Comments (2)
tbennun
commented on May 24, 2024
- For the sake of making the training setting more challenging, we tried to completely separate the tasks of training inst2vec from training the downstream tasks. However, for real problems I don't see a point in ensuring such a split. It's ok if you use Stencil-synthetic in your classifyapp sample
- The slow training time may be related to a bug that was fixed in #16, which isn't merged yet since we need to verify some further results. Please try it and see the performance for your case.
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tbennun
commented on May 24, 2024
Closed due to inactivity. Hope I could help!
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Related Issues (20)
- train_inst2vec.py fails on specific file during vocabulary building HOT 11
- [train_task_classifyapp.py] GPU memory grows unlimitedly HOT 9
- loss and acc have large differences even though train and valid are set totally same HOT 4
- llvm ir of linux kernel HOT 1
- test
- Asm inline call handling HOT 1
- question about predict value p at train_task_classifyapp.py line 417 HOT 2
- ValueError: GraphDef cannot be larger than 2GB. HOT 1
- Bug of Regular Express matching Report For Preprocessing LLVM IR HOT 1
- 'MultiDiGraph' object has no attribute 'node' HOT 3
- Confusion in inst2vec_preprocess.py when reading code HOT 6
- The original source code of the datasets HOT 3
- Expected combineable dataset HOT 1
- for classifyapp, vocubalary dictionary is not present. HOT 1
- How to get the embedding result of inst2vec ? HOT 3
- the links to all the datasets did not work. HOT 3
- dictionary_pickle not available HOT 3
- [inst2vec_evaluate.py] IndexError : list index out of range in analogies HOT 1
- The link to the dataset is not working HOT 2
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