Hi, I tried to tag (featured) both source and target languages with OpenNMT but didn't succeed. PyTorch just crashed at translation step (training was ok); and TensorFlow docs says that "target_inputter – A opennmt.inputters.Inputter to process the target data. Currently, only the opennmt.inputters.WordEmbedder is supported." which means (to me) that we can't tag the target. How could I try to tag both source and target with OpenNMT ? Is this possible ? May someone help me ?
Hi, tags are not supported on the target side in OpenNMT-tf. In OpenNMT-py, there is an open pull request for this feature: OpenNMT/OpenNMT-py#1710
Thanks @guillaumekln. In OpenNMT-py what puzzled me is that the training was perfect (at least no error) but at the translation step it seems that the crash was due to a non uniform word added by the system at the end of the sentence (looks like a end-of-sentence word that is of course? non featured).
Sure. Just run 2 separate training processes, each running on a different GPU. You can restrict the GPU visible to each process with this approach: https://opennmt.net/OpenNMT-tf/faq.html#how-can-i-restrict-the-tensorflow-runtime-to-specific-gpu
If you have more questions regarding CTranslate2, please post in https://gitter.im/OpenNMT/CTranslate2 or in the forum
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.