Embeddings training process


I’m a little confused with the use of embeddings on the training process for the Encoder-decoder model. Can you please explain the main steps in the training and how the embeddings in this process are trained?


Hi Claudia,
there is related information in the following discussion:

Hi! I’m trying to feed my pre-trained embeddings (word2vec) in the training process but I get an error.

nohup: ignoring input
[2019-04-09 10:15:58,734 INFO]  * src vocab size = 9907
[2019-04-09 10:15:58,735 INFO]  * tgt vocab size = 7948
[2019-04-09 10:15:58,735 INFO] Building model...
Traceback (most recent call last):
  File "train.py", line 109, in <module>
  File "train.py", line 39, in main
    single_main(opt, 0)
  File "/home/speech/cmatosve/OpenNMT-py/onmt/train_single.py", line 83$
    model = build_model(model_opt, opt, fields, checkpoint)
  File "/home/speech/cmatosve/OpenNMT-py/onmt/model_builder.py", line 2$
    model = build_base_model(model_opt, fields, use_gpu(opt), checkpoin$
  File "/home/speech/cmatosve/OpenNMT-py/onmt/model_builder.py", line 2$
  File "/home/speech/cmatosve/OpenNMT-py/onmt/modules/embeddings.py", l$
    pretrained = torch.load(emb_file)
  File "/home/speech/cmatosve/OpenNMT-py/env/local/lib/python2.7/site-p$
    return _load(f, map_location, pickle_module)
  File "/home/speech/cmatosve/OpenNMT-py/env/local/lib/python2.7/site-p$
    magic_number = pickle_module.load(f)
cPickle.UnpicklingError: invalid load key, '9'.

Can you help me with that?