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Cannot get the config names for the dataset.
Error code:   ConfigNamesError
Exception:    RuntimeError
Message:      Dataset scripts are no longer supported, but found stan_small.py
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1031, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 989, in dataset_module_factory
                  raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
              RuntimeError: Dataset scripts are no longer supported, but found stan_small.py

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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, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, 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-ranking, 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, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

Dataset Card for STAN Small

Dataset Summary

Manually Annotated Stanford Sentiment Analysis Dataset by Bansal et al..

Languages

English

Dataset Structure

Data Instances

{
    "index": 300,
    "hashtag": "microsoftfail",
    "segmentation": "microsoft fail",
    "alternatives": {
        "segmentation": [
            "Microsoft fail"
        ]
    }
}

Data Fields

  • index: a numerical index.
  • hashtag: the original hashtag.
  • segmentation: the gold segmentation for the hashtag.
  • alternatives: other segmentations that are also accepted as a gold segmentation.

Although segmentation has exactly the same characters as hashtag except for the spaces, the segmentations inside alternatives may have characters corrected to uppercase.

Dataset Creation

  • All hashtag segmentation and identifier splitting datasets on this profile have the same basic fields: hashtag and segmentation or identifier and segmentation.

  • The only difference between hashtag and segmentation or between identifier and segmentation are the whitespace characters. Spell checking, expanding abbreviations or correcting characters to uppercase go into other fields.

  • There is always whitespace between an alphanumeric character and a sequence of any special characters ( such as _ , :, ~ ).

  • If there are any annotations for named entity recognition and other token classification tasks, they are given in a spans field.

Additional Information

Citation Information

@misc{bansal2015deep,
      title={Towards Deep Semantic Analysis Of Hashtags}, 
      author={Piyush Bansal and Romil Bansal and Vasudeva Varma},
      year={2015},
      eprint={1501.03210},
      archivePrefix={arXiv},
      primaryClass={cs.IR}
}

Contributions

This dataset was added by @ruanchaves while developing the hashformers library.

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