33fea90b16
* fix data * datasets * embeddings * experimental * layers * metrics * ops * server * trainer * transformers * fix datasets
99 lines
4.1 KiB
Python
99 lines
4.1 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors and Chinese-Word-Vectors Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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URL_ROOT = "https://bj.bcebos.com/paddlenlp"
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EMBEDDING_URL_ROOT = URL_ROOT + "/models/embeddings"
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PAD_TOKEN = "[PAD]"
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UNK_TOKEN = "[UNK]"
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EMBEDDING_NAME_LIST = [
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# Word2Vec
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# baidu_encyclopedia
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"w2v.baidu_encyclopedia.target.word-word.dim300",
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"w2v.baidu_encyclopedia.target.word-character.char1-1.dim300",
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"w2v.baidu_encyclopedia.target.word-character.char1-2.dim300",
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"w2v.baidu_encyclopedia.target.word-character.char1-4.dim300",
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"w2v.baidu_encyclopedia.target.word-ngram.1-2.dim300",
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"w2v.baidu_encyclopedia.target.word-ngram.1-3.dim300",
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"w2v.baidu_encyclopedia.target.word-ngram.2-2.dim300",
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"w2v.baidu_encyclopedia.target.word-wordLR.dim300",
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"w2v.baidu_encyclopedia.target.word-wordPosition.dim300",
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"w2v.baidu_encyclopedia.target.bigram-char.dim300",
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"w2v.baidu_encyclopedia.context.word-word.dim300",
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"w2v.baidu_encyclopedia.context.word-character.char1-1.dim300",
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"w2v.baidu_encyclopedia.context.word-character.char1-2.dim300",
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"w2v.baidu_encyclopedia.context.word-character.char1-4.dim300",
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"w2v.baidu_encyclopedia.context.word-ngram.1-2.dim300",
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"w2v.baidu_encyclopedia.context.word-ngram.1-3.dim300",
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"w2v.baidu_encyclopedia.context.word-ngram.2-2.dim300",
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"w2v.baidu_encyclopedia.context.word-wordLR.dim300",
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"w2v.baidu_encyclopedia.context.word-wordPosition.dim300",
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# wikipedia
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"w2v.wiki.target.bigram-char.dim300",
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"w2v.wiki.target.word-char.dim300",
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"w2v.wiki.target.word-word.dim300",
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"w2v.wiki.target.word-bigram.dim300",
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# people_daily
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"w2v.people_daily.target.bigram-char.dim300",
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"w2v.people_daily.target.word-char.dim300",
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"w2v.people_daily.target.word-word.dim300",
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"w2v.people_daily.target.word-bigram.dim300",
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# weibo
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"w2v.weibo.target.bigram-char.dim300",
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"w2v.weibo.target.word-char.dim300",
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"w2v.weibo.target.word-word.dim300",
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"w2v.weibo.target.word-bigram.dim300",
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# sogou
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"w2v.sogou.target.bigram-char.dim300",
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"w2v.sogou.target.word-char.dim300",
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"w2v.sogou.target.word-word.dim300",
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"w2v.sogou.target.word-bigram.dim300",
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# zhihu
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"w2v.zhihu.target.bigram-char.dim300",
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"w2v.zhihu.target.word-char.dim300",
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"w2v.zhihu.target.word-word.dim300",
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"w2v.zhihu.target.word-bigram.dim300",
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# finacial
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"w2v.financial.target.bigram-char.dim300",
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"w2v.financial.target.word-char.dim300",
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"w2v.financial.target.word-word.dim300",
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"w2v.financial.target.word-bigram.dim300",
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# literature
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"w2v.literature.target.bigram-char.dim300",
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"w2v.literature.target.word-char.dim300",
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"w2v.literature.target.word-word.dim300",
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"w2v.literature.target.word-bigram.dim300",
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# siku
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"w2v.sikuquanshu.target.word-word.dim300",
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"w2v.sikuquanshu.target.word-bigram.dim300",
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# Mix-large
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"w2v.mixed-large.target.word-char.dim300",
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"w2v.mixed-large.target.word-word.dim300",
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# GOOGLE NEWS
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"w2v.google_news.target.word-word.dim300.en",
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# GloVe
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"glove.wiki2014-gigaword.target.word-word.dim50.en",
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"glove.wiki2014-gigaword.target.word-word.dim100.en",
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"glove.wiki2014-gigaword.target.word-word.dim200.en",
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"glove.wiki2014-gigaword.target.word-word.dim300.en",
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"glove.twitter.target.word-word.dim25.en",
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"glove.twitter.target.word-word.dim50.en",
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"glove.twitter.target.word-word.dim100.en",
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"glove.twitter.target.word-word.dim200.en",
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# FastText
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"fasttext.wiki-news.target.word-word.dim300.en",
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"fasttext.crawl.target.word-word.dim300.en",
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]
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