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hieuhocnlp
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deep-research

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The dataset tries to import a module that is not installed.

Error code:   DatasetModuleNotInstalledError
Exception:    ImportError
Message:      To be able to use hieuhocnlp/deep-research, you need to install the following dependencies: boto3, botocore.
Please install them using 'pip install boto3 botocore' for instance.
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 1481, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1202, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 294, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use hieuhocnlp/deep-research, you need to install the following dependencies: boto3, botocore.
              Please install them using 'pip install boto3 botocore' for instance.

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Dataset Card for "deep-research"

Dataset Summary

Briefly summarize the dataset...

Supported Tasks and Leaderboards

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Languages

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Dataset Structure

Data Instances.

train.json

Size of downloaded dataset files: 181.42 MB
Size of the generated dataset: 522.66 MB
Total amount of disk used: 704.07 MB

An example of 'train' looks as follows.

This example was too long and was cropped:
{'id': '5733be284776f41900661182',
 'title': 'University_of_Notre_Dame',
 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary...',
 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}
 }

dev.json

Size of downloaded dataset files: 183.09 MB
Size of the generated dataset: 523.97 MB
Total amount of disk used: 707.06 MB

An example of 'devepopment' looks as follows.

This example was too long and was cropped:

{'id': '5733be284776f41900661182',
 'title': 'University_of_Notre_Dame',
 'context': 'Architecturally, the school has a Catholic character. Atop the Main Building\'s gold dome is a golden statue of the Virgin Mary...',
 'question': 'To whom did the Virgin Mary allegedly appear in 1858 in Lourdes France?',
 'answers': {'text': ['Saint Bernadette Soubirous'], 'answer_start': [515]}
 }

Data Fields

id: ID of the context, question unit
title: Title of the question ...

Data Splits

	train	development	test
Input Sentences
Average Sentence Length

Dataset Creation

Considerations for Using the Data

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Additional Information

Licensing Information

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Citation Information

Provide the BibTex-formatted reference for the dataset. For example:

@inproceedings{cheng-etal-2021-multimodal,
    title = "Multimodal Phased Transformer for Sentiment Analysis",
    author = "Cheng, Junyan  and
      Fostiropoulos, Iordanis  and
      Boehm, Barry  and
      Soleymani, Mohammad",
    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2021",
    address = "Online and Punta Cana, Dominican Republic",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.emnlp-main.189",
    doi = "10.18653/v1/2021.emnlp-main.189",
    pages = "2447--2458",
    abstract = "Multimodal Transformers achieve superior performance in multimodal learning tasks. However, the quadratic complexity of the self-attention mechanism in Transformers limits their deployment in low-resource devices and makes their inference and training computationally expensive. We propose multimodal Sparse Phased Transformer (SPT) to alleviate the problem of self-attention complexity and memory footprint. SPT uses a sampling function to generate a sparse attention matrix and compress a long sequence to a shorter sequence of hidden states. SPT concurrently captures interactions between the hidden states of different modalities at every layer. To further improve the efficiency of our method, we use Layer-wise parameter sharing and Factorized Co-Attention that share parameters between Cross Attention Blocks, with minimal impact on task performance. We evaluate our model with three sentiment analysis datasets and achieve comparable or superior performance compared with the existing methods, with a 90{\%} reduction in the number of parameters. We conclude that (SPT) along with parameter sharing can capture multimodal interactions with reduced model size and improved sample efficiency.",
}