张宗平 204d5237ba feat(m3): losses — masked LM CE + symmetric InfoNCE (T3.1) ...

- src/tsmm/train/losses.py:
  * lm_loss(logits, labels): shifted masked CE (answer span only); clean 0.0
    when all positions masked.
  * infonce_contrastive_loss(z1, z2): symmetric CLIP-style InfoNCE in TS-
    embedding space; positive = original vs light-augmentation view.
- tests/test_losses.py: 9 tests. 100 tests passing.

Spec clarification (small tier): InfoNCE operates in TS-embedding space
(encoder/projector output) rather than answer-embedding space; light
augmentations are positives. T3.3 strong-perturbation sensitivity is a
separate downstream check.

2026-06-30 02:19:02 +00:00

..

__init__.py

feat(m1): scaffold tsmm package (T1.1)

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test_attributes.py

feat(m1): attribute vocab + sampling (T1.2)

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test_collator.py

feat(m2): Collator — JSONL → wrapper raw batch (T2.5)

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test_gen_pipeline.py

feat(m1): offline generation pipeline + CLI (T1.5)

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test_instruct.py

feat(m1): offline generation pipeline + CLI (T1.5)

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test_losses.py

feat(m3): losses — masked LM CE + symmetric InfoNCE (T3.1)

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test_multimodal.py

feat(m2): multimodal splice [属性][时间戳][TS tok][事件][问题][回答] (T2.3)

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test_projector.py

feat(m2): Projector Linear(256→896)+LayerNorm (T2.2)

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test_real_bench.py

feat(m1): real benchmark loader (T1.6)

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test_smoke.py

feat(m1): scaffold tsmm package (T1.1)

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test_synthesis.py

feat(m1): synthesis — components + anomaly/event/missing (T1.3)

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test_ts_encoder.py

feat(m2): TS Encoder — Patchify + 2-layer Transformer (T2.1)

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test_wrapper.py

feat(m2): MultimodalTSModel wrapper — end-to-end fwd/generate + LoRA (T2.4)

2026-06-30 02:11:36 +00:00