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f2e61f01cc42e553d90269cdd8a876ca519e0d389a356d780152abcf0498c37456401d9840df5ebe24c435ef813c0e4e0d296106c26d4266f7c603284b9b8049
23a1a26eb54ee97fc44af3d4db238c132fdbfcac72c242a32faf77a52bb1ac571e191da977ac02d9b8358c8cd293c5a47d08882dffc8ea65bd3bfa747c66ba54
pypi-f2e61f01cc42e553d90269cdd8a876ca519e0d389a356d780152abcf0498c37456401d9840df5ebe24c435ef813c0e4e0d296106c26d4266f7c603284b9b8049
pypi
3.14
98c63097a6f5a07c87696968beb36ec2370345972d772287715ff68e553ed17d
from __future__ import annotations from dataclasses import dataclass from kiota_abstractions.get_path_parameters import get_path_parameters from kiota_abstractions.method import Method from kiota_abstractions.request_adapter import RequestAdapter from kiota_abstractions.request_information import RequestInformation fro...
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[ { "source": "from <mask_0><mask_2> import <mask_1>", "bytecode": "LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , ('<mask_1>',) <SEP> IMPORT_NAME , <mask_2> <SEP> IMPORT_FROM , <mask_1> <SEP> STORE_NAME , <mask_1> <SEP> POP_TOP" }, { "source": "from <mask_0><mask_2> import <mask_1>", "bytecode": "L...
[ { "source": "from <mask_0><mask_22> import <mask_4> <SEP> from <mask_0><mask_23> import <mask_5> <SEP> from <mask_0><mask_24> import <mask_6> <SEP> from <mask_0><mask_25> import <mask_7> <SEP> from <mask_0><mask_26> import <mask_8> <SEP> from <mask_0><mask_27> import <mask_9> <SEP> from <mask_0><mask_28> import...
train
afd8afe216afa0eb63b49c20d642a94eca1bccf35926591aae7432b269999ece4f5e876029c6d53335de204c0fcd07ed3d35e23c5ecd10127835e4c25231dd95
9718e4caee5fe92c538eace2677e9ab782deb60e91aa4ccdc2761bb7465a5a763d1b986cbd488a1d3f091be89061e268554cd55bf43b85d19896bf8ddb059824
pypi-afd8afe216afa0eb63b49c20d642a94eca1bccf35926591aae7432b269999ece4f5e876029c6d53335de204c0fcd07ed3d35e23c5ecd10127835e4c25231dd95
pypi
3.14
db1496eaafe882f40c1104f1f4539395d6ffce720864c25151e003c47d48171b
"""pass""" from enum import Enum from typing import Optional, Type, TypeVar from pydantic import BaseModel from metadata.generated.schema.api.data.createCustomProperty import CreateCustomPropertyRequest from metadata.generated.schema.type import basic, entityHistory T = TypeVar('T', bound=BaseModel) class CustomProper...
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[ { "source": "\"\"\"<mask_0>\"\"\"", "bytecode": "LOAD_CONST , '<mask_0>' <SEP> STORE_NAME , __doc__" }, { "source": "from <mask_0><mask_2> import <mask_1>", "bytecode": "LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , ('<mask_1>',) <SEP> IMPORT_NAME , <mask_2> <SEP> IMPORT_FROM , <mask_1> <SEP> STO...
