mirror of
https://gitee.com/fastnlp/fastNLP.git
synced 2024-12-05 05:38:31 +08:00
881ce01762
* 1. CRF增加支持bmeso类型的tag 2. vocabulary中增加注释 * BucketSampler增加一条错误检测 * 1.修改ClipGradientCallback的bug;删除LRSchedulerCallback中的print,之后应该传入pbar进行打印;2.增加MLP注释 * update MLP module * 增加metric注释;修改trainer save过程中的bug * Update README.md fix tutorial link * Add ENAS (Efficient Neural Architecture Search) * add ignore_type in DataSet.add_field * * AutoPadder will not pad when dtype is None * add ignore_type in DataSet.apply * 修复fieldarray中padder潜在bug * 修复crf中typo; 以及可能导致数值不稳定的地方 * 修复CRF中可能存在的bug * change two default init arguments of Trainer into None * Changes to Callbacks: * 给callback添加给定几个只读属性 * 通过manager设置这些属性 * 代码优化,减轻@transfer的负担 * * 将enas相关代码放到automl目录下 * 修复fast_param_mapping的一个bug * Trainer添加自动创建save目录 * Vocabulary的打印,显示内容 * * 给vocabulary添加遍历方法 * 修复CRF为负数的bug * add SQuAD metric * add sigmoid activate function in MLP * - add star transformer model - add ConllLoader, for all kinds of conll-format files - add JsonLoader, for json-format files - add SSTLoader, for SST-2 & SST-5 - change Callback interface - fix batch multi-process when killed - add README to list models and their performance * - fix test * - fix callback & tests * - update README * 修改部分bug;调整callback * 准备发布0.4.0版本“ * update readme * support parallel loss * 防止多卡的情况导致无法正确计算loss“ * update advance_tutorial jupyter notebook * 1. 在embedding_loader中增加新的读取函数load_with_vocab(), load_without_vocab, 比之前的函数改变主要在(1)不再需要传入embed_dim(2)自动判断当前是word2vec还是glove. 2. vocabulary增加from_dataset(), index_dataset()函数。避免需要多行写index dataset的问题。 3. 在utils中新增一个cache_result()修饰器,用于cache函数的返回值。 4. callback中新增update_every属性 * 1.DataSet.apply()报错时提供错误的index 2.Vocabulary.from_dataset(), index_dataset()提供报错时的vocab顺序 3.embedloader在embed读取时遇到不规则的数据跳过这一行. * update attention * doc tools * fix some doc errors * 修改为中文注释,增加viterbi解码方法 * 样例版本 * - add pad sequence for lstm - add csv, conll, json filereader - update dataloader - remove useless dataloader - fix trainer loss print - fix tests * - fix test_tutorial * 注释增加 * 测试文档 * 本地暂存 * 本地暂存 * 修改文档的顺序 * - add document * 本地暂存 * update pooling * update bert * update documents in MLP * update documents in snli * combine self attention module to attention.py * update documents on losses.py * 对DataSet的文档进行更新 * update documents on metrics * 1. 删除了LSTM中print的内容; 2. 将Trainer和Tester的use_cuda修改为了device; 3.补充Trainer的文档 * 增加对Trainer的注释 * 完善了trainer,callback等的文档; 修改了部分代码的命名以使得代码从文档中隐藏 * update char level encoder * update documents on embedding.py * - update doc * 补充注释,并修改部分代码 * - update doc - add get_embeddings * 修改了文档配置项 * 修改embedding为init_embed初始化 * 1.增加对Trainer和Tester的多卡支持; * - add test - fix jsonloader * 删除了注释教程 * 给 dataset 增加了get_field_names * 修复bug * - add Const - fix bugs * 修改部分注释 * - add model runner for easier test models - add model tests * 修改了 docs 的配置和架构 * 修改了核心部分的一大部分文档,TODO: 1. 完善 trainer 和 tester 部分的文档 2. 研究注释样例与测试 * core部分的注释基本检查完成 * 修改了 io 部分的注释 * 全部改为相对路径引用 * 全部改为相对路径引用 * small change * 1. 从安装文件中删除api/automl的安装 2. metric中存在seq_len的bug 3. sampler中存在命名错误,已修改 * 修复 bug :兼容 cpu 版本的 PyTorch TODO:其它地方可能也存在类似的 bug * 修改文档中的引用部分 * 把 tqdm.autonotebook 换成tqdm.auto * - fix batch & vocab * 上传了文档文件 *.rst * 上传了文档文件和若干 TODO * 讨论并整合了若干模块 * core部分的测试和一些小修改 * 删除了一些冗余文档 * update init files * update const files * update const files * 增加cnn的测试 * fix a little bug * - update attention - fix tests * 完善测试 * 完成快速入门教程 * 修改了sequence_modeling 命名为 sequence_labeling 的文档 * 重新 apidoc 解决改名的遗留问题 * 修改文档格式 * 统一不同位置的seq_len_to_mask, 现统一到core.utils.seq_len_to_mask * 增加了一行提示 * 在文档中展示 dataset_loader * 提示 Dataset.read_csv 会被 CSVLoader 替换 * 完成 Callback 和 Trainer 之间的文档 * index更新了部分 * 删除冗余的print * 删除用于分词的metric,因为有可能引起错误 * 修改文档中的中文名称 * 完成了详细介绍文档 * tutorial 的 ipynb 文件 * 修改了一些介绍文档 * 修改了 models 和 modules 的主页介绍 * 加上了 titlesonly 这个设置 * 修改了模块文档展示的标题 * 修改了 core 和 io 的开篇介绍 * 修改了 modules 和 models 开篇介绍 * 使用 .. todo:: 隐藏了可能被抽到文档中的 TODO 注释 * 修改了一些注释 * delete an old metric in test * 修改 tutorials 的测试文件 * 把暂不发布的功能移到 legacy 文件夹 * 删除了不能运行的测试 * 修改 callback 的测试文件 * 删除了过时的教程和测试文件 * cache_results 参数的修改 * 修改 io 的测试文件; 删除了一些过时的测试 * 修复bug * 修复无法通过test_utils.py的测试 * 修复与pytorch1.1中的padsequence的兼容问题; 修改Trainer的pbar * 1. 修复metric中的bug; 2.增加metric测试 * add model summary * 增加别名 * 删除encoder中的嵌套层 * 修改了 core 部分 import 的顺序,__all__ 暴露的内容 * 修改了 models 部分 import 的顺序,__all__ 暴露的内容 * 修改了文件名 * 修改了 modules 模块的__all__ 和 import * fix var runn * 增加vocab的clear方法 * 一些符合 PEP8 的微调 * 更新了cache_results的例子 * 1. 对callback中indices潜在None作出提示;2.DataSet支持通过List进行index * 修改了一个typo * 修改了 README.md * update documents on bert * update documents on encoder/bert * 增加一个fitlog callback,实现与fitlog实验记录 * typo * - update dataset_loader * 增加了到 fitlog 文档的链接。 * 增加了 DataSet Loader 的文档 * - add star-transformer reproduction
281 lines
