Rename v2 selection assets folders to metadata

Move eight selection manifests to metadata and update reproduction scripts and README references.

bugsinpy_v2/README.md

@@ -58,7 +58,7 @@ The kept tasks inherit v1's rule-based repairs, build checks, reference reward 1
 
 ## Select tasks by successful-attempt count
 
-`tasks.parquet` includes `instruct_successes`, `thinking_successes` and `attempts_per_model` (16). Counts are also available in [selection.json](assets/selection.json). Excluded payloads remain in the pinned v1 source recorded in `selection.json`; no duplicate archive Parquet is uploaded.
+`tasks.parquet` includes `instruct_successes`, `thinking_successes` and `attempts_per_model` (16). Counts are also available in [selection.json](metadata/selection.json). Excluded payloads remain in the pinned v1 source recorded in `selection.json`; no duplicate archive Parquet is uploaded.
 
 ```python
 import pyarrow.parquet as pq

bugsinpy_v2/patch.py

@@ -54,7 +54,7 @@ def build(source_dir, out, selection_path):
     images = restrict_images(image_manifest, kept)
     assert set(images['tasks']) == kept
     release = {k: v for k, v in selection.items() if k != 'tasks'}
-    release['selection_file'] = 'assets/selection.json'
+    release['selection_file'] = 'metadata/selection.json'
     metadata = {b'ot.release.v2': json.dumps(release).encode(), b'ot.images.v1': json.dumps(images).encode()}
     schema = original_schema.remove_metadata().append(pa.field('instruct_successes', pa.int64())).append(
         pa.field('thinking_successes', pa.int64())).append(pa.field('attempts_per_model', pa.int64()))
@@ -116,7 +116,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument('--source-dir', type=Path)
     parser.add_argument('--out', type=Path, required=True)
-    parser.add_argument('--selection', type=Path, default=Path(__file__).parent / 'assets' / 'selection.json')
+    parser.add_argument('--selection', type=Path, default=Path(__file__).parent / 'metadata' / 'selection.json')
     parser.add_argument('--include-archive', action='store_true', help='Also reconstruct excluded payloads locally')
     args = parser.parse_args()
     build(args.source_dir, args.out, args.selection)

calibforge_v2/README.md

@@ -56,7 +56,7 @@ Observed counts from up to eight attempts, not complete pass@8 estimates.
 
 ## Files and selecting tasks
 
-`tasks.parquet` contains kept payloads, `instruct_successes`, `instruct_graded_attempts`, `instruct_missing_attempts` and `target_attempts_per_model` (8). `assets/selection.json` records all task statuses, attempt IDs, rewards and the pinned v1 source. Excluded payloads remain in v1; no duplicate archive Parquet is uploaded. `patch.py` reproduces this snapshot.
+`tasks.parquet` contains kept payloads, `instruct_successes`, `instruct_graded_attempts`, `instruct_missing_attempts` and `target_attempts_per_model` (8). `metadata/selection.json` records all task statuses, attempt IDs, rewards and the pinned v1 source. Excluded payloads remain in v1; no duplicate archive Parquet is uploaded. `patch.py` reproduces this snapshot.
 
 ```python
 import pyarrow.parquet as pq

calibforge_v2/patch.py

@@ -5,7 +5,7 @@ import pyarrow as pa,pyarrow.parquet as pq
 from huggingface_hub import hf_hub_download
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=True)
 assert not (a.out/'tasks.parquet').exists()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
 src=pq.ParquetFile(f);schema=src.schema_arrow.remove_metadata()
 for name in ['instruct_successes','instruct_graded_attempts','instruct_missing_attempts','target_attempts_per_model']:schema=schema.append(pa.field(name,pa.int64()))
 rows=[]

crosscodeeval_v2/README.md

@@ -60,7 +60,7 @@ The kept tasks inherit v1's rule-based repairs, build checks, reference reward 1
 
