0;36m_wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_word2idx\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrebuild\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild_vocab\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0m_wrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/envs/now/lib/python3.8/site-packages/FastNLP-0.5.0-py3.8.egg/fastNLP/core/vocabulary.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, w)\u001b[0m\n\u001b[1;32m 272\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_word2idx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munknown\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 273\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 274\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"word `{}` not in vocabulary\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 275\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 276\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0m_check_build_vocab\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: word `neutral` not in vocabulary" + ] + } + ], + "source": [ + "vocab.to_index('neutral') # 会报错，因为没有unk这种情况" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 设置 unk 的情况" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0, '')" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from fastNLP import Vocabulary\n", + "\n", + "vocab = Vocabulary(unknown='', padding=None)\n", + "vocab.add_word_lst(['positive', 'negative'])\n", + "vocab.to_index('neutral'), vocab.to_word(vocab.to_index('neutral'))" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Vocabulary(['positive', 'negative']...)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "vocab" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------------------------------------------+--------+\n", + "| chars | target |\n", + "+---------------------------------------------------+--------+\n", + "| [4, 2, 2, 5, 6, 7, 3] | 0 |\n", + "| [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 3] | 1 |\n", + "+---------------------------------------------------+--------+\n" + ] + } + ], + "source": [ + "from fastNLP import Vocabulary\n", + "from fastNLP import DataSet\n", + "\n", + "dataset = DataSet({'chars': [\n", + " ['今', '天', '天', '气', '很', '好', '。'],\n", + " ['被', '这', '部', '电', '影', '浪', '费', '了', '两', '个', '小', '时', '。']\n", + " ],\n", + " 'target': ['neutral', 'negative']\n", + "})\n", + "\n", + "vocab = Vocabulary()\n", + "vocab.from_dataset(dataset, field_name='chars')\n", + "vocab.index_dataset(dataset, field_name='chars')\n", + "\n", + "target_vocab = Vocabulary(padding=None, unknown=None)\n", + "target_vocab.from_dataset(dataset, field_name='target')\n", + "target_vocab.index_dataset(dataset, field_name='target')\n", + "print(dataset)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Vocabulary(['今', '天', '心', '情', '很']...)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from fastNLP import Vocabulary\n", + "from fastNLP import DataSet\n", + "\n", + "tr_data = DataSet({'chars': [\n", + " ['今', '天', '心', '情', '很', '好', '。'],\n", + " ['被', '这', '部', '电', '影', '浪', '费', '了', '两', '个', '小', '时', '。']\n", + " ],\n", + " 'target': ['positive', 'negative']\n", + "})\n", + "dev_data = DataSet({'chars': [\n", + " ['住', '宿', '条', '件', '还', '不', '错'],\n", + " ['糟', '糕', '的', '天', '气', '，', '无', '法', '出', '行', '。']\n", + " ],\n", + " 'target': ['positive', 'negative']\n", + "})\n", + "\n", + "vocab = Vocabulary()\n", + "# 将验证集或者测试集在建立词表是放入no_create_entry_dataset这个参数中。\n", + "vocab.from_dataset(tr_data, field_name='chars', no_create_entry_dataset=[dev_data])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + " 4%|▎ | 2.31M/63.5M [00:00<00:02, 22.9MB/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "http://212.129.155.247/embedding/glove.6B.50d.zip not found in cache, downloading to /tmp/tmpvziobj_e\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 63.5M/63.5M [00:01<00:00, 41.3MB/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Finish download from http://212.129.155.247/embedding/glove.6B.50d.zip\n", + "Copy file to /remote-home/ynzheng/.fastNLP/embedding/glove.6B.50d\n", + "Found 2 out of 6 words in the pre-training embedding.\n", + "tensor([[ 0.9497, 0.3433, 0.8450, -0.8852, -0.7208, -0.2931, -0.7468, 0.6512,\n", + " 0.4730, -0.7401, 0.1877, -0.3828, -0.5590, 0.4295, -0.2698, -0.4238,\n", + " -0.3124, 1.3423, -0.7857, -0.6302, 0.9182, 0.2113, -0.5744, 1.4549,\n", + " 0.7546, -1.6165, -0.0085, 0.0029, 0.5130, -0.4745, 2.5306, 0.8594,\n", + " -0.3067, 0.0578, 0.6623, 0.2080, 0.6424, -0.5246, -0.0534, 1.1404,\n", + " -0.1370, -0.1836, 0.4546, -0.5096, -0.0255, -0.0286, 0.1805, -0.4483,\n", + " 0.4053, -0.3682]], grad_fn=)\n", + "tensor([[ 0.1320, -0.2392, 0.1732, -0.2390, -0.0463, 0.0494, 0.0488, -0.0886,\n", + " 0.0224, -0.1300, 0.0369, 0.1800, 0.0750, -0.0183, 0.2264, 0.1628,\n", + " 0.1261, -0.1259, 0.1663, -0.1230, -0.1904, -0.0532, 0.1397, -0.0259,\n", + " -0.1799, 0.0226, 0.1858, 0.1981, 0.1338, 0.2394, 0.0248, 0.0203,\n", + " -0.1722, -0.1683, -0.1892, 0.0874, 0.0562, -0.0394, 0.0306, -0.1761,\n", + " 0.1015, -0.0171, 0.1172, 0.1357, 0.1519, -0.0011, 0.1572, 0.1265,\n", + " -0.2391, -0.0258]], grad_fn=)\n", + "tensor([[ 0.1318, -0.2552, -0.0679, 0.2619, -0.2616, 0.2357, 0.1308, -0.0118,\n", + " 1.7659, 0.2078, 0.2620, -0.1643, -0.8464, 0.0201, 0.0702, 0.3978,\n", + " 0.1528, -0.2021, -1.6184, -0.5433, -0.1786, 0.5389, 0.4987, -0.1017,\n", + " 0.6626, -1.7051, 0.0572, -0.3241, -0.6683, 0.2665, 2.8420, 0.2684,\n", + " -0.5954, -0.5004, 1.5199, 0.0396, 1.6659, 0.9976, -0.5597, -0.7049,\n", + " -0.0309, -0.2830, -0.1356, 0.6429, 0.4149, 1.2362, 0.7659, 0.9780,\n", + " 0.5851, -0.3018]], grad_fn=)\n", + "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.]], grad_fn=)\n", + "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0.]], grad_fn=)\n" + ] + } + ], + "source": [ + "import torch\n", + "from fastNLP.embeddings import StaticEmbedding\n", + "from fastNLP import Vocabulary\n", + "\n", + "vocab = Vocabulary()\n", + "vocab.add_word('train')\n", + "vocab.add_word('only_in_train') # 仅在train出现，但肯定在预训练词表中不存在\n", + "vocab.add_word('test', no_create_entry=True) # 该词只在dev或test中出现\n", + "vocab.add_word('only_in_test', no_create_entry=True) # 这个词在预训练的词表中找不到\n", + "\n", + "embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50d')\n", + "print(embed(torch.LongTensor([vocab.to_index('train')])))\n", + "print(embed(torch.LongTensor([vocab.to_index('only_in_train')])))\n", + "print(embed(torch.LongTensor([vocab.to_index('test')])))\n", + "print(embed(torch.LongTensor([vocab.to_index('only_in_test')])))\n", + "print(embed(torch.LongTensor([vocab.unknown_idx])))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python Now", + "language": "python", + "name": "now" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}