PaddleNLP Embedding API

介绍

PaddleNLP提供多个开源的预训练词向量模型,用户仅需在使用paddlenlp.embeddings.TokenEmbedding时,指定预训练模型的名称,即可加载相对应的预训练模型。以下将介绍TokenEmbeddign详细用法,并列出PaddleNLP所支持的预训练Embedding模型。

用法

TokenEmbedding参数

参数 类型 属性
embedding_name string 预训练embedding名称,可通过paddlenlp.embeddings.list_embedding_name()或Embedding 模型汇总查询。
unknown_token string unknown token。
unknown_token_vector list 或者 np.array 用来初始化unknown token对应的vector。默认为None(以正态分布方式初始化vector)
extended_vocab_path string 扩展词表的文件名路径。词表格式为一行一个词。
trainable bool 是否可训练。True表示Embedding可以更新参数,False为不可更新。

初始化

import paddle
from paddlenlp.embeddings import TokenEmbedding, list_embedding_name
paddle.set_device("cpu")

# 查看预训练embedding名称:
print(list_embedding_name()) # ['w2v.baidu_encyclopedia.target.word-word.dim300']

# 初始化TokenEmbedding, 预训练embedding没下载时会自动下载并加载数据
token_embedding = TokenEmbedding(embedding_name="w2v.baidu_encyclopedia.target.word-word.dim300")

# 查看token_embedding详情
print(token_embedding)

Object   type: <paddlenlp.embeddings.token_embedding.TokenEmbedding object at 0x7fda7eb5f290>
Unknown index: 635963
Unknown token: [UNK]
Padding index: 635964
Padding token: [PAD]
Parameter containing:
Tensor(shape=[635965, 300], dtype=float32, place=CPUPlace, stop_gradient=False,
       [[-0.24200200,  0.13931701,  0.07378800, ...,  0.14103900,  0.05592300, -0.08004800],
        [-0.08671700,  0.07770800,  0.09515300, ...,  0.11196400,  0.03082200, -0.12893000],
        [-0.11436500,  0.12201900,  0.02833000, ...,  0.11068700,  0.03607300, -0.13763499],
        ...,
        [ 0.02628800, -0.00008300, -0.00393500, ...,  0.00654000,  0.00024600, -0.00662600],
        [-0.00924490,  0.00652097,  0.01049327, ..., -0.01796000,  0.03498908, -0.02209341],
        [ 0.        ,  0.        ,  0.        , ...,  0.        ,  0.        ,  0.        ]])

查询embedding结果

test_token_embedding = token_embedding.search("中国")
print(test_token_embedding)
[[ 0.260801  0.1047    0.129453 -0.257317 -0.16152   0.19567  -0.074868
   0.361168  0.245882 -0.219141 -0.388083  0.235189  0.029316  0.154215
  -0.354343  0.017746  0.009028  0.01197  -0.121429  0.096542  0.009255
   ...,
  -0.260592 -0.019668 -0.063312 -0.094939  0.657352  0.247547 -0.161621
   0.289043 -0.284084  0.205076  0.059885  0.055871  0.159309  0.062181
   0.123634  0.282932  0.140399 -0.076253 -0.087103  0.07262 ]]

可视化embedding结果

使用深度学习可视化工具VisualDL的High Dimensional组件可以对embedding结果进行可视化展示,便于对其直观分析,步骤如下:

# 获取词表中前1000个单词
labels = token_embedding.vocab.to_tokens(list(range(0,1000)))
test_token_embedding = token_embedding.search(labels)

# 引入VisualDL的LogWriter记录日志
from visualdl import LogWriter

with LogWriter(logdir='./visualize') as writer:
    writer.add_embeddings(tag='test', mat=test_token_embedding, metadata=labels)

执行完毕后会在当前路径下生成一个visualize目录,并将日志存放在其中,我们在命令行启动VisualDL即可进行查看,启动命令为:

visualdl logdir ./visualize

启动后打开浏览器即可看到可视化结果

使用VisualDL除可视化embedding结果外,还可以对标量、图片、音频等进行可视化,有效提升训练调参效率。关于VisualDL更多功能和详细介绍,可参考VisualDL使用文档

