Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- text-classification
+- summarization
+language:
+- en
+tags:
+- named-entity-recognition
+- synthetic-data
+---
+# Dataset Card for WHODUNIT: Evaluation Benchmark for Culprit Detection in Mystery Stories
+
+This dataset contains crime and mystery novels along with their metadata. Each entry includes the full text, title, author, book length, and a list of identified culprits. Additionally, an augmented version of the dataset introduces entity replacements and synthetic data variations.
+
+## Dataset Details
+
+### Dataset Description
+
+- **Language(s):** English
+- **License:** Apache-2.0
+
+### Dataset Sources
+
+- **Repository:** [WhoDunIt Evaluation Benchmark](https://github.com/kjgpta/WhoDunIt-Evaluation_benchmark_for_culprit_detection_in_mystery_stories)
+
+## Uses
+
+### Direct Use
+
+This dataset can be used for:
+- Training models for text classification based on authorship, themes, or book characteristics.
+- Named Entity Recognition (NER) for detecting culprits and other entities in crime stories.
+- Summarization tasks for generating concise descriptions of mystery novels.
+- Text generation and storytelling applications.
+- Evaluating models' robustness against entity alterations using the augmented dataset.
+
+### Out-of-Scope Use
+
+- The dataset should not be used for real-world criminal investigations or forensic profiling.
+- Any misuse involving biased predictions or unethical AI applications should be avoided.
+
+## Dataset Structure
+
+### Data Fields
+#### **Original Dataset**
+- `text` (*string*): The full text or an excerpt from the novel.
+- `title` (*string*): The title of the novel.
+- `author` (*string*): The author of the novel.
+- `length` (*integer*): The number of pages in the novel.
+- `culprit_ids` (*list of strings*): The list of culprits in the story.
+
+#### **Augmented Dataset**
+- Contains the same fields as the original dataset.
+- Additional field:
+  - `metadata` (*dict*): Information on entity replacement strategies (e.g., replacing names with fictional or thematic counterparts).
+- Modified `culprit_ids`: The culprits' names have been replaced using different replacement styles (e.g., random names, thematic names, etc.).
+
+### Data Splits
+Both the original and augmented datasets are provided as single corpora without predefined splits.
+
+## Dataset Creation
+
+### Curation Rationale
+
+This dataset was curated to aid in the study of crime fiction narratives and their structural patterns, with a focus on culprit detection in mystery stories. The augmented dataset was created to test the robustness of NLP models against entity modifications.
+
+### Source Data
+
+#### Data Collection and Processing
+
+The original dataset is curated from public domain literary works. The text is processed to extract relevant metadata such as title, author, book length, and named culprits.
+
+The augmented dataset introduces variations using entity replacement techniques, where character names are substituted based on predefined rules (e.g., random names, theme-based replacements, etc.).
+
+#### Who are the source data producers?
+
+The dataset is composed of classic crime and mystery novels written by renowned authors such as Agatha Christie, Arthur Conan Doyle, and Fyodor Dostoevsky.
+
+## Bias, Risks, and Limitations
+
+- The dataset consists primarily of classic literature, which may not reflect modern storytelling techniques.
+- The augmented dataset's entity replacements may introduce artificial biases.
+- It may have inherent biases based on the cultural and historical context of the original works.
+
+
+## Citation
+
+**BibTeX:**
+```
+@misc{gupta2025whodunitevaluationbenchmarkculprit,
+      title={WHODUNIT: Evaluation benchmark for culprit detection in mystery stories}, 
+      author={Kshitij Gupta},
+      year={2025},
+      eprint={2502.07747},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.07747}, 
+}
+```