From 993952876066d85cb51c8df0e7e9a84718eb8f65 Mon Sep 17 00:00:00 2001 From: yh_cc Date: Sun, 6 Dec 2020 13:27:00 +0800 Subject: [PATCH] =?UTF-8?q?=E4=BF=AE=E5=A4=8DTrainer=20fp16=E7=9A=84bug;?= =?UTF-8?q?=20=E6=B7=BB=E5=8A=A0=E4=BD=BF=E7=94=A8=E4=B8=AD=E6=96=87?= =?UTF-8?q?=E8=AF=8D=E8=BF=9B=E8=A1=8C=E5=88=86=E7=B1=BB=E7=9A=84=E4=BE=8B?= =?UTF-8?q?=E5=AD=90?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/source/_static/notebooks/文本分类.ipynb | 226 ++++++++++++++++++++++- 1 file changed, 221 insertions(+), 5 deletions(-) diff --git a/docs/source/_static/notebooks/文本分类.ipynb b/docs/source/_static/notebooks/文本分类.ipynb index de29f632..d18301ec 100644 --- a/docs/source/_static/notebooks/文本分类.ipynb +++ b/docs/source/_static/notebooks/文本分类.ipynb @@ -47,7 +47,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from fastNLP.io import ChnSentiCorpLoader\n", @@ -126,7 +128,9 @@ { "cell_type": "code", "execution_count": 3, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from fastNLP.io import ChnSentiCorpPipe\n", @@ -280,7 +284,9 @@ { "cell_type": "code", "execution_count": 9, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "from torch import nn\n", @@ -803,11 +809,221 @@ ] }, { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 基于词进行文本分类" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "由于汉字中没有显示的字与字的边界，一般需要通过分词器先将句子进行分词操作。\n", + "下面的例子演示了如何不基于fastNLP已有的数据读取、预处理代码进行文本分类。" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (1) 读取数据" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这里我们继续以之前的数据为例，但这次我们不使用fastNLP自带的数据读取代码 " + ] + }, + { "cell_type": "code", "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from fastNLP.io import ChnSentiCorpLoader\n", + "\n", + "loader = ChnSentiCorpLoader() # 初始化一个中文情感分类的loader\n", + "data_dir = loader.download() # 这一行代码将自动下载数据到默认的缓存地址, 并将该地址返回" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "下面我们先定义一个read_file_to_dataset的函数, 即给定一个文件路径，读取其中的内容，并返回一个DataSet。然后我们将所有的DataSet放入到DataBundle对象中来方便接下来的预处理" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, "outputs": [], - "source": [] + "source": [ + "import os\n", + "from fastNLP import DataSet, Instance\n", + "from fastNLP.io import DataBundle\n", + "\n", + "\n", + "def read_file_to_dataset(fp):\n", + " ds = DataSet()\n", + " with open(fp, 'r') as f:\n", + " f.readline() # 第一行是title名称，忽略掉\n", + " for line in f:\n", + " line = line.strip()\n", + " target, chars = line.split('\\t')\n", + " ins = Instance(target=target, raw_chars=chars)\n", + " ds.append(ins)\n", + " return ds\n", + "\n", + "data_bundle = DataBundle()\n", + "for name in ['train.tsv', 'dev.tsv', 'test.tsv']:\n", + " fp = os.path.join(data_dir, name)\n", + " ds = read_file_to_dataset(fp)\n", + " data_bundle.set_dataset(name=name.split('.')[0], dataset=ds)\n", + "\n", + "print(data_bundle) # 查看以下数据集的情况\n", + "# In total 3 datasets:\n", + "# train has 9600 instances.\n", + "# dev has 1200 instances.\n", + "# test has 1200 instances." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (2) 数据预处理" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "在这里，我们首先把句子通过 [fastHan](http://gitee.com/fastnlp/fastHan) 进行分词操作，然后创建词表，并将词语转换为序号。