计算词向量cosine相似度

score = token_embedding.cosine_sim("中国", "美国")
print(score) # 0.49586025

计算词向量内积

score = token_embedding.dot("中国", "美国")
print(score) # 8.611071

训练

以下为TokenEmbedding简单的组网使用方法。有关更多TokenEmbedding训练流程相关的使用方法,请参考Word Embedding with PaddleNLP

in_words = paddle.to_tensor([0, 2, 3])
input_embeddings = token_embedding(in_words)
linear = paddle.nn.Linear(token_embedding.embedding_dim, 20)
input_fc = linear(input_embeddings)
print(input_fc)
Tensor(shape=[3, 20], dtype=float32, place=CPUPlace, stop_gradient=False,
       [[ 0.        ,  0.        ,  0.        ,  ...,  0.        ,  0.        ,  0.        ],
        [-0.23473957,  0.17878169,  0.07215232,  ...,  0.03698236,  0.14291850,  0.05136518],
        [-0.42466098,  0.15017235, -0.04780108,  ..., -0.04995505,  0.15847842,  0.00025209]])

切词

from paddlenlp.data import JiebaTokenizer
tokenizer = JiebaTokenizer(vocab=token_embedding.vocab)
words = tokenizer.cut("中国人民")
print(words) # ['中国人', '民']

tokens = tokenizer.encode("中国人民")
print(tokens) # [12530, 1334]

预训练模型

以下将列举PaddleNLP支持的Embedding预训练模型。

  • 模型命名方式为:${训练模型}.${语料}.${词向量类型}.${co-occurrence type}.dim${维度}。

  • 模型有三种,分别是Word2Vec(w2v, skip-gram), GloVe(glove)和FastText(fasttext)。

中文词向量

以下预训练词向量由Chinese-Word-Vectors提供。

根据不同类型的上下文为每个语料训练多个目标词向量,第二列开始表示不同类型的上下文。以下为上下文类别:

  • Word表示训练时目标词预测的上下文是一个Word。

  • Word + N-gram表示训练时目标词预测的上下文是一个Word或者Ngram,其中bigram表示2-grams,ngram.1-2表示1-gram或者2-grams。

  • Word + Character表示训练时目标词预测的上下文是一个Word或者Character,其中word-character.char1-2表示上下文是1个或2个Character。

  • Word + Character + Ngram表示训练时目标词预测的上下文是一个Word、Character或者Ngram。bigram-char表示上下文是2-grams或者1个Character。

语料 Word Word + N-gram Word + Character Word + Character + N-gram
Baidu Encyclopedia 百度百科 w2v.baidu_encyclopedia.target.word-word.dim300 w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300 w2v.baidu_encyclopedia.target.word-character.char1-2.dim300 w2v.baidu_encyclopedia.target.bigram-char.dim300
Wikipedia_zh 中文维基百科 w2v.wiki.target.word-word.dim300 w2v.wiki.target.word-bigram.dim300 w2v.wiki.target.word-char.dim300 w2v.wiki.target.bigram-char.dim300
People's Daily News 人民日报 w2v.people_daily.target.word-word.dim300 w2v.people_daily.target.word-bigram.dim300 w2v.people_daily.target.word-char.dim300 w2v.people_daily.target.bigram-char.dim300
Sogou News 搜狗新闻 w2v.sogou.target.word-word.dim300 w2v.sogou.target.word-bigram.dim300 w2v.sogou.target.word-char.dim300 w2v.sogou.target.bigram-char.dim300
Financial News 金融新闻 w2v.financial.target.word-word.dim300 w2v.financial.target.word-bigram.dim300 w2v.financial.target.word-char.dim300 w2v.financial.target.bigram-char.dim300
Zhihu_QA 知乎问答 w2v.zhihu.target.word-word.dim300 w2v.zhihu.target.word-bigram.dim300 w2v.zhihu.target.word-char.dim300 w2v.zhihu.target.bigram-char.dim300
Weibo 微博 w2v.weibo.target.word-word.dim300 w2v.weibo.target.word-bigram.dim300 w2v.weibo.target.word-char.dim300 w2v.weibo.target.bigram-char.dim300
Literature 文学作品 w2v.literature.target.word-word.dim300 w2v.literature.target.word-bigram.dim300 w2v.literature.target.word-char.dim300 w2v.literature.target.bigram-char.dim300
Complete Library in Four Sections 四库全书 w2v.sikuquanshu.target.word-word.dim300 w2v.sikuquanshu.target.word-bigram.dim300
Mixed-large 综合 w2v.mixed-large.target.word-word.dim300 暂无 w2v.mixed-large.target.word-word.dim300 暂无

