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。以下为PaddleNLP所支持的预训练Embedding模型，其名称用作paddlenlp.embeddings.TokenEmbedding的参数。
- 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)。
在使用方式这一节中，将介绍如何通过模型名称使用paddlenlp.embeddings.TokenEmbedding加载预训练模型。
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 | 无 | 无 |
| 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 |
| 语料 | 名称 |
|---|---|
| Google News | w2v.google_news.target.word-word.dim300.en |
| 语料 | 25维 | 50维 | 100维 | 200维 | 300 维 |
|---|---|---|---|---|---|
| Wiki2014 + GigaWord | 无 | 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 |
| 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 | 无 |
| 语料 | 名称 |
|---|---|
| Wiki2017 | fasttext.wiki-news.target.word-word.dim300.en |
| Crawl | fasttext.crawl.target.word-word.dim300.en |
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 | 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 |
- 感谢 Chinese-Word-Vectors提供Word2Vec中文预训练词向量。
- 感谢 GloVe Project提供的GloVe英文预训练词向量。
- 感谢 FastText Project提供的英文预训练词向量。
- 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.