Hello, I want to ask some question about embedding. I wonder how does the Embedding from WordEmbedder class trained? I looked at the source code but still don't understand. And is it possible to export the embedding from the trained WordEmbedder class into the pretrained word embedding format? Thanks in advance
Edit: As an additional information, I trained my model using the Transformer class
Edit: As an additional information, I trained my model using the Transformer class
Hi @guillaumekln I have trained a good transformer model but I saw some lengthy inference examples. for example, the source query is 10-characters long, but the generated inference by the model can be 200 characters, some portions are just repeating. Is there a way to control the length of the inference to be generated by the transformer model?
(BTW, I had the sequence_length set to 80 during training. ）
Hi. Look for
maximum_decoding_length in the parameters: https://opennmt.net/OpenNMT-tf/configuration.html. Maybe length_penalty can also help.
However, when this issue happens it usually means the input was unexpected for the model and the training may lack this type of examples.
Hi @guillaumekln , thanks so much for your response, it's very helpful!! Just a couple of quick follow-up questions: 1. I think the maximum_decoding_length and length_penalty are parameters affecting both training and inferencing, am I correct? 2. does the 'length' here refer to the number of characters, the number or subwords, or the number of words?
@guillaumekln for another parameter 'maximum_features_length', does the length here also refer to the number of tokens, as defined by the tokenization?
Hi @guillaumekln I've trained a Transformer model using onmt and have also run infer command to do the inference. all went good. now I have a requirement to bring the model into production which supports tensorflow, so I exported the model into TF SavedModel format (https://opennmt.net/OpenNMT-tf/serving.html). However for the inferencing part, some beam or decoder related parameters may affect the inference latency, such as beam_width, n_best, maximum_decoding_length, how I could incorporate these onmt parameters given an exported TF SavedModel package, in the tensorflow production environment? onmt inferencing refers to a config file where we specify those parameters, would a similar config file needed for doing inference given a SavedModel on a machine where only tensorflow is supported?
Thanks in advance!
One additional question @guillaumekln :) I read the comment for the parameter 'sampling_topk': # (optional) Sample predictions from the top K most likely tokens (requires beam_width to 1). If 0, sample from the full output distribution (default: 1).
My question is: With greedy search model, how should I set "sampling_topk" to maximize the speed?
My question is: With greedy search model, how should I set "sampling_topk" to maximize the speed?
Just should be the default value 1, right?
Hi. See this code example: https://forum.opennmt.net/t/problem-in-using-extract-embeddings-lua/3779/2
Thank you @guillaumekln ! Got it.
Not sure though about the one in the image. I printed the size of my vocab and the embedding shape. What could be this difference? 24000 and 24001
I would like to treat unknown words like Names as UNKs and they should be replaced by their sources. I do set replace_unknown_target to True. However, the result seems not like what I expects, all Names replaces by some word which are not correct. Am I missing something? I don't see <UNK> in my vocab as well as the prediction. Is it normal ? Thank you
replace_unknown_target uses the model attention to select the corresponding source token. However, it is well known that Transformer attention usually can not be used as target-source alignments. You should either constrain the attention to be an alignment or use subword tokenization (like SentencePiece) to avoid UNK. Note that the UNK token does not appear in the vocab but is automatically added when starting the training.
You probably need to increase
max_step in the training parameters. There should be a warning about this somewhere in the logs. We just improved that for the next version: a more visible error message will be shown, see OpenNMT/OpenNMT-tf@21df1c7
Ive tried both with normal pip install and with downloading and pip installing the wheels available on pypi
Hello
I'm using SetentencePiece as tokenizer to train an OpenNMT model
I will like that when I ask the model to translate something in upper case "HELLO" is able to preserve the case in the translation.
I was expecting this to be a configuration of the Tokenizer but I have not been able to found it. Any help or hint is appreciated.
I'm using SetentencePiece as tokenizer to train an OpenNMT model
I will like that when I ask the model to translate something in upper case "HELLO" is able to preserve the case in the translation.
I was expecting this to be a configuration of the Tokenizer but I have not been able to found it. Any help or hint is appreciated.
Hi. You can either add training examples in uppercase or look into the
case_markup option from the Tokenizer.
Hello
I updated from OpenNMT 2.40 to 2.70 and "replace_unknown_target=True" has stop working. And now I get <unk> tag instead of the source tags for words of vocabulary. Is possible that a regression has been introduced after 2.40? Thanks
(also fails for me with the latest version 2.13)
I updated from OpenNMT 2.40 to 2.70 and "replace_unknown_target=True" has stop working. And now I get <unk> tag instead of the source tags for words of vocabulary. Is possible that a regression has been introduced after 2.40? Thanks
(also fails for me with the latest version 2.13)