Comments (1)
ricardorei
commented on May 8, 2024
I am not sure I understand this. But we added Embedding Caching to COMET.
This means that after you instantiate a COMET model and that model will reuse computations for repeated batches (during inference only). Yet I think you mean something else... I think you want the "heavy" weight to be performed in a different machine and then we just reuse those embeddings? That seems over-complicated... What is your use case for this?
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Related Issues (20)
- Can't reproduce Cometinho model scores HOT 3
- Do system scores above 100 really "differ"? HOT 4
- wmt20-comet-qe-da do not work under huggingface guides (possible version conflicts) HOT 3
- Support pandas 2
- [QUESTION] How to finetune `wmt22-comet-da` and have results scaled to 0-1 range HOT 1
- UnifiedMetric test failing: test_multitask_with_references HOT 1
- Segmentation error when tring to reproduce wmt22 results HOT 1
- How to reproduce Unbabel/wmt22-comet-da model HOT 2
- Evaluate lines with newline characters
- [QUESTION] Does COMET support Scoring multiple refs like scarebleu? HOT 2
- [QUESTION] How are the older models supposed to be used?
- v1.x and v2.x have different scores for wmt20-comet-qe-da model HOT 2
- [QUESTION]When I train my COMET model, I have the following problem when I am almost successful, it seems to be stuck HOT 2
- Unbabel/wmt22-cometkiwi-da doesn't work from Huggingface HOT 1
- pretrained_model setting in hparams.yaml has no effect HOT 1
- [QUESTION] Why does training speed go down? HOT 5
- Multi-GPU training HOT 10
- Using comet-mbr for Multi-Model Translation Ranking: Questions About Input Format and GPU Disabling HOT 3
- 504 Server error when running comet-score using multiple machines HOT 7
- Is `wmt22-comet-da` the same as "COMET-22" & trained on MQM data? HOT 2
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