diff --git a/PaddleNLP/docs/embeddings.md b/PaddleNLP/docs/embeddings.md index 589c34c4aee11df171659bc54fd5f4a239fbeb3d..f3aceafc458200233e5fc2161cf07f18492cc0f6 100644 --- a/PaddleNLP/docs/embeddings.md +++ b/PaddleNLP/docs/embeddings.md @@ -1,3 +1,12 @@ +- [Embedding 模型汇总](#embedding-模型汇总) + - [中文词向量](#中文词向量) + - [英文词向量](#英文词向量) + - [GloVe](#glove) + - [FastText](#fasttext) + - [模型信息](#模型信息) + - [致谢](#致谢) + - [参考论文](#参考论文) + # Embedding 模型汇总 PaddleNLP提供多个开源的预训练Embedding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}。训练模型有三种，分别是Word2Vec(w2v, 使用skip-gram模型训练), GloVe(glove)和FastText(fasttext)。 @@ -42,11 +51,91 @@ PaddleNLP提供多个开源的预训练Embedding模型，用户仅需在使用`p ## 英文词向量 -待更新。 +### GloVe + +| 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 | +| 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 | 无 | + +### FastText + +| 语料 | 名称 | +|------|------| +| Wiki2017 | fasttext.wiki-news.target.word-word.dim300.en | +| Crawl | fasttext.crawl.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 | +| 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 -- 感谢 [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)提供Word2Vec中文Embedding来源。 +- 感谢 [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)提供Word2Vec中文Embedding预训练模型，[GloVe Project](https://nlp.stanford.edu/projects/glove)提供的GloVe英文Embedding预训练模型，[FastText Project](https://fasttext.cc/docs/en/english-vectors.html)提供的fasttext英文预训练模型。 ## 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 diff --git a/PaddleNLP/examples/word_embedding/README.md b/PaddleNLP/examples/word_embedding/README.md index 898db75ebd982d9c4e407c319d191544f4676258..d25d70228f4a59e06ec17b7526c9b26d2d8e8d9a 100644 --- a/PaddleNLP/examples/word_embedding/README.md +++ b/PaddleNLP/examples/word_embedding/README.md @@ -2,7 +2,7 @@ ## 简介 -PaddleNLP已预置多个公开的预训练Embedding，用户可以通过使用`paddle.embeddings.TokenEmbedding`接口加载预训练Embedding，从而提升训练效果。以下通过文本分类训练的例子展示`paddle.embeddings.TokenEmbedding`对训练提升的效果。 +PaddleNLP已预置多个公开的预训练Embedding，用户可以通过使用`paddlenlp.embeddings.TokenEmbedding`接口加载预训练Embedding，从而提升训练效果。以下通过文本分类训练的例子展示`paddlenlp.embeddings.TokenEmbedding`对训练提升的效果。 ## 快速开始 @@ -13,17 +13,14 @@ PaddleNLP已预置多个公开的预训练Embedding，用户可以通过使用`p 本项目依赖于 PaddlePaddle 2.0 及以上版本，请参考 [安装指南](http://www.paddlepaddle.org/#quick-start) 进行安装 -* PaddleNLP 安装 - - ```shell - pip install paddlenlp - ``` - * 环境依赖 - 本项目依赖于jieba分词，请在运行本项目之前，安装jieba，如`pip install -U jieba` + - python >= 3.6 + - paddlepaddle-gpu >= 2.0.0-rc1 - Python的版本要求 3.6+，其它环境请参考 PaddlePaddle [安装说明](https://www.paddlepaddle.org.cn/install/quick/zh/2.0rc-linux-docker) 部分的内容 + ``` + pip install paddlenlp==2.0.0b + ``` ### 下载词表 @@ -35,24 +32,27 @@ wget https://paddlenlp.bj.bcebos.com/data/dict.txt ### 启动训练 -我们以中文情感分类公开数据集ChnSentiCorp为示例数据集，可以运行下面的命令，在训练集（train.tsv）上进行模型训练，并在开发集（dev.tsv）验证。实验输出的日志保存在use_token_embedding.txt和use_normal_embedding.txt。使用PaddlePaddle框架的Embedding在ChnSentiCorp下非常容易过拟合，因此调低了它的学习率。 +我们以中文情感分类公开数据集ChnSentiCorp为示例数据集，可以运行下面的命令，在训练集（train.tsv）上进行模型训练，并在验证集（dev.tsv）验证。 