 ## Select tasks by successful-attempt count
 
-`tasks.parquet` includes `instruct_successes`, `thinking_successes` and `attempts_per_model` (16). Counts are also available in [selection.json](assets/selection.json). Excluded payloads remain in the pinned v1 source recorded in `selection.json`; no duplicate archive Parquet is uploaded.
+`tasks.parquet` includes `instruct_successes`, `thinking_successes` and `attempts_per_model` (16). Counts are also available in [selection.json](metadata/selection.json). Excluded payloads remain in the pinned v1 source recorded in `selection.json`; no duplicate archive Parquet is uploaded.
 
 ```python
 import pyarrow.parquet as pq

crosscodeeval_v2/patch.py

@@ -56,7 +56,7 @@ def build(source_dir, out, selection_path):
     images = restrict_images(image_manifest, kept)
     assert set(images['tasks']) == kept
     release = {k: v for k, v in selection.items() if k != 'tasks'}
-    release['selection_file'] = 'assets/selection.json'
+    release['selection_file'] = 'metadata/selection.json'
     metadata = {b'ot.release.v2': json.dumps(release).encode(), b'ot.images.v1': json.dumps(images).encode()}
     schema = original_schema.remove_metadata().append(pa.field('instruct_successes', pa.int64())).append(
         pa.field('thinking_successes', pa.int64())).append(pa.field('attempts_per_model', pa.int64())).append(pa.field('archive_category', pa.string()))
@@ -131,7 +131,7 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser(description=__doc__)
     parser.add_argument('--source-dir', type=Path)
     parser.add_argument('--out', type=Path, required=True)
-    parser.add_argument('--selection', type=Path, default=Path(__file__).parent / 'assets' / 'selection.json')
+    parser.add_argument('--selection', type=Path, default=Path(__file__).parent / 'metadata' / 'selection.json')
     parser.add_argument('--include-archive', action='store_true', help='Also reconstruct excluded payloads locally')
     args = parser.parse_args()
     build(args.source_dir, args.out, args.selection)

endless_v2/README.md

@@ -46,7 +46,7 @@ path = hf_hub_download("FWeindel/validated-tasks", "endless_v2/tasks.parquet", r
 tasks = pq.read_table(path, filters=[("instruct_successes", ">=", 1), ("instruct_successes", "<=", 2)])
 ```
 
-`assets/selection.json` records all v1 task decisions and counts. `patch.py` reproduces the task Parquet from pinned v1 data. `instruct_graded_attempts` counts attempts assigned a filtering score, including terminal failures.
+`metadata/selection.json` records all v1 task decisions and counts. `patch.py` reproduces the task Parquet from pinned v1 data. `instruct_graded_attempts` counts attempts assigned a filtering score, including terminal failures.
 
 ## Traces
 

endless_v2/patch.py

@@ -5,7 +5,7 @@ import pyarrow as pa,pyarrow.parquet as pq
 from huggingface_hub import hf_hub_download
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=True)
 assert not (a.out/'tasks.parquet').exists()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
 src=pq.ParquetFile(f);schema=src.schema_arrow.remove_metadata();columns=[model+'_'+metric for model in m['models'] for metric in ['successes','graded_attempts','missing_attempts']]
 for name in columns+['target_attempts_per_model']:schema=schema.append(pa.field(name,pa.int64()))
 rows=[]

scaleswe_v2/README.md

@@ -56,7 +56,7 @@ path = hf_hub_download("FWeindel/validated-tasks", "scaleswe_v2/tasks.parquet",
 tasks = pq.read_table(path, filters=[("instruct_successes", ">=", 1), ("instruct_successes", "<=", 2)])
 ```
 
-`assets/selection.json` records every v1 task's snapshot results and retention decision. `patch.py` reproduces the task Parquet from pinned v1 data.
+`metadata/selection.json` records every v1 task's snapshot results and retention decision. `patch.py` reproduces the task Parquet from pinned v1 data.
 
 ## Resources
 

scaleswe_v2/patch.py

@@ -5,7 +5,7 @@ import pyarrow as pa,pyarrow.parquet as pq
 from huggingface_hub import hf_hub_download
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=True)
 assert not (a.out/'tasks.parquet').exists()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
 src=pq.ParquetFile(f);schema=src.schema_arrow.remove_metadata();columns=[model+'_'+metric for model in m['models'] for metric in ['successes','graded_attempts','missing_attempts']]
 for name in columns+['target_attempts_per_model']:schema=schema.append(pa.field(name,pa.int64()))
 rows=[]

seta_v2/README.md

@@ -67,7 +67,7 @@ These are observed success counts from up to 16 attempts per model, not complete
 
 ## Files and selecting tasks
 
-`tasks.parquet` contains kept payloads and each model's successes, graded attempts and missing attempts. `assets/selection.json` records every v1 task's results, selection reason and the pinned v1 revision. Excluded payloads remain in v1; no duplicate archive Parquet is uploaded. `patch.py` reproduces this snapshot.
+`tasks.parquet` contains kept payloads and each model's successes, graded attempts and missing attempts. `metadata/selection.json` records every v1 task's results, selection reason and the pinned v1 revision. Excluded payloads remain in v1; no duplicate archive Parquet is uploaded. `patch.py` reproduces this snapshot.
 
