Comments (1)
Hello @baoloc97,
Sorry I took a while to respond.
Openkiwi does not produce a confidence score in that sense. When you are talking about a confidence score from a MT model, it can be thought of as the uncertainty of the model on inference time. It can be obtained with methods such as monte carlo dropout and similar.
On the other hand, what OpenKiwi does it Quality Estimation, it provides models that specialise in looking at parallel corpora and producing "quality scores" for this data. Either at word-level (with Ok/BAD tags) or at sentence level (by predicting HTER), this functions as a proxy of MT quality when you don't have a reference, with results from wmt20 metrics shared task showing a high correlation between QE and metrics.
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Related Issues (20)
- TypeError: cannot unpack non-iterable NoneType object HOT 1
- The prediction process is not complete by Predictor Estimator. HOT 5
- OpenKiwi always download the tokenizer files for XLMRoberta even if a local path is configured. HOT 2
- Error Pre-Training Predictor: "model -> encoder -> encode_source extra fields not permitted (type=value_error.extra)" HOT 1
- some confusions
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- Got exception when import kiwi
- Seems that maximum token support for a sentence is 512?
- PicklingError: Can't pickle <class 'kiwi.data.encoders.wmt_qe_data_encoder.InputFields[PositiveInt]'>: attribute lookup InputFields[PositiveInt] on kiwi.data.encoders.wmt_qe_data_encoder failed HOT 2
- Do you need to tokenize your data when using a BERT/ROBERTA model?
- Pretrain config file
- What are source_pos and target_pos in the train_config.yaml?
- Why does it need "--model" paramter when I give a specific config? HOT 2
- What languages do the OpenKiwi support?
- some problems about data without alignments HOT 11
- I suppose that the code comment should be remove. HOT 2
- Error at Predictor Training: "Predictor is not a subclass of QESystem" HOT 2
- OSError: Can't load weights for 'xlm-roberta-base'. HOT 16
- open cannot unpack non-iterable NoneType object HOT 16
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