8.9 KiB
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281 lines
8.9 KiB
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"collapsed": true
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},
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"source": [
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"# 快速入门"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str}"
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]
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},
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"execution_count": 1,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP.io import CSVLoader\n",
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"\n",
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"loader = CSVLoader(headers=('raw_sentence', 'label'), sep='\\t')\n",
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"dataset = loader.load(\"./sample_data/tutorial_sample_dataset.csv\")\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str,\n",
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"'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'words': ['a', 'series', 'of', 'escapades', 'demonstrating', 'the', 'adage', 'that', 'what', 'is', 'good', 'for', 'the', 'goose', 'is', 'also', 'good', 'for', 'the', 'gander', ',', 'some', 'of', 'which', 'occasionally', 'amuses', 'but', 'none', 'of', 'which', 'amounts', 'to', 'much', 'of', 'a', 'story', '.'] type=list}"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 将所有字母转为小写, 并所有句子变成单词序列\n",
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"dataset.apply(lambda x: x['raw_sentence'].lower(), new_field_name='sentence')\n",
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"dataset.apply(lambda x: x['sentence'].split(), new_field_name='words', is_input=True)\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str,\n",
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"'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list}"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP import Vocabulary\n",
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"\n",
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"# 使用Vocabulary类统计单词,并将单词序列转化为数字序列\n",
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"vocab = Vocabulary(min_freq=2).from_dataset(dataset, field_name='words')\n",
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"vocab.index_dataset(dataset, field_name='words',new_field_name='words')\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'raw_sentence': A series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'label': 1 type=str,\n",
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"'sentence': a series of escapades demonstrating the adage that what is good for the goose is also good for the gander , some of which occasionally amuses but none of which amounts to much of a story . type=str,\n",
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"'words': [4, 1, 6, 1, 1, 2, 1, 11, 153, 10, 28, 17, 2, 1, 10, 1, 28, 17, 2, 1, 5, 154, 6, 149, 1, 1, 23, 1, 6, 149, 1, 8, 30, 6, 4, 35, 3] type=list,\n",
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"'target': 1 type=int}"
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]
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 将label转为整数,并设置为 target\n",
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"dataset.apply(lambda x: int(x['label']), new_field_name='target', is_target=True)\n",
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"dataset[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"CNNText(\n",
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" (embed): Embedding(\n",
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" 177, 50\n",
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" (dropout): Dropout(p=0.0)\n",
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" )\n",
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" (conv_pool): ConvMaxpool(\n",
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" (convs): ModuleList(\n",
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" (0): Conv1d(50, 3, kernel_size=(3,), stride=(1,), padding=(2,))\n",
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" (1): Conv1d(50, 4, kernel_size=(4,), stride=(1,), padding=(2,))\n",
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" (2): Conv1d(50, 5, kernel_size=(5,), stride=(1,), padding=(2,))\n",
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" )\n",
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" )\n",
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" (dropout): Dropout(p=0.1)\n",
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" (fc): Linear(in_features=12, out_features=5, bias=True)\n",
