Comments (3)
jorgtied
commented on May 10, 2024
The MT models are not trained for replying to messages but rather to produce the same meaning covered by the input in some output message. Typically this is done for different languages but it can also be used in the same language for paraphrasing (or possibly error correction). I don't think it can be fine-tuned to do chat responses, this would rather require training from scratch I believe ...
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remotejob
commented on May 10, 2024
I have about 1 mio chat pairs.
How I can startup create model?
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jorgtied
commented on May 10, 2024
I guess you should have a look into general seq2seq models and train from scratch. I don't think that the OPUS-MT models are a good starting point.
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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
- What is tatoeba-langtune? HOT 2
- 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
- Data for Brazilian Portuguese HOT 2
- Lack of transparency on used training data. - Does finetuning make sense? HOT 1
- preprocess.sh [: ==: unary operator expected HOT 2
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