 ```python
 import pyarrow.parquet as pq

seta_v2/patch.py

@@ -5,7 +5,7 @@ import pyarrow as pa
 import pyarrow.parquet as pq
 from huggingface_hub import hf_hub_download
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());a.out.mkdir(parents=True,exist_ok=True)
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text());a.out.mkdir(parents=True,exist_ok=True)
 assert not (a.out/'tasks.parquet').exists(),'Choose an empty output directory'
 f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'))
 assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']

swelego_v2/README.md

@@ -50,7 +50,7 @@ path = hf_hub_download("FWeindel/validated-tasks", "swelego_v2/tasks.parquet", r
 tasks = pq.read_table(path, filters=[("instruct_successes", ">=", 1), ("instruct_successes", "<=", 2)])
 ```
 
-`assets/selection.json` records every v1 task's snapshot results and retention decision. `patch.py` reproduces the task Parquet from pinned v1 data.
+`metadata/selection.json` records every v1 task's snapshot results and retention decision. `patch.py` reproduces the task Parquet from pinned v1 data.
 
 ## Resources
 

swelego_v2/patch.py

@@ -5,7 +5,7 @@ import pyarrow as pa,pyarrow.parquet as pq
 from huggingface_hub import hf_hub_download
 p=argparse.ArgumentParser(description=__doc__);p.add_argument('--out',type=Path,required=True);p.add_argument('--source',type=Path);a=p.parse_args();a.out.mkdir(parents=True,exist_ok=True)
 assert not (a.out/'tasks.parquet').exists()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text());f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'));assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']
 src=pq.ParquetFile(f);schema=src.schema_arrow.remove_metadata();columns=[model+'_'+metric for model in m['models'] for metric in ['successes','graded_attempts','missing_attempts']]
 for name in columns+['target_attempts_per_model']:schema=schema.append(pa.field(name,pa.int64()))
 rows=[]

termigen_v2/README.md

@@ -50,7 +50,7 @@ Per-task Coder evidence establishes whether a task was solved; exact per-task su
 
 ## Files and selecting tasks
 
-`tasks.parquet` contains kept tasks, `coder_solved`, `instruct_successes`, `instruct_missing_rewards` and `attempts_per_model`. `assets/selection.json` records every v1 task's selection and pins the source revision. Excluded payloads remain in that v1 revision; no duplicate archive Parquet is uploaded. `patch.py` reproduces this selection.
+`tasks.parquet` contains kept tasks, `coder_solved`, `instruct_successes`, `instruct_missing_rewards` and `attempts_per_model`. `metadata/selection.json` records every v1 task's selection and pins the source revision. Excluded payloads remain in that v1 revision; no duplicate archive Parquet is uploaded. `patch.py` reproduces this selection.
 
 ```python
 import pyarrow.parquet as pq

termigen_v2/patch.py

@@ -8,7 +8,7 @@ p=argparse.ArgumentParser(description=__doc__)
 p.add_argument('--out',type=Path,required=True)
 p.add_argument('--source',type=Path)
 a=p.parse_args()
-m=json.loads((Path(__file__).parent/'assets/selection.json').read_text())
+m=json.loads((Path(__file__).parent/'metadata/selection.json').read_text())
 a.out.mkdir(parents=True,exist_ok=True)
 f=a.source or Path(hf_hub_download(m['repo_id'],m['source_folder']+'/tasks.parquet',revision=m['source_revision'],repo_type='dataset',cache_dir=a.out/'.source-cache'))
 assert hashlib.sha256(f.read_bytes()).hexdigest()==m['source_sha256']