CPU 启动： ``` -nohup python train.py --vocab_path='./dict.txt' --use_gpu=False --lr=5e-4 --batch_size=64 --epochs=20 --use_token_embedding=True --vdl_dir='./vdl_dir' >use_token_embedding.txt 2>&1 & +# 使用paddlenlp.embeddings.TokenEmbedding +python train.py --vocab_path='./dict.txt' --use_gpu=False --lr=5e-4 --batch_size=64 --epochs=20 --use_token_embedding=True --vdl_dir='./vdl_dir' -nohup python train.py --vocab_path='./dict.txt' --use_gpu=False --lr=1e-4 --batch_size=64 --epochs=20 --use_token_embedding=False --vdl_dir='./vdl_dir'>use_normal_embedding.txt 2>&1 & +# 使用paddle.nn.Embedding +python train.py --vocab_path='./dict.txt' --use_gpu=False --lr=1e-4 --batch_size=64 --epochs=20 --use_token_embedding=False --vdl_dir='./vdl_dir' ``` GPU 启动： ``` export CUDA_VISIBLE_DEVICES=0 -nohup python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=5e-4 --batch_size=64 --epochs=20 --use_token_embedding=True --vdl_dir='./vdl_dir' > use_token_embedding.txt 2>&1 & +# 使用paddlenlp.embeddings.TokenEmbedding +python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=5e-4 --batch_size=64 --epochs=20 --use_token_embedding=True --vdl_dir='./vdl_dir' -# 如显存不足，可以先等第一个训练完成再启动该训练 -nohup python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=1e-4 --batch_size=64 --epochs=20 --use_token_embedding=False --vdl_dir='./vdl_dir' > use_normal_embedding.txt 2>&1 & +# 使用paddle.nn.Embedding +python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=1e-4 --batch_size=64 --epochs=20 --use_token_embedding=False --vdl_dir='./vdl_dir' ``` 以上参数表示： @@ -62,7 +62,7 @@ nohup python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=1e-4 --batch * `lr`: 学习率， 默认为5e-4。 * `batch_size`: 运行一个batch大小，默认为64。 * `epochs`: 训练轮次，默认为5。 -* `use_token_embedding`: 是否使用PaddleNLP的TokenEmbedding，默认为True。 +* `use_token_embedding`: 是否使用`paddlenlp.embeddings.TokenEmbedding`，默认为True。 * `vdl_dir`: VisualDL日志目录。训练过程中的VisualDL信息会在该目录下保存。默认为`./vdl_dir` 该脚本还提供以下参数： @@ -76,14 +76,14 @@ nohup python train.py --vocab_path='./dict.txt' --use_gpu=True --lr=1e-4 --batch 推荐使用VisualDL查看实验对比。以下为VisualDL的启动命令，其中logdir参数指定的目录需要与启动训练时指定的`vdl_dir`相同。（更多VisualDL的用法，可参考[VisualDL使用指南](https://github.com/PaddlePaddle/VisualDL#2-launch-panel)） ``` -nohup visualdl --logdir ./vdl_dir --port 8888 --host 0.0.0.0 & +visualdl --logdir ./vdl_dir --port 8888 --host 0.0.0.0 ``` ### 训练效果对比 在Chrome浏览器输入 `ip:8888` (ip为启动VisualDL机器的IP)。 -以下为示例实验效果对比图，蓝色是使用`paddle.embeddings.TokenEmbedding`进行的实验，绿色是使用没有加载预训练模型的Embedding进行的实验。可以看到，使用`paddle.embeddings.TokenEmbedding`的训练，其验证acc变化趋势上升，并收敛于0.90左右，收敛后相对平稳，不容易过拟合。而没有使用`paddle.embeddings.TokenEmbedding`的训练，其验证acc变化趋势向下，并收敛于0.86左右。从示例实验可以观察到，使用`paddle.embedding.TokenEmbedding`能提升训练效果。 +以下为示例实验效果对比图，蓝色是使用`paddlenlp.embeddings.TokenEmbedding`进行的实验，绿色是使用没有加载预训练模型的Embedding进行的实验。可以看到，使用`paddlenlp.embeddings.TokenEmbedding`的训练，其验证acc变化趋势上升，并收敛于0.90左右，收敛后相对平稳，不容易过拟合。而没有使用`paddlenlp.embeddings.TokenEmbedding`的训练，其验证acc变化趋势向下，并收敛于0.86左右。从示例实验可以观察到，使用`paddlenlp.embedding.TokenEmbedding`能提升训练效果。 Eval Acc： @@ -95,8 +95,10 @@ Eval Acc： | paddelnlp.embeddings.TokenEmbedding | 0.9082 | ## 致谢 -- 感谢 [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)提供Word2Vec中文Embedding来源。 +- 感谢 [Chinese-Word-Vectors](https://github.com/Embedding/Chinese-Word-Vectors)提供Word2Vec中文Embedding预训练模型，[GloVe Project](https://nlp.stanford.edu/projects/glove)提供的GloVe英文Embedding预训练模型，[FastText Project](https://fasttext.cc/docs/en/english-vectors.html)提供的fasttext英文预训练模型。 ## 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 diff --git a/PaddleNLP/paddlenlp/embeddings/constant.py b/PaddleNLP/paddlenlp/embeddings/constant.py index 454b4c2265248edc30b6e7dd134e68fffb11a005..1ab5ccd21fe399d405311ac533724dcb3f3623d5 100644 --- a/PaddleNLP/paddlenlp/embeddings/constant.py +++ b/PaddleNLP/paddlenlp/embeddings/constant.py @@ -83,5 +83,18 @@ EMBEDDING_NAME_LIST = [ "w2v.sikuquanshu.target.word-bigram.dim300", # Mix-large "w2v.mixed-large.target.word-char.dim300", - "w2v.mixed-large.target.word-word.dim300" + "w2v.mixed-large.target.word-word.dim300", + + # GloVe + "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", + # FastText + "fasttext.wiki-news.target.word-word.dim300.en", + "fasttext.crawl.target.word-word.dim300.en" ] diff --git a/PaddleNLP/paddlenlp/embeddings/token_embedding.py b/PaddleNLP/paddlenlp/embeddings/token_embedding.py index 463326dcc0aead29ec6e85e2748101857879b6f6..c76fb9fba121819963aeb063ba1aae88083a23c5 100644 --- a/PaddleNLP/paddlenlp/embeddings/token_embedding.py +++ b/PaddleNLP/paddlenlp/embeddings/token_embedding.py @@ -33,10 +33,33 @@ __all__ = ['list_embedding_name', 'TokenEmbedding'] def list_embedding_name(): + """ + List all names of pretrained embedding models paddlenlp provides. + """ return list(EMBEDDING_NAME_LIST) class TokenEmbedding(nn.Embedding): + """ + A `TokenEmbedding` can load pre-trained embedding model which paddlenlp provides by + specifying embedding name. Furthermore, a `TokenEmbedding` can load extended vocabulary + by specifying extended_vocab_path. + + Args: + embedding_name (object: `str`, optional, default to `w2v.baidu_encyclopedia.target.word-word.dim300`): + The pre-trained embedding model name. Use `paddlenlp.embeddings.list_embedding_name()` to + show which embedding model we have alreaady provide. + unknown_token (object: `str`, optional, default to `[UNK]`): + Specifying unknown token as unknown_token. + unknown_token_vector (object: list, optional, default to `None`): + To initialize the vector of unknown token. If it's none, use normal distribution to + initialize the vector of unknown token. + extended_vocab_path (object: `str`, optional, default to `None`): + The file path of extended vocabulary. + trainable (object: `bool`, optional, default to True): + Whether the weight of embedding can be trained. + """ + def __init__(self, embedding_name=EMBEDDING_NAME_LIST[0], unknown_token=UNK_TOKEN, @@ -49,7 +72,7 @@ class TokenEmbedding(nn.Embedding): url = osp.join(EMBEDDING_URL_ROOT, embedding_name + ".tar.gz") get_path_from_url(url, EMBEDDING_HOME) - logger.info("Loading embedding vector...") + logger.info("Loading token embedding...") vector_np = np.load(vector_path) self.embedding_dim = vector_np['embedding'].shape[1] self.unknown_token = unknown_token @@ -81,7 +104,7 @@ class TokenEmbedding(nn.Embedding): self.weight.set_value(embedding_table) self.set_trainable(trainable) logger.info("Finish loading embedding vector.") - s = "Token Embedding brief:\ + s = "Token Embedding info:\ \nUnknown index: {}\ \nUnknown token: {}\ \nPadding index: {}\ @@ -92,6 +115,9 @@ class TokenEmbedding(nn.Embedding): logger.info(s) def _init_without_extend_vocab(self, vector_np, pad_vector, unk_vector): + """ + Construct index to word list, word to index dict and embedding weight. + """ self._idx_to_word = list(vector_np['vocab']) self._idx_to_word.append(self.unknown_token) self._idx_to_word.append(PAD_TOKEN) @@ -113,6 +139,10 @@ class TokenEmbedding(nn.Embedding): def _extend_vocab(self, extended_vocab_path, vector_np, pad_vector, unk_vector): + """ + Construct index to word list, word to index dict and embedding weight using + extended vocab. + """ logger.info("Start extending vocab.") extend_vocab_list = self._read_vocab_list_from_file(extended_vocab_path) extend_vocab_set = set(extend_vocab_list) @@ -182,18 +212,37 @@ class TokenEmbedding(nn.Embedding): return embedding_table def set_trainable(self, trainable): + """ + Set the weight of embedding can be trained. + Args: + trainable (object: `bool`, required): + Whether the weight of embedding can be trained. + """ self.weight.stop_gradient = not trainable def search(self, words): + """ + Get the vectors of specifying words. + Args: + words (object: `list` or `str` or `int`, required): The words which need to be searched. + Returns: + word_vector (object: `numpy.array`): The vectors of specifying words. + """ idx_list = self.get_idx_list_from_words(words) idx_tensor = paddle.to_tensor(idx_list) return self(idx_tensor).numpy() def get_idx_from_word(self, word): + """ + Get the index of specifying word by searching word_to_idx dict. + """ return get_idx_from_word(word, self.vocab.token_to_idx, self.unknown_token) def get_idx_list_from_words(self, words): + """ + Get the index list of specifying words by searching word_to_idx dict. + """ if isinstance(words, str): idx_list = [self.get_idx_from_word(words)] elif isinstance(words, int): @@ -217,23 +266,50 @@ class TokenEmbedding(nn.Embedding): return calc_kernel(embedding_a, embedding_b) def dot(self, word_a, word_b): + """ + Calculate the scalar product of 2 words. + Args: + word_a (object: `str`, required): The first word string. + word_b (object: `str`, required): The second word string. + Returns: + The scalar product of 2 words. + """ dot = self._dot_np return self._calc_word(word_a, word_b, lambda x, y: dot(x, y)) def cosine_sim(self, word_a, word_b): + """ + Calculate the cosine similarity of 2 words. + Args: + word_a (object: `str`, required): The first word string. + word_b (object: `str`, required): The second word string. + Returns: + The cosine similarity of 2 words. + """ dot = self._dot_np return self._calc_word( word_a, word_b, lambda x, y: dot(x, y) / (np.sqrt(dot(x, x)) * np.sqrt(dot(y, y)))) def _construct_word_to_idx(self, idx_to_word): + """ + Construct word to index dict. + Args: + idx_to_word (object: 'list', required): + Returns: + word_to_idx (object: `dict`): The word to index dict constructed by idx_to_word. + """ word_to_idx = {} for i, word in enumerate(idx_to_word): word_to_idx[word] = i return word_to_idx def __repr__(self): - s = "Object type: {}\ + """ + Returns: + info (object: `str`): The token embedding infomation. + """ + info = "Object type: {}\ \nUnknown index: {}\ \nUnknown token: {}\ \nPadding index: {}\ @@ -242,4 +318,4 @@ class TokenEmbedding(nn.Embedding): super(TokenEmbedding, self).__repr__(), self._word_to_idx[self.unknown_token], self.unknown_token, self._word_to_idx[PAD_TOKEN], PAD_TOKEN, self.weight) - return s + return info