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dl400

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The dataset generation failed

Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
task_id: string
direction: string
pandas_final_code: string
pandas_test_cases: list<item: string>
  child 0, item: string
numpy_final_code: string
numpy_test_cases: list<item: string>
  child 0, item: string
pandas_input_data: string
numpy_input_data: string
tensorflow_final_test_cases: list<item: string>
  child 0, item: string
pytorch_final_code: string
tensorflow_final_code: string
pytorch_final_test_cases: list<item: string>
  child 0, item: string
to
{'task_id': Value('string'), 'direction': Value('string'), 'pytorch_final_code': Value('string'), 'pytorch_final_test_cases': List(Value('string')), 'tensorflow_final_code': Value('string'), 'tensorflow_final_test_cases': List(Value('string'))}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1779, in _prepare_split_single
                  for key, table in generator:
                                    ^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              task_id: string
              direction: string
              pandas_final_code: string
              pandas_test_cases: list<item: string>
                child 0, item: string
              numpy_final_code: string
              numpy_test_cases: list<item: string>
                child 0, item: string
              pandas_input_data: string
              numpy_input_data: string
              tensorflow_final_test_cases: list<item: string>
                child 0, item: string
              pytorch_final_code: string
              tensorflow_final_code: string
              pytorch_final_test_cases: list<item: string>
                child 0, item: string
              to
              {'task_id': Value('string'), 'direction': Value('string'), 'pytorch_final_code': Value('string'), 'pytorch_final_test_cases': List(Value('string')), 'tensorflow_final_code': Value('string'), 'tensorflow_final_test_cases': List(Value('string'))}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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task_id string	direction string	pytorch_final_code string	pytorch_final_test_cases list	tensorflow_final_code string	tensorflow_final_test_cases list
142_0	pytorch_to_tensorflow	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, embed_dim: int, head_dim: int): super().__init__() self.embed_dim = embed_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n \n batch_size, seq_len = 2, 10\n x = torch.randn(batch_size, seq_len, embed_dim)\n \n output, attn_weights = head(x)\n \n assert output.shape ...	import tensorflow as tf import numpy as np from typing import Tuple, Optional class AttentionHead(tf.keras.layers.Layer): def __init__(self, embed_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.embed_dim = embed_dim self.head_dim = head_dim self.query =...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n \n batch_size, seq_len = 2, 10\n x = tf.random.normal((batch_size, seq_len, embed_dim))\n \n output, attn_weights = head(x)\n \n assert output...
142_1	tensorflow_to_pytorch	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, embed_dim: int, head_dim: int): super().__init__() self.embed_dim = embed_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n \n batch_size, seq_len = 2, 10\n x = torch.randn(batch_size, seq_len, embed_dim)\n \n output, attn_weights = head(x)\n \n assert output.shape ...	import tensorflow as tf import numpy as np from typing import Tuple, Optional class AttentionHead(tf.keras.layers.Layer): def __init__(self, embed_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.embed_dim = embed_dim self.head_dim = head_dim self.query =...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n \n batch_size, seq_len = 2, 10\n x = tf.random.normal((batch_size, seq_len, embed_dim))\n \n output, attn_weights = head(x)\n \n assert output...
131_0	pytorch_to_tensorflow	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, input_dim: int, head_dim: int): super().__init__() self.input_dim = input_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n head = AttentionHead(64, 32)\n x = torch.randn(2, 10, 64)\n output, attn_weights = head(x)\n assert output.shape == (2, 10, 32), f\"Output shape mismatch: {output.shape}\"\n assert attn_weights.shape == (2, 10, 10), f\"Attention ...	import tensorflow as tf import numpy as np from typing import Tuple, Optional, List class AttentionHead(tf.keras.layers.Layer): def __init__(self, input_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.input_dim = input_dim self.head_dim = head_dim self.q...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n head = AttentionHead(64, 32)\n x = tf.random.normal((2, 10, 64))\n output, attn_weights = head(x)\n assert tuple(output.shape) == (2, 10, 32), f\"Output shape mismatch: {output.shape}\"\n assert tuple(attn_weights.shape) == (2, 1...
131_1	tensorflow_to_pytorch	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, input_dim: int, head_dim: int): super().__init__() self.input_dim = input_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n head = AttentionHead(64, 32)\n x = torch.randn(2, 10, 64)\n output, attn_weights = head(x)\n assert output.shape == (2, 10, 32), f\"Output shape mismatch: {output.shape}\"\n assert attn_weights.shape == (2, 10, 10), f\"Attention ...	import tensorflow as tf import numpy as np from typing import Tuple, Optional, List class AttentionHead(tf.keras.layers.Layer): def __init__(self, input_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.input_dim = input_dim self.head_dim = head_dim self.q...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n head = AttentionHead(64, 32)\n x = tf.random.normal((2, 10, 64))\n output, attn_weights = head(x)\n assert tuple(output.shape) == (2, 10, 32), f\"Output shape mismatch: {output.shape}\"\n assert tuple(attn_weights.shape) == (2, 1...
149_0	pytorch_to_tensorflow	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, embed_dim: int, head_dim: int): super().__init__() self.embed_dim = embed_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n x = torch.randn(2, 10, embed_dim)\n output, attn_weights = head(x)\n assert output.shape == (2, 10, head_dim), f\"Expected {(2, 10, head_dim)}, got {outpu...	import tensorflow as tf import numpy as np from typing import Tuple, Optional, List class AttentionHead(tf.keras.layers.Layer): def __init__(self, embed_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.embed_dim = embed_dim self.head_dim = head_dim self.q...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n x = tf.random.normal((2, 10, embed_dim))\n output, attn_weights = head(x)\n assert output.shape == (2, 10, head_dim), f\"Expected {(2, 10, head_dim)}, got...
149_1	tensorflow_to_pytorch	import torch import torch.nn as nn import torch.optim as optim import numpy as np from typing import Tuple, Optional class AttentionHead(nn.Module): def __init__(self, embed_dim: int, head_dim: int): super().__init__() self.embed_dim = embed_dim self.head_dim = head_dim sel...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n x = torch.randn(2, 10, embed_dim)\n output, attn_weights = head(x)\n assert output.shape == (2, 10, head_dim), f\"Expected {(2, 10, head_dim)}, got {outpu...	import tensorflow as tf import numpy as np from typing import Tuple, Optional, List class AttentionHead(tf.keras.layers.Layer): def __init__(self, embed_dim: int, head_dim: int, kwargs): super().__init__(kwargs) self.embed_dim = embed_dim self.head_dim = head_dim self.q...	[ "def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64, 16\n head = AttentionHead(embed_dim, head_dim)\n x = tf.random.normal((2, 10, embed_dim))\n output, attn_weights = head(x)\n assert output.shape == (2, 10, head_dim), f\"Expected {(2, 10, head_dim)}, got...
133_0	pytorch_to_tensorflow	"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nfrom typing i(...TRUNCATED)	["def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64(...TRUNCATED)	"import tensorflow as tf\nimport numpy as np\nfrom typing import Tuple, Optional\n\nclass AttentionH(...TRUNCATED)	["def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64(...TRUNCATED)
133_1	tensorflow_to_pytorch	"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nfrom typing i(...TRUNCATED)	["def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64(...TRUNCATED)	"import tensorflow as tf\nimport numpy as np\nfrom typing import Tuple, Optional\n\nclass AttentionH(...TRUNCATED)	["def test_case():\n # Test 1: Basic functionality of AttentionHead\n embed_dim, head_dim = 64(...TRUNCATED)
183_0	pytorch_to_tensorflow	"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\n# Cus(...TRUNCATED)	["def test_case():\n # Test correct output shapes\n expected_output_shape = (5, 3, 40) # seq_(...TRUNCATED)	"import tensorflow as tf\nimport numpy as np\n\n\n# Custom RNN implementation with detached gradient(...TRUNCATED)	["def test_case():\n # Test correct output shapes\n expected_output_shape = (5, 3, 40) # seq_(...TRUNCATED)
183_1	tensorflow_to_pytorch	"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\n\n# Cus(...TRUNCATED)	["def test_case():\n # Test correct output shapes\n expected_output_shape = (5, 3, 40) # seq_(...TRUNCATED)	"import tensorflow as tf\nimport numpy as np\n\n\n# Custom RNN implementation with detached gradient(...TRUNCATED)	["def test_case():\n # Test correct output shapes\n expected_output_shape = (5, 3, 40) # seq_(...TRUNCATED)

End of preview.

ORCA

This directory contains the data release for the ORCA benchmark.

dq600-00000-of-00001.jsonl: grounding-level data querying instances.
dm600-00000-of-00001.jsonl: grounding-level data manipulation instances.
dl400-00000-of-00001.jsonl: grounding-level deep learning instances.
project200-00000-of-00001.jsonl: project-level tasks stored in JSON format.
reconstruct_project_data.py: script for reconstructing the project-level directory structure from project200-00000-of-00001.jsonl.

Statistics

Grounding level: 1600 JSONL instances across DQ, DM, and DL.
Project level: 200 task records.

Reconstruct Project Data

Run the following command from this directory:

python3 reconstruct_project_data.py --input project200-00000-of-00001.jsonl --output project

This command creates a project/ directory and reconstructs the text-based project files for each task.

Notes

This directory contains data only.
Code, prompts, and execution environment are released separately.

Downloads last month: 1

Total file size:

28.2 MB

Datasets:

user95783
/

orca

ORCA

Contents

Statistics

Reconstruct Project Data

Notes