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")"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP.models import CNNText\n",
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"model = CNNText((len(vocab),50), num_classes=5, padding=2, dropout=0.1)\n",
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"model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(62, 15)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# 分割训练集/验证集\n",
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"train_data, dev_data = dataset.split(0.2)\n",
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"len(train_data), len(dev_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"input fields after batch(if batch size is 2):\n",
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"\twords: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2, 26]) \n",
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"target fields after batch(if batch size is 2):\n",
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"\ttarget: (1)type:torch.Tensor (2)dtype:torch.int64, (3)shape:torch.Size([2]) \n",
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"\n",
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"training epochs started 2019-05-09-10-59-39\n"
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]
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},
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"HBox(children=(IntProgress(value=0, layout=Layout(flex='2'), max=20), HTML(value='')), layout=Layout(display='…"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Evaluation at Epoch 1/10. Step:2/20. AccuracyMetric: acc=0.333333\n",
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"\n",
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"Evaluation at Epoch 2/10. Step:4/20. AccuracyMetric: acc=0.533333\n",
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"\n",
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"Evaluation at Epoch 3/10. Step:6/20. AccuracyMetric: acc=0.533333\n",
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"\n",
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"Evaluation at Epoch 4/10. Step:8/20. AccuracyMetric: acc=0.533333\n",
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"\n",
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"Evaluation at Epoch 5/10. Step:10/20. AccuracyMetric: acc=0.6\n",
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"\n",
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"Evaluation at Epoch 6/10. Step:12/20. AccuracyMetric: acc=0.8\n",
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"\n",
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"Evaluation at Epoch 7/10. Step:14/20. AccuracyMetric: acc=0.8\n",
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"\n",
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"Evaluation at Epoch 8/10. Step:16/20. AccuracyMetric: acc=0.733333\n",
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"\n",
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"Evaluation at Epoch 9/10. Step:18/20. AccuracyMetric: acc=0.733333\n",
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"\n",
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"Evaluation at Epoch 10/10. Step:20/20. AccuracyMetric: acc=0.733333\n",
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"\n",
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"\n",
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"In Epoch:6/Step:12, got best dev performance:AccuracyMetric: acc=0.8\n",
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"Reloaded the best model.\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"{'best_eval': {'AccuracyMetric': {'acc': 0.8}},\n",
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" 'best_epoch': 6,\n",
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" 'best_step': 12,\n",
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" 'seconds': 0.22}"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from fastNLP import Trainer, CrossEntropyLoss, AccuracyMetric\n",
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"\n",
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"# 定义trainer并进行训练\n",
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"trainer = Trainer(model=model, train_data=train_data, dev_data=dev_data,\n",
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" loss=CrossEntropyLoss(), metrics=AccuracyMetric())\n",
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"trainer.train()"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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||
"pygments_lexer": "ipython3",
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"version": "3.6.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
|