Nmt: How to use the pretrained word representations (word2vec) ?

[yapingzhao]

Hi,
1.How to use the pretrained word representations (word2vec) to initialize the embedding weights?
2.The embedding principle in nmt/tensorflow is CBOW or skip-gram?
Looking forward to your advice or answers.
If you have any suggestions, please give me some suggestions, thank you very much!
Best regards,
Thank you.

[Lapis-Hong]

set this option --embed_prefix to use your embedding file

[yapingzhao]

thank you very much!

[yapingzhao]

@Lapis-Hong Hi,
I want to use the --embed_prefix option to use my own trained word vector (word2vec).
But,An error occurred while I was running the command
python -m nmt.nmt
--src=vi --tgt=en
--num_units=300
--embed_prefix=nmt/nmt_data/vector
error:
FailedPreconditionError (see above for traceback): HashTable has different value for same key. Key ᠤ has 23 and trying to add value 1518
[[Node: string_to_index/hash_table/table_init = InitializeTableFromTextFileV2[delimiter="t", key_index=-2, value_index=-1, vocab_size=-1, _device="/job:localhost/replica:0/task:0/device:CPU:0"](string_to_index/hash_table, string_to_index/hash_table/table_init/asset_filepath)]]

If you have any suggestions, please give me some suggestions, thank you very much!