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from fastHan import FastHan\n", + "from fastNLP import Vocabulary\n", + "\n", + "model=FastHan()\n", + "\n", + "# 定义分词处理操作\n", + "def word_seg(ins):\n", + " raw_chars = ins['raw_chars']\n", + " # 由于有些句子比较长，我们只截取前128个汉字\n", + " raw_words = model(raw_chars[:128], target='CWS')[0]\n", + " return raw_words\n", + "\n", + "for name, ds in data_bundle.iter_datasets():\n", + " # apply函数将对内部的instance依次执行word_seg操作，并把其返回值放入到raw_words这个field\n", + " ds.apply(word_seg, new_field_name='raw_words')\n", + " # 除了apply函数，fastNLP还支持apply_field, apply_more(可同时创建多个field)等操作\n", + "\n", + "vocab = Vocabulary()\n", + "\n", + "# 对raw_words列创建词表, 建议把非训练集的dataset放在no_create_entry_dataset参数中\n", + "# 也可以通过add_word(), add_word_lst()等建立词表，请参考http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_2_vocabulary.html\n", + "vocab.from_dataset(data_bundle.get_dataset('train'), field_name='raw_words', \n", + " no_create_entry_dataset=[data_bundle.get_dataset('dev'), \n", + " data_bundle.get_dataset('test')]) \n", + "\n", + "# 将建立好词表的Vocabulary用于对raw_words列建立词表，并把转为序号的列存入到words列\n", + "vocab.index_dataset(data_bundle.get_dataset('train'), data_bundle.get_dataset('dev'), \n", + " data_bundle.get_dataset('test'), field_name='raw_words', new_field_name='words')\n", + "\n", + "# 建立target的词表，target的词表一般不需要padding和unknown\n", + "target_vocab = Vocabulary(padding=None, unknown=None) \n", + "# 一般情况下我们可以只用训练集建立target的词表\n", + "target_vocab.from_dataset(data_bundle.get_dataset('train'), field_name='target') \n", + "# 如果没有传递new_field_name, 则默认覆盖原词表\n", + "target_vocab.index_dataset(data_bundle.get_dataset('train'), data_bundle.get_dataset('dev'), \n", + " data_bundle.get_dataset('test'), field_name='target')\n", + "\n", + "# 我们可以把词表保存到data_bundle中，方便之后使用\n", + "data_bundle.set_vocab(field_name='words', vocab=vocab)\n", + "data_bundle.set_vocab(field_name='target', vocab=target_vocab)\n", + "\n", + "# 我们把words和target分别设置为input和target，这样它们才会在训练循环中被取出并自动padding, 有关这部分更多的内容参考\n", + "# http://www.fastnlp.top/docs/fastNLP/tutorials/tutorial_6_datasetiter.html\n", + "data_bundle.set_target('target')\n", + "data_bundle.set_input('words') # DataSet也有这两个接口\n", + "# 如果某些field，您希望它被设置为target或者input，但是不希望fastNLP自动padding或需要使用特定的padding方式，请参考\n", + "# http://www.fastnlp.top/docs/fastNLP/fastNLP.core.dataset.html\n", + "\n", + "print(data_bundle.get_dataset('train')[:2]) # 我们可以看一下当前dataset的内容" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (3) 选择预训练词向量" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "这里我们选择腾讯的预训练中文词向量，可以在 [腾讯词向量](https://ai.tencent.com/ailab/nlp/en/embedding.html) 处下载并解压。这里我们不能直接使用BERT，因为BERT是基于中文字进行预训练的。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from fastNLP.embeddings import StaticEmbedding\n", + "\n", + "word2vec_embed = StaticEmbedding(data_bundle.get_vocab('words'), \n", + " model_dir_or_name='/path/to/Tencent_AILab_ChineseEmbedding.txt')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 初始化模型\n", + "model = BiLSTMMaxPoolCls(word2vec_embed, len(data_bundle.get_vocab('target')))\n", + "\n", + "# 开始训练\n", + "loss = CrossEntropyLoss()\n", + "optimizer = Adam(model.parameters(), lr=0.001)\n", + "metric = AccuracyMetric()\n", + "device = 0 if torch.cuda.is_available() else 'cpu' # 如果有gpu的话在gpu上运行，训练速度会更快\n", + "\n", + "trainer = Trainer(train_data=data_bundle.get_dataset('train'), model=model, loss=loss, \n", + " optimizer=optimizer, batch_size=32, dev_data=data_bundle.get_dataset('dev'),\n", + " metrics=metric, device=device)\n", + "trainer.train() # 开始训练，训练完成之后默认会加载在dev上表现最好的模型\n", + "\n", + "# 在测试集上测试一下模型的性能\n", + "from fastNLP import Tester\n", + "print(\"Performance on test is:\")\n", + "tester = Tester(data=data_bundle.get_dataset('test'), model=model, metrics=metric, batch_size=64, device=device)\n", + "tester.test()" + ] } ], "metadata": { @@ -826,7 +1042,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.10" } }, "nbformat": 4,