Comments (2)
jorgtied
commented on June 10, 2024
langtune is indeed for tuning multilingual models for specific languages. If you have your own data, it may be easier to just prepare it with the subword segmentation and then to run marian with appropriate parameters and that kind of fine-tuning data. It may bot work out of the box to use the langtune recipe in your case.
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hdeval1
commented on June 10, 2024
Awesome, thank you for clarifying! Nothing with technology ever works straight out the box, so I was sure there is some configuring I would have to work through and just wanted to make sure I was on the right track. I just got the finetuning recipes in finetune/ to work so I figured that would be the next step. Thanks again!
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Related Issues (20)
- What's the dataset used for training opus-mt-en-de HOT 1
- Language Code Difference HOT 1
- Preprocessing Script Question
- Korean Finetuning
- Multilingual Tuned Model Translating everything to "sssssssss" HOT 2
- What could cause widely varying inference time when using pre-trained opus-mt-en-fr model with python transformers library? HOT 2
- Wrong tokenizer/vocab for the 'Helsinki-NLP/opus-mt-tc-big-en-ko' model
- How to translate from english to Japan?
- Using OPUS-MT with DeepSpeed
- update Dockerfile.gpu--fixed
- different sizes of dictionaries in different models HOT 1
- Reproduced crash on Opus-mt-en-de model using string "J" and "J-10" HOT 1
- Unable to find current origin/master revision in submodule path HOT 2
- Hyperparameters used for pretrained models? HOT 1
- how to train our dataset HOT 3
- Unbelievably High BLEU scores from finetuning... HOT 3
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- Lack of transparency on used training data. - Does finetuning make sense? HOT 1
- preprocess.sh [: ==: unary operator expected HOT 2
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