[ { "source": "\"\"\"<mask_0>\"\"\" <SEP> from <mask_8><mask_19> import <mask_9> <SEP> from <mask_8><mask_20> import <mask_10>, <mask_11>, <mask_12> <SEP> from <mask_8><mask_21> import <mask_13> <SEP> from <mask_8><mask_22> import <mask_14> <SEP> from <mask_8><mask_23> import <mask_15>, <mask_16> <SEP> <mask_1> =...
train
9b33737c39b06e637a67edf78246ee19140c28fd82d8ac431e12a8d2e78e03993db631e8496ec08a6330d0abf39fffd24c9f74da9b8175210308fcbf829801a3
99d7a6e69af60bc6a7aba2b554d87354bbe6d44c516e6a5a9b36a0e0471e78e61080936cbe904b381a372be74a882b44624e390e1a8dd1cbbb2b71fe1053d1c3
pypi-9b33737c39b06e637a67edf78246ee19140c28fd82d8ac431e12a8d2e78e03993db631e8496ec08a6330d0abf39fffd24c9f74da9b8175210308fcbf829801a3
pypi
3.14
7179267ed204c61ed25f484a3ce53a3cd518ae68448b456453a6e2f2d08dd1cd
from typing import Any, Dict from modelscope.metainfo import Pipelines from modelscope.outputs import OutputKeys from modelscope.pipelines.base import Input, Model, Pipeline from modelscope.pipelines.builder import PIPELINES from modelscope.pipelines.util import is_model, is_official_hub_path from modelscope.utils.cons...
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[ { "source": "from <mask_0><mask_3> import <mask_1>, <mask_2>", "bytecode": "LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , ('<mask_1>', '<mask_2>') <SEP> IMPORT_NAME , <mask_3> <SEP> IMPORT_FROM , <mask_1> <SEP> STORE_NAME , <mask_1> <SEP> IMPORT_FROM , <mask_2> <SEP> STORE_NAME , <mask_2> <SEP> POP_TOP" },...
[ { "source": "from <mask_0><mask_17> import <mask_3>, <mask_4> <SEP> from <mask_0><mask_18> import <mask_5> <SEP> from <mask_0><mask_19> import <mask_6> <SEP> from <mask_0><mask_20> import <mask_7>, <mask_8>, <mask_9> <SEP> from <mask_0><mask_21> import <mask_10> <SEP> from <mask_0><mask_22> import <mask_11>, <m...
train
"8c31e39ed7d6e36dad5e220b923e7d60c0f00289de52fcb4b7d2fa8fa1d00b08eb8e903d50dcfb9fb18c9f0641b01ec24a9(...TRUNCATED)
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"pypi-8c31e39ed7d6e36dad5e220b923e7d60c0f00289de52fcb4b7d2fa8fa1d00b08eb8e903d50dcfb9fb18c9f0641b01e(...TRUNCATED)
pypi
3.14
85c6fbbfe96f6242083ff7c9b0f621477525239c913b4d3df69396320d5dd444
"\"\"\"pass\"\"\"\nimport re\nimport sys\nfrom intersight.model_utils import ApiTypeError, ModelComp(...TRUNCATED)
"Kw4NCgAAAAAAAAAA5yIAAOMAAAAAAAAAAAAAAAAFAAAAAAAAAPOCAAAAgABSAHQAXgBSAUkBdAFeAFIBSQJ0Al4AUgJJA0gEdAR(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\"","bytecode":"LOAD_CONST , '<mask_0>' <SEP> STORE_NAME , __doc__"},{(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\" <SEP> import <mask_21> <SEP> import <mask_22> <SEP> from <mask_4><m(...TRUNCATED)
train
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pypi
3.14
5cff7d04d904d76e41114b6ca266217d60f89e65da5f65f30ad1fedd7de92c44
"import six\nfrom huaweicloudsdkcore.utils.http_utils import sanitize_for_serialization\n\nclass Cre(...TRUNCATED)
