如何在 Tensorboard 投影仪中可视化 Gensim Word2vec 嵌入

Question

按照gensim word2vec 嵌入教程，我训练了一个简单的 word2vec 模型：

from gensim.test.utils import common_texts
from gensim.models import Word2Vec
model = Word2Vec(sentences=common_texts, size=100, window=5, min_count=1, workers=4)
model.save("/content/word2vec.model")

我想使用 TensorBoard 中的嵌入投影仪将其可视化。gensim 文档中有另一个简单的教程。我在 Colab 中做了以下操作：

!python3 -m gensim.scripts.word2vec2tensor -i /content/word2vec.model -o /content/my_model

Traceback (most recent call last):
  File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/usr/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/usr/local/lib/python3.7/dist-packages/gensim/scripts/word2vec2tensor.py", line 94, in <module>
    word2vec2tensor(args.input, args.output, args.binary)
  File "/usr/local/lib/python3.7/dist-packages/gensim/scripts/word2vec2tensor.py", line 68, in word2vec2tensor
    model = gensim.models.KeyedVectors.load_word2vec_format(word2vec_model_path, binary=binary)
  File "/usr/local/lib/python3.7/dist-packages/gensim/models/keyedvectors.py", line 1438, in load_word2vec_format
    limit=limit, datatype=datatype)
  File "/usr/local/lib/python3.7/dist-packages/gensim/models/utils_any2vec.py", line 172, in _load_word2vec_format
    header = utils.to_unicode(fin.readline(), encoding=encoding)
  File "/usr/local/lib/python3.7/dist-packages/gensim/utils.py", line 355, in any2unicode
    return unicode(text, encoding, errors=errors)

UnicodeDecodeError: 'utf-8' codec can't decode byte 0x80 in position 0: invalid start byte

请注意，我确实在2018 年首先检查过这个完全相同的问题- 但接受的答案不再有效，因为 gensim 和 tensorflow 都已更新，因此我认为值得在 2021 年第四季度再次询问。

Answer 1

以原始 C word2vec 实现格式保存模型可以解决该问题 model.wv.save_word2vec_format("/content/word2vec.model")：：

from gensim.test.utils import common_texts
from gensim.models import Word2Vec
model = Word2Vec(sentences=common_texts, size=100, window=5, min_count=1, workers=4)
model.wv.save_word2vec_format("/content/word2vec.model")

有两种存储 word2vec 模型的格式gensim：原始 word2vec 实现的键控向量格式和另外存储隐藏权重、词汇频率等的格式。示例和详细信息可以在文档中找到。该脚本使用原始格式并使用以下代码word2vec2tensor.py加载模型。load_word2vec_format