Update README.md

README.md

@@ -1,4 +1,21 @@
 ---
+annotations_creators:
+- expert-generated
+language_creators:
+- found
+language:
+- en
+license: cc-by-nc-sa-4.0
+multilinguality:
+- monolingual