"Kw4NCgAAAAAAAAAAXwcAAOMAAAAAAAAAAAAAAAAEAAAAAAAAAPMuAAAAgABeAFIBSQB0AF4AUgJJAUgCdAIfABUAIQBSAxcAUgQ(...TRUNCATED)
[{"source":"import <mask_1>","bytecode":"LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , None <SEP> IMP(...TRUNCATED)
[{"source":"import <mask_4> <SEP> from <mask_0><mask_5> import <mask_3> <SEP> class <mask_40>:","byt(...TRUNCATED)
train
"e5f5e0afb2774458a1f7c0325b02c9bbbb4c756d3b84813dab13d59a332765f4c0f97af68842a45d99d7d6616dba1fa6cc3(...TRUNCATED)
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"pypi-e5f5e0afb2774458a1f7c0325b02c9bbbb4c756d3b84813dab13d59a332765f4c0f97af68842a45d99d7d6616dba1f(...TRUNCATED)
pypi
3.14
8487db5d33f0c9d3b0164bd85b0f90550ef55b3d389703a1c1d2e6464c615ec7
"\"\"\"pass\"\"\"\nfrom __future__ import annotations\nimport logging\nlog = logging.getLogger(__nam(...TRUNCATED)
"Kw4NCgAAAAAAAAAA4wIAAOMAAAAAAAAAAAAAAAAFAAAAAAAAAfOcAAAAgABSAHQAXgBSAUkBSAJ0Ah8AXgBSAkkDdANdA1AIAAA(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\"","bytecode":"LOAD_CONST , '<mask_0>' <SEP> STORE_NAME , __doc__"},{(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\" <SEP> from <mask_3><mask_12> import <mask_6> <SEP> import <mask_13>(...TRUNCATED)
train
"ec5fcbc5b5175fa62626ccb5958115cef7c0473fa0fd4f4b55adc682f43f60c8575d817e6bf9cd584baa260c9a6568534cd(...TRUNCATED)
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"pypi-ec5fcbc5b5175fa62626ccb5958115cef7c0473fa0fd4f4b55adc682f43f60c8575d817e6bf9cd584baa260c9a6568(...TRUNCATED)
pypi
3.14
db6a4e7d3dcf3d6bcb866b82d0a6d9d47ad3fbe6be79e89662078bfd87e91118
"import copy\nimport warnings\nimport pulumi\nimport pulumi.runtime\nfrom typing import Any, Mapping(...TRUNCATED)
"Kw4NCgAAAAAAAAAAgSwAAOMAAAAAAAAAAAAAAAAFAAAAAAAAAPP+AAAAgABeAFIBSQB0AF4AUgFJAXQBXgBSAUkCdAJeAFIBSQN(...TRUNCATED)
[{"source":"import <mask_1>","bytecode":"LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , None <SEP> IMP(...TRUNCATED)
[{"source":"import <mask_18> <SEP> import <mask_19> <SEP> import <mask_20> <SEP> import <mask_21> <S(...TRUNCATED)
train
"ba5c0a6e42272d4cad3c1d2067603e7971c5dccc59687b113ebbf4b782b215b016b50d06caf867f46f612f3ac819b7c44cc(...TRUNCATED)
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"pypi-ba5c0a6e42272d4cad3c1d2067603e7971c5dccc59687b113ebbf4b782b215b016b50d06caf867f46f612f3ac819b7(...TRUNCATED)
pypi
3.14
6751c2b991eb5e06eb47be4d30484932cdcdd0f9cb9d8284fd57673aa2688bbd
"import os\nimport pathlib\nimport openai\nimport yaml\nimport json\nimport asyncio\nfrom tqdm impor(...TRUNCATED)
"Kw4NCgAAAAAAAAAA5wgAAOMAAAAAAAAAAAAAAAAOAAAAAAAAAPOuBQAAgABeAFIBSQB0AF4AUgFJAXQBXgBSAUkCdAJeAFIBSQN(...TRUNCATED)
[{"source":"import <mask_1>","bytecode":"LOAD_SMALL_INT , <mask_0> <SEP> LOAD_CONST , None <SEP> IMP(...TRUNCATED)
[{"source":"import <mask_36> <SEP> import <mask_37> <SEP> import <mask_38> <SEP> import <mask_39> <S(...TRUNCATED)
train
