hieuhocnlp/deep-research

--- annotations_creators: [] language: [] language_creators: - found license: [] multilinguality: - monolingual pretty_name: Deep Research USC size_categories: - 10B<n<100B source_datasets: [] tags: - continual learning task_categories: [] task_ids: [] --- # Dataset Card for "deep-research" ## Table of Contents - [Table of Contents](#table-of-contents) - [Dataset Description](#dataset-description) - [Dataset Summary](#dataset-summary) - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) - [Languages](#languages) - [Dataset Structure](#dataset-structure) - [Data Instances](#data-instances) - [Data Fields](#data-fields) - [Data Splits](#data-splits) - [Dataset Creation](#dataset-creation) - [Curation Rationale](#curation-rationale) - [Source Data](#source-data) - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) - [Who are the source language producers?](#who-are-the-source-language-producers) - [Annotations](#annotations) - [Annotation process](#annotation-process) - [Who are the annotators?](#who-are-the-annotators) - [Personal and Sensitive Information](#personal-and-sensitive-information) - [Considerations for Using the Data](#considerations-for-using-the-data) - [Social Impact of Dataset](#social-impact-of-dataset) - [Discussion of Biases](#discussion-of-biases) - [Other Known Limitations](#other-known-limitations) - [Additional Information](#additional-information) - [Dataset Curators](#dataset-curators) - [Licensing Information](#licensing-information) - [Citation Information](#citation-information) - [Contributions](#contributions) ## Dataset Description - **Homepage:** [Deep USC Research](http://deep.usc.edu/) - **Repository:** [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) - **Paper:** [Multimodal Phased Transformer for Sentiment Analysis](https://aclanthology.org/2021.emnlp-main.189.pdf) - **Point of Contact:** [Iordanis Fostiropoulos](mailto:[e-mail]) ### Dataset Summary Briefly summarize the dataset... ### Supported Tasks and Leaderboards [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Languages [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## 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 ### Curation Rationale [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Source Data [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Annotations [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Personal and Sensitive Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Considerations for Using the Data [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ## Additional Information ### Licensing Information [More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards) ### Citation Information Provide the [BibTex](http://www.bibtex.org/)-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.", } ``` ### Contributions Thanks to [@github-username](https://github.com/<github-username>) for adding this dataset.

原始信息汇总

数据集概述

基本信息

• 数据集名称: Deep Research USC
• 数据集别名: deep-research
• 数据集大小: 10B<n<100B
• 语言: 单语种
• 数据集标签: 持续学习

数据集结构

• 数据实例:
  • train.json: 下载大小181.42 MB，生成后大小522.66 MB，总磁盘使用704.07 MB。
  • dev.json: 下载大小183.09 MB，生成后大小523.97 MB，总磁盘使用707.06 MB。
• 数据字段:
  • id: 上下文、问题单元的ID。
  • title: 问题的标题。

数据集创建

• 数据收集与规范化: 信息未提供。
• 源语言生产者: 信息未提供。
• 注释过程: 信息未提供。
• 注释者: 信息未提供。

其他信息

• 论文: Multimodal Phased Transformer for Sentiment Analysis
• 联系人: Iordanis Fostiropoulos ([e-mail])
• 许可证: 信息未提供。
• 引用信息: 提供BibTex格式的引用信息。

数据集详细信息

数据实例示例

• train.json示例: json { "id": "5733be284776f41900661182", "title": "University_of_Notre_Dame", "context": "Architecturally, the school has a Catholic character. Atop the Main Buildings 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]} }

数据分割

• 数据分割详情: 信息未提供。

数据集贡献

• 贡献者: 信息未提供。