Comments (3)
Hi @wanyu2018umac,
The naming of the file test19-relative-ranks is a bit misleading. That file is just a random split that I did to validate data from 2019 without having to run the entire corpus from that year. To reproduce the results you have to use the entire en-de data from 2019.
The emnlp-base-da-ranker
is trained on data from 2017 and 2018 only and the reported results use the entire 2019 data.
We do have a script to reproduce the result https://github.com/Unbabel/COMET/blob/master/wmt-shared-task/segment-level/segment_level_comet.py
You just need to download the data as you did and download the checkpoint locally also. Then it should be easy to run
from comet.
Please send me an email if you have any problems reproducing the results (I am usually faster at replying to emails).
from comet.
Ah I see it. It's a little misleading though. Thanks for your response!
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Related Issues (20)
- Is `wmt22-comet-da` the same as "COMET-22" & trained on MQM data? HOT 2
- Quantization HOT 2
- TypeError: comet.models.utils.Prediction is not a dataclasss. This is a subclass of ModelOutput and so must use the @dataclass decorator. HOT 5
- Not compatible with recent transformers? HOT 3
- Comet Evaluation Metric Error with Command line Utility HOT 1
- Running the code on Mac
- whar aspects of language gets considered while evaluating the tranlsation by COMET. as far as I am aware of MQM metric sare covered in it. Interested to know other terminolofy/features of language gets in COMET. HOT 1
- what aspects of language get considered while evaluating the translation by COMET. as far as I am aware of MQM metrics are covered in it. Interested in knowing other terminology/features of language gets in COMET. I am looking for metric along with COMET that can assess the translated content for cosmetic domain data. please suggest. HOT 1
- [QUESTION] Train Your Own Metric HOT 2
- Multi-gpu inference returns AttributeError HOT 7
- Mismatch between error span and offsets HOT 2
- [QUESTION] Reliability for en-th and th-en pair HOT 2
- AttributeError: 'dict' object has no attribute 'scores' HOT 2
- [QUESTION] AttributeError: An error occurred during model prediction: 'dict' object has no attribute 'scores' HOT 5
- AttributeError: 'dict' object has no attribute 'scores' when using the model 'Unbabel/wmt22-cometkiwi-da' HOT 4
- When loading huggingface Unbabel/wmt22-cometkiwi-da checkpoint, UnpicklingError
- [QUESTION] Unable to use ReLU as activation function HOT 4
- [QUESTION] Keep getting scores of '0' no matter what input used HOT 5
- HF Hub dependency conflicts with latest `tranformers` version. HOT 3
- if tgt is same with src, the score is still high HOT 2
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from comet.