"a386758949a9d7e04263063bce93126fa86bfcbaa7bea0590d3f2741878cb8a6280aaf722366fa8d514bee48b044bf8c475(...TRUNCATED)
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pypi
3.14
603775f68685bf2af6b71284694b5899a5462d2a7c6321b423873b25ab012c5a
"from ai2_kit.core.artifact import Artifact\nfrom ai2_kit.core.log import get_logger\nfrom typing im(...TRUNCATED)
"Kw4NCgAAAAAAAAAAUgwAAOMAAAAAAAAAAAAAAAAGAAAAAAAAAPMiAQAAgABeAFIBSQBIAXQBHwBeAFICSQJIA3QDHwBeAFIDSQR(...TRUNCATED)
[{"source":"from <mask_0><mask_2> import <mask_1>","bytecode":"LOAD_SMALL_INT , <mask_0> <SEP> LOAD_(...TRUNCATED)
[{"source":"from <mask_0><mask_24> import <mask_13> <SEP> from <mask_0><mask_25> import <mask_14> <S(...TRUNCATED)
train
"92a040bacc2f35bb8cc686112869b6cf957820992e7a9869012ddc87827273fb62fc541294be20a6ff53be9be7da889cfde(...TRUNCATED)
"005b6a27d341d534234e8ce944bb7ca735ffe2ceeb34c8d70780357ac492743d5e5081bfd4c1cfc417b0423b583a8d96d9d(...TRUNCATED)
"pypi-92a040bacc2f35bb8cc686112869b6cf957820992e7a9869012ddc87827273fb62fc541294be20a6ff53be9be7da88(...TRUNCATED)
pypi
3.14
b11c549e37bbfa4ae9e7d99751056ff076b2a1617c3d4b973fafff6c6ad92b11
"\"\"\"pass\"\"\"\nimport paddle\nimport paddle.nn as nn\nfrom paddle.nn import Layer\nfrom paddlenl(...TRUNCATED)
"Kw4NCgAAAAAAAAAATDIAAOMAAAAAAAAAAAAAAAAGAAAAAAAAAPNSAQAAgABSAHQAXgBSAUkBdAFeAFIBSQJIA3QDHwBeAFICSQJ(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\"","bytecode":"LOAD_CONST , '<mask_0>' <SEP> STORE_NAME , __doc__"},{(...TRUNCATED)
[{"source":"\"\"\"<mask_0>\"\"\" <SEP> import <mask_32> <SEP> import <mask_33> as <mask_34> <SEP> fr(...TRUNCATED)
train
End of preview. Expand in Data Studio

syssec-utd/pylingual-shared-adapter-corpus-v2

Per-version source pools for the Python 3.14 and 3.15 LoRAs of the shared-backbone PyLingual study. Each row is one source file compiled for exactly one Python version, with every row the PyLingual converter produced. Validation files are exactly the sources whose canonical hash falls in the reserved bucket (int(canonical_sha256[:8], 16) % 10 == 0); training files never do.

  • Disjoint from the backbone corpus (journal sha256 207467ab130ca1468dcf81c26765cec7626d394d9687b6f128ed427643f0ad03), from every pylingual_eval_dataset v1/v2 source (exclusions sha256 79559f4d82e7b1bf5a14eb610eba87007378153bbe2f6e05743007e4abbf9f9f) and from each other, by raw SHA-512, canonical hash and NAS identity
  • Seed 20261102; sampled from /syssec-nas1/pyc-research/datasets
  • Code: https://github.com/quinntyx/pylingual branch feat-shared-backbone-lora, commit 87226a491a9ee76670601a6b3e8a37e8187241c5
Python Split Files PyPI CSN Statement rows Compiler
3.14 train 100000 90000 10000 9378298 3.14.6
3.14 validation 1000 900 100 95474 3.14.6
3.15 train 100000 90000 10000 9336777 3.15.0b2
3.15 validation 1000 900 100 97651 3.15.0b2

<version>/<split>/accepted.jsonl is the exact journal the trainers hash-check.

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