特别地,对于百度百科语料,在不同的 Co-occurrence类型下分别提供了目标词与上下文向量:

Co-occurrence 类型 目标词向量 上下文词向量
Word → Word w2v.baidu_encyclopedia.target.word-word.dim300 w2v.baidu_encyclopedia.context.word-word.dim300
Word → Ngram (1-2) w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300 w2v.baidu_encyclopedia.context.word-ngram.1-2.dim300
Word → Ngram (1-3) w2v.baidu_encyclopedia.target.word-ngram.1-3.dim300 w2v.baidu_encyclopedia.context.word-ngram.1-3.dim300
Ngram (1-2) → Ngram (1-2) w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300 w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300
Word → Character (1) w2v.baidu_encyclopedia.target.word-character.char1-1.dim300 w2v.baidu_encyclopedia.context.word-character.char1-1.dim300
Word → Character (1-2) w2v.baidu_encyclopedia.target.word-character.char1-2.dim300 w2v.baidu_encyclopedia.context.word-character.char1-2.dim300
Word → Character (1-4) w2v.baidu_encyclopedia.target.word-character.char1-4.dim300 w2v.baidu_encyclopedia.context.word-character.char1-4.dim300
Word → Word (left/right) w2v.baidu_encyclopedia.target.word-wordLR.dim300 w2v.baidu_encyclopedia.context.word-wordLR.dim300
Word → Word (distance) w2v.baidu_encyclopedia.target.word-wordPosition.dim300 w2v.baidu_encyclopedia.context.word-wordPosition.dim300

英文词向量

Word2Vec

语料 名称
Google News w2v.google_news.target.word-word.dim300.en

GloVe

语料 25维 50维 100维 200维 300 维
Wiki2014 + GigaWord glove.wiki2014-gigaword.target.word-word.dim50.en glove.wiki2014-gigaword.target.word-word.dim100.en glove.wiki2014-gigaword.target.word-word.dim200.en glove.wiki2014-gigaword.target.word-word.dim300.en
Twitter glove.twitter.target.word-word.dim25.en glove.twitter.target.word-word.dim50.en glove.twitter.target.word-word.dim100.en glove.twitter.target.word-word.dim200.en

FastText

语料 名称
Wiki2017 fasttext.wiki-news.target.word-word.dim300.en
Crawl fasttext.crawl.target.word-word.dim300.en

使用方式

以上所述的模型名称可直接以参数形式传入padddlenlp.embeddings.TokenEmbedding,加载相对应的模型。比如要加载语料为Wiki2017,通过FastText训练的预训练模型(fasttext.wiki-news.target.word-word.dim300.en),只需执行以下代码:

import paddle
from paddlenlp.embeddings import TokenEmbedding

token_embedding = TokenEmbedding(embedding_name="fasttext.wiki-news.target.word-word.dim300.en")

