Update README.md

README.md

@@ -69,4 +69,68 @@ configs:
   data_files:
   - split: train
     path: var-09/train.jsonl
----
+---
+
+# Re-DocRED-CF
+
+Many datasets have been developed to train and evaluate document-level relation extraction (RE) models. Most of these are constructed using real-world data. However, it has been shown that RE models trained on real-world data suffer from factual biases. To evaluate and address this issue, we present [**CovEReD** (Paper)](https://www.arxiv.org/abs/2407.06699), a counterfactual data generation approach for document-level relation extraction datasets through entity replacement.
+
+Using our pipeline, we have generated **Re-DocRED-CF**, a dataset of counterfactual RE documents, to help evaluate and address inconsistencies in document-level RE.
+This repo contains five counterfactual variations of the seed dataset, i.e., Re-DocRED. All five sets of train/dev/test dataset files are available here through the HuggingFace Datasets API 🤗.
+
+To select a specific variation (e.g. `var-01`):
+```python
+dataset = load_dataset("amodaresi/Re-DocRED-CF", "var-01")
+```
+#### Output:
+```python
+DatasetDict({
+    train: Dataset({
+        features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
+        num_rows: 2870
+    })
+    dev: Dataset({
+        features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
+        num_rows: 466
+    })
+    test: Dataset({
+        features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
+        num_rows: 453
+    })
+    train_mix: Dataset({
+        features: ['title', 'labels', 'original_doc_id', 'vertexSet', 'sents'],
+        num_rows: 5923
+    })
+})
+```
+The `train_mix` is the original training set combined with its counterfactual variation counterpart.
+We have also included four additional training set variations (var-[06, 07, 08, 09]), though they were not used in the evaluations presented in our paper.
+
+The properties `title`, `labels`, `vertexSet`, and `sents` are structured similarly to those in the original DocRED & Re-DocRED datasets:
+
+- `title`: Document title.
+- `labels`: List of relations. Each entry indicates the relation between a head and a tail entity, with some entries also specifying evidence sentences.
+- `vertexSet`: List of entity vertex sets. Each entry represents a vertex specifying all mentions of an entity by their position in the document, along with their type.
+- `sents`: Tokenized sentences.
+
+In examples that are counterfactually generated, the title includes a variation number. For example: `AirAsia Zest ### 1`.
+The `original_doc_id` denotes the index of the example in the original seed dataset, i.e., Re-DocRED.
+
+## GitHub Repo & Paper
+For more information about the **CovEReD** pipeline, refer to:
+
+- 📄 Paper: "[Consistent Document-Level Relation Extraction via Counterfactuals](https://www.arxiv.org/abs/2407.06699)"
+- 🔗 GitHub Repo: [https://github.com/amodaresi/CovEReD](https://github.com/amodaresi/CovEReD)
+
+## Cite
+If you use the dataset, **CovEReD** pipeline, or code from this repository, please cite the paper:
+```bibtex
+@inproceedings{modarressi-covered-2024,
+    title="Consistent Document-Level Relation Extraction via Counterfactuals", 
+    author="Ali Modarressi and Abdullatif Köksal and Hinrich Schütze",
+    year="2024",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    address = "Miami, United States",
+    publisher = "Association for Computational Linguistics",
+}
+```