Update README.md

README.md

@@ -1,3 +1,150 @@
 ---
 license: apache-2.0
+language:
+- en
+pretty_name: ConstructCIE
+size_categories:
+- n<1K
+task_categories:
+- text2text-generation
+- text-classification
+tags:
+- information-extraction
+- causal-extraction
+- construction-safety
+- accident-reports
 ---
+
+# Dataset Card for ConstructCIE
+
+<!-- Provide a quick summary of the dataset. -->
+
+ConstructCIE is a dataset for extracting causal information from construction accident narratives. Each accident report is annotated with a hierarchy of causal factors (managerial, behavioral, equipment, and working-condition factors), circumstances, and consequences, supporting both extraction and classification tasks.
+
+## Dataset Details
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+The dataset contains 530 English construction accident narratives. Each narrative is annotated with a tree of causal information: **extraction** nodes carry supporting text spans and keywords, and **classification** nodes carry categorical labels (accident type, construction trade, severity). It supports two settings: End-to-End Joint Hierarchical Extraction (**JHE**) and End-to-End Individual Hierarchical Extraction (**IHE**).
+
+- **Curated by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0
+
+### Dataset Sources [optional]
+
+<!-- Provide the basic links for the dataset. -->
+
+- **Repository:** https://github.com/Sussycat/TextEE_Hung
+- **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. -->
+
+Benchmarking LLMs (zero-/few-shot) and supervised models on hierarchical causal information extraction from safety narratives: extracting causal factor spans, and classifying accident type, construction trade, and severity.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
+
+Not intended for regulatory or compliance decisions, incident liability determination, or as an exhaustive record of construction accidents.
+
+## 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. -->
+
+Each record is one JSON line with three top-level fields:
+
+- `id` — unique record id
+- `accident_report` — the narrative text with its hierarchical annotation tree
+- `accident_type` — classification: `caught-in/between`, `electrocution`, `fall`, or `struck-by`
+
+Nodes in the `accident_report` tree are either **extraction** nodes (`{text, keywords, children}`) or **classification** nodes (`{value}`), organized as:
+
+- `working_circumstances` → `construction_trade` (17 CSI-division classes)
+- `managerial_factors` → `failure_of_hazard_management`, `deficiency_in_safety_training`
+- `working_condition_factors` → `weather_condition`, `workspace_condition`
+- `equipment_factors` → `protective_equipment_condition`, `work_equipment_condition`
+- `behavioral_factors` → `inattentive_behavior`, `noncompliant_behavior`
+- `consequences` → `severity` (`fatality`, `hospitalized injury`, `non hospitalized injury`), `affected_body_part`
+- `object_involved`
+
+The dataset ships with five alternate train/val/test partitions (`split1`–`split5`) of the same 530 records, 424/53/53 each, for multi-seed evaluation.
+
+## Dataset Creation
+
+### Curation Rationale
+
+<!-- Motivation for the creation of this dataset. -->
+
+Construction accident reports contain rich causal information locked in free text. Structuring it as a hierarchy of causal factors enables systematic safety analysis and provides a benchmark for hierarchical information extraction methods.
+
+### Source Data
+
+<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
+
+#### 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]
+
+### Annotations [optional]
+
+<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
+
+#### Annotation process
+
+<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+<!-- This section describes the people or systems who created the annotations. -->
+
+[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.). -->
+
+Narratives describe real workplace injuries and fatalities. Workers are referred to anonymously (e.g., "Employee #1") and no names or direct identifiers appear in the text.
+
+## 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
+
+<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->
+
+```bibtex
+@article{
+}
+```
+