模型信息

模型 文件大小 词表大小
w2v.baidu_encyclopedia.target.word-word.dim300 678.21 MB 635965
w2v.baidu_encyclopedia.target.word-character.char1-1.dim300 679.15 MB 636038
w2v.baidu_encyclopedia.target.word-character.char1-2.dim300 679.30 MB 636038
w2v.baidu_encyclopedia.target.word-character.char1-4.dim300 679.51 MB 636038
w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300 679.48 MB 635977
w2v.baidu_encyclopedia.target.word-ngram.1-3.dim300 671.27 MB 628669
w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300 7.28 GB 6969069
w2v.baidu_encyclopedia.target.word-wordLR.dim300 678.22 MB 635958
w2v.baidu_encyclopedia.target.word-wordPosition.dim300 679.32 MB 636038
w2v.baidu_encyclopedia.target.bigram-char.dim300 679.29 MB 635976
w2v.baidu_encyclopedia.context.word-word.dim300 677.74 MB 635952
w2v.baidu_encyclopedia.context.word-character.char1-1.dim300 678.65 MB 636200
w2v.baidu_encyclopedia.context.word-character.char1-2.dim300 844.23 MB 792631
w2v.baidu_encyclopedia.context.word-character.char1-4.dim300 1.16 GB 1117461
w2v.baidu_encyclopedia.context.word-ngram.1-2.dim300 7.25 GB 6967598
w2v.baidu_encyclopedia.context.word-ngram.1-3.dim300 5.21 GB 5000001
w2v.baidu_encyclopedia.context.word-ngram.2-2.dim300 7.26 GB 6968998
w2v.baidu_encyclopedia.context.word-wordLR.dim300 1.32 GB 1271031
w2v.baidu_encyclopedia.context.word-wordPosition.dim300 6.47 GB 6293920
w2v.wiki.target.bigram-char.dim300 375.98 MB 352274
w2v.wiki.target.word-char.dim300 375.52 MB 352223
w2v.wiki.target.word-word.dim300 374.95 MB 352219
w2v.wiki.target.word-bigram.dim300 375.72 MB 352219
w2v.people_daily.target.bigram-char.dim300 379.96 MB 356055
w2v.people_daily.target.word-char.dim300 379.45 MB 355998
w2v.people_daily.target.word-word.dim300 378.93 MB 355989
w2v.people_daily.target.word-bigram.dim300 379.68 MB 355991
w2v.weibo.target.bigram-char.dim300 208.24 MB 195199
w2v.weibo.target.word-char.dim300 208.03 MB 195204
w2v.weibo.target.word-word.dim300 207.94 MB 195204
w2v.weibo.target.word-bigram.dim300 208.19 MB 195204
w2v.sogou.target.bigram-char.dim300 389.81 MB 365112
w2v.sogou.target.word-char.dim300 389.89 MB 365078
w2v.sogou.target.word-word.dim300 388.66 MB 364992
w2v.sogou.target.word-bigram.dim300 388.66 MB 364994
w2v.zhihu.target.bigram-char.dim300 277.35 MB 259755
w2v.zhihu.target.word-char.dim300 277.40 MB 259940
w2v.zhihu.target.word-word.dim300 276.98 MB 259871
w2v.zhihu.target.word-bigram.dim300 277.53 MB 259885
w2v.financial.target.bigram-char.dim300 499.52 MB 467163
w2v.financial.target.word-char.dim300 499.17 MB 467343
w2v.financial.target.word-word.dim300 498.94 MB 467324
w2v.financial.target.word-bigram.dim300 499.54 MB 467331
w2v.literature.target.bigram-char.dim300 200.69 MB 187975
w2v.literature.target.word-char.dim300 200.44 MB 187980
w2v.literature.target.word-word.dim300 200.28 MB 187961
w2v.literature.target.word-bigram.dim300 200.59 MB 187962
w2v.sikuquanshu.target.word-word.dim300 20.70 MB 19529
w2v.sikuquanshu.target.word-bigram.dim300 20.77 MB 19529
w2v.mixed-large.target.word-char.dim300 1.35 GB 1292552
w2v.mixed-large.target.word-word.dim300 1.35 GB 1292483
w2v.google_news.target.word-word.dim300.en 1.61 GB 3000000
glove.wiki2014-gigaword.target.word-word.dim50.en 73.45 MB 400002
glove.wiki2014-gigaword.target.word-word.dim100.en 143.30 MB 400002
glove.wiki2014-gigaword.target.word-word.dim200.en 282.97 MB 400002
glove.wiki2014-gigaword.target.word-word.dim300.en 422.83 MB 400002
glove.twitter.target.word-word.dim25.en 116.92 MB 1193516
glove.twitter.target.word-word.dim50.en 221.64 MB 1193516
glove.twitter.target.word-word.dim100.en 431.08 MB 1193516
glove.twitter.target.word-word.dim200.en 848.56 MB 1193516
fasttext.wiki-news.target.word-word.dim300.en 541.63 MB 999996
fasttext.crawl.target.word-word.dim300.en 1.19 GB 2000002

致谢

参考论文

  • Li, Shen, et al. "Analogical reasoning on chinese morphological and semantic relations." arXiv preprint arXiv:1805.06504 (2018).

  • Qiu, Yuanyuan, et al. "Revisiting correlations between intrinsic and extrinsic evaluations of word embeddings." Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. Springer, Cham, 2018. 209-221.

  • Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation.

  • T. Mikolov, E. Grave, P. Bojanowski, C. Puhrsch, A. Joulin. Advances in Pre-Training Distributed Word Representations.