dataset card skeleton

README.md

@@ -3,10 +3,18 @@ license: odbl
 language:
 - en
 tags:
-- chemistry
-- biology
+- science
+- evaluation
+- academic-documents
 - art
+- biology
+- chemistry
+- computer-science
 - medical
+- mathematics
+- physics
+- psychology
+- sociology
 size_categories:
 - 1M<n<10M
 dataset_info:
@@ -6283,4 +6291,125 @@ configs:
     path: Sociology,1970-2022/val/*
   - split: test
     path: Sociology,1970-2022/test/*
----
+---
+# Dataset Card for Modular S2ORC
+
+<!-- Provide a quick summary of the dataset. -->
+
+Topically and temporally partitioned data from [S2ORC](https://aclanthology.org/2020.acl-main.447/), used for continued pre-training experiments in "Scalable Data Ablation Approximations for Language Models through Modular Training and Merging" to be presented at EMNLP 2024.
+
+Validation and test splits are determined by 'sha1' values in metadata.
+
+More details to come.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+
+
+- **Curated by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** English
+- **License:** [More Information Needed]
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/clarana/ez-data-ablations
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the dataset is intended to be used. -->
+
+### Direct Use
+
+<!-- This section describes suitable use cases for the dataset. -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+[More Information Needed]
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+[More Information Needed]
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+[More Information Needed]
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+This dataset is a re-partitioned Scientific documents available on SemanticScholar.
+
+#### Data Collection and Processing
+
+<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
+
+[More Information Needed]
+
+#### Who are the source data producers?
+
+<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
+
+[More Information Needed]
+
+
+#### Personal and Sensitive Information
+
+<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+
+**BibTeX:**
+
+@inproceedings{na2024scalable,
+        author    = {Clara Na and Ian Magnusson and Ananya Harsh Jha and Tom Sherborne and Emma Strubell and Jesse Dodge and Pradeep Dasigi},
+        title     = {Scalable Data Ablation Approximations for Language Models through Modular Training and Merging},
+        booktitle = {The 2024 Conference on Empirical Methods in Natural Language Processing,
+        year      = {2024},
+        url       = {https://openreview.net/forum?id=clE6jmcnm4},
+    }
+
+**APA:**
+
+[More Information Needed]
+
+
+## Dataset Card Contact
+
+Clara Na ([email protected])