diff options
author | S. VenkataKeerthy <31350914+svkeerthy@users.noreply.github.com> | 2025-07-29 11:56:52 -0700 |
---|---|---|
committer | GitHub <noreply@github.com> | 2025-07-29 11:56:52 -0700 |
commit | 130f24b28d38ca3679a76ab7a6a4f7ff7e9f4c81 (patch) | |
tree | 7eed217790695fc8c2049092114ea9879aa626fd /lldb/unittests/ScriptInterpreter/Python/PythonDataObjectsTests.cpp | |
parent | 052b836d2384c8a583ae5f945dcdcfba2004bbb0 (diff) | |
download | llvm-130f24b28d38ca3679a76ab7a6a4f7ff7e9f4c81.zip llvm-130f24b28d38ca3679a76ab7a6a4f7ff7e9f4c81.tar.gz llvm-130f24b28d38ca3679a76ab7a6a4f7ff7e9f4c81.tar.bz2 |
[IR2Vec][llvm-ir2vec] Revamp triplet generation and add entity mapping mode (#149214)
Add entity mapping mode to llvm-ir2vec and improve triplet generation format for knowledge graph embedding training.
This change streamlines the workflow for training the vocabulary embeddings with IR2Vec by:
1. Directly generating numeric IDs instead of requiring string-to-ID preprocessing
2. Providing entity mappings in standard knowledge graph embedding format
3. Structuring triplet output in train2id format compatible with knowledge graph embedding frameworks
4. Adding metadata headers to simplify post-processing and training setup
These improvements make IR2Vec more compatible with standard knowledge graph embedding training pipelines and reduce the preprocessing steps needed before training.
See #149215 for more details on how it is used.
(Tracking issues - #141817, #141834)
Diffstat (limited to 'lldb/unittests/ScriptInterpreter/Python/PythonDataObjectsTests.cpp')
0 files changed, 0 insertions, 0 deletions