Datasets:
Tasks:
Text Classification
Formats:
parquet
Sub-tasks:
semantic-similarity-classification
Languages:
code
Size:
1M - 10M
License:
sync EmbedEd artifacts (phase01-v6)
Browse files- .gitattributes +1 -0
- artifacts/runs/C0_13/eval_config.json +13 -0
- artifacts/runs/C0_13/eval_metrics.json +37 -0
- artifacts/runs/C0_13/predictions.npz +3 -0
- report/measurements.md +15 -0
- sync_manifest.json +4 -1
.gitattributes
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@@ -49,3 +49,4 @@ artifacts/sprime/Clones[[:space:]]paper[[:space:]]replication[[:space:]]package[
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artifacts/sprime/Clones[[:space:]]paper[[:space:]]replication[[:space:]]package[[:space:]]release[[:space:]]zip/oj_dataset_experiments/astnn/data/c/oj_and_scb/pair_ids.pkl filter=lfs diff=lfs merge=lfs -text
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artifacts/sprime/Clones[[:space:]]paper[[:space:]]replication[[:space:]]package[[:space:]]release[[:space:]]zip/oj_dataset_experiments/astnn/data/c/oj_and_scb/programs.pkl filter=lfs diff=lfs merge=lfs -text
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artifacts/corpus_emb.npy filter=lfs diff=lfs merge=lfs -text
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artifacts/sprime/Clones[[:space:]]paper[[:space:]]replication[[:space:]]package[[:space:]]release[[:space:]]zip/oj_dataset_experiments/astnn/data/c/oj_and_scb/pair_ids.pkl filter=lfs diff=lfs merge=lfs -text
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artifacts/sprime/Clones[[:space:]]paper[[:space:]]replication[[:space:]]package[[:space:]]release[[:space:]]zip/oj_dataset_experiments/astnn/data/c/oj_and_scb/programs.pkl filter=lfs diff=lfs merge=lfs -text
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artifacts/corpus_emb.npy filter=lfs diff=lfs merge=lfs -text
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artifacts/runs/C0_13/predictions.npz filter=lfs diff=lfs merge=lfs -text
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artifacts/runs/C0_13/eval_config.json
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{
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"condition": "C0",
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"seed": 13,
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"smoke": false,
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"version": "phase01-v6",
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"model_id": "microsoft/graphcodebert-base",
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"pooling": "mean",
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"max_len": 512,
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"eval_batch": 64,
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"threshold_policy": "max_f1_on_valid",
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"pairs_source": "artifacts/pairs_{valid,test}.tsv",
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"fingerprint": "670ec216db20b468"
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}
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artifacts/runs/C0_13/eval_metrics.json
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{
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"condition": "C0",
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"seed": 13,
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"version": "phase01-v6",
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"fingerprint": "670ec216db20b468",
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"smoke": false,
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"model": "microsoft/graphcodebert-base (untuned)",
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"threshold_policy": "max_f1_on_valid",
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"threshold": 0.977894,
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"valid": {
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"precision": 0.2029,
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"recall": 0.3378,
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"f1": 0.2536,
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"n": 415416
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},
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"valid_map_at_r": 0.1021,
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"test_f1": 0.2572,
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"test_precision": 0.2075,
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"test_recall": 0.3383,
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"test_map_at_r": 0.1024,
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"test": {
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"precision": 0.2075,
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"recall": 0.3383,
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"f1": 0.2572,
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"tp": 19220,
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"fp": 73401,
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"fn": 37600,
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"n": 415416
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},
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"n_fragments_encoded": 1685,
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"n_pairs": {
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"valid": 415416,
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"test": 415416
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},
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"encode_seconds": 14.9,
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"evaluated_utc": "2026-09-27T16:46:21Z"
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}
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artifacts/runs/C0_13/predictions.npz
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version https://git-lfs.github.com/spec/v1
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oid sha256:5ed148d7a85dfd07d6006931474cf9c06452109e85c776b1387c41402420cf5f
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size 2800167
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report/measurements.md
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@@ -533,3 +533,18 @@ Label-noise floors (correction 5; proxies, not the audit): share of negatives th
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| C2 | 50 | 21 | 29 | 0 | **42%** [29%, 56%] | 42% [29%, 56%] |
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| C3 | 50 | 15 | 34 | 1 | **30%** [19%, 44%] | 32% [21%, 46%] |
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| C2 | 50 | 21 | 29 | 0 | **42%** [29%, 56%] | 42% [29%, 56%] |
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| C3 | 50 | 15 | 34 | 1 | **30%** [19%, 44%] | 32% [21%, 46%] |
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## Phase 2 — main results
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Generated from `artifacts/runs/<condition>_<seed>/eval_metrics.json` by `python -m embeded.evaluate --results-table` — version `phase01-v6`. Threshold policy: *max_f1_on_valid* (chosen on validation, applied unchanged to test; test is scored once per run).
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| condition | seed | F1 | precision | recall | MAP@R | valid threshold | n test pairs |
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|---|---|---|---|---|---|---|---|
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| C0 | 13 | 0.2572 | 0.2075 | 0.3383 | 0.1024 | 0.9779 | 415416 |
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| condition | runs | mean F1 | sd F1 | mean P | mean R | mean MAP@R |
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|---|---|---|---|---|---|---|
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| C0 | 1 | 0.2572 | 0.0000 | 0.2075 | 0.3383 | 0.1024 |
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- C0 is the untuned baseline (one evaluation, not a trained condition) at mean F1 0.2572.
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- Sanity reference: published CodeXGLUE fine-tuned-CodeBERT F1 ~ 0.95 under a different model/protocol. Observed F1 range 0.2572–0.2572. A large discrepancy is a harness/protocol check (metric implementation, pair order, labels, threshold handling), not a reason to tune against the test split.
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sync_manifest.json
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{
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"version": "phase01-v6",
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-
"synced_at_utc": "2026-09-
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"staged_files": [
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"artifacts/audit/audit_key.csv",
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"artifacts/audit/audit_labels.csv",
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"artifacts/pairs_test.tsv",
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"artifacts/pairs_train.tsv",
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"artifacts/pairs_valid.tsv",
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"artifacts/token_lengths.json",
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"artifacts/triples_C1.jsonl",
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"artifacts/triples_C2.jsonl",
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{
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"version": "phase01-v6",
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"synced_at_utc": "2026-09-27T17:03:35Z",
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"staged_files": [
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"artifacts/audit/audit_key.csv",
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"artifacts/audit/audit_labels.csv",
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"artifacts/pairs_test.tsv",
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"artifacts/pairs_train.tsv",
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"artifacts/pairs_valid.tsv",
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"artifacts/runs/C0_13/eval_config.json",
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"artifacts/runs/C0_13/eval_metrics.json",
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"artifacts/runs/C0_13/predictions.npz",
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"artifacts/token_lengths.json",
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"artifacts/triples_C1.jsonl",
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"artifacts/triples_C2.jsonl",
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