Dataset Viewer
View in Dataset Viewer
Viewer
The dataset viewer is not available for this dataset.
Cannot get the config names for the dataset.
Error code: ConfigNamesError Exception: ValueError Message: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('csv', {})} Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response for config in sorted(get_dataset_config_names(path=dataset, token=hf_token)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names dataset_module = dataset_module_factory( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1512, in dataset_module_factory raise e1 from None File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1489, in dataset_module_factory return HubDatasetModuleFactoryWithoutScript( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1054, in get_module module_name, default_builder_kwargs = infer_module_for_data_files( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 513, in infer_module_for_data_files raise ValueError(f"Couldn't infer the same data file format for all splits. Got {split_modules}") ValueError: Couldn't infer the same data file format for all splits. Got {NamedSplit('train'): ('json', {}), NamedSplit('validation'): ('json', {}), NamedSplit('test'): ('csv', {})}
Need help to make the dataset viewer work? Open a discussion for direct support.
YAML Metadata
Warning:
The task_categories "Code Generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
YAML Metadata
Warning:
The task_categories "Translation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
YAML Metadata
Warning:
The task_categories "Text2Text generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, text2text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, other
CoNaLa Dataset for Code Generation
Table of content
Dataset Descritpion
This dataset has been processed for Code Generation. CMU CoNaLa, the Code/Natural Language Challenge is a joint project of the Carnegie Mellon University NeuLab and STRUDEL Lab. This dataset was designed to test systems for generating program snippets from natural language. It is avilable at https://conala-corpus.github.io/ , and this is about 13k records from the full corpus of about 600k examples.
Languages
English
Dataset Structure
Data Instances
A sample from this dataset looks as follows:
[
{
"intent": "convert a list to a dictionary in python",
"snippet": "b = dict(zip(a[0::2], a[1::2]))"
},
{
"intent": "python - sort a list of nested lists",
"snippet": "l.sort(key=sum_nested)"
}
]
Dataset Fields
The dataset has the following fields (also called "features"):
{
"intent": "Value(dtype='string', id=None)",
"snippet": "Value(dtype='string', id=None)"
}
Dataset Splits
This dataset is split into a train, validation and test split. The split sizes are as follow:
Split name | Num samples |
---|---|
train | 11125 |
valid | 1237 |
test | 500 |
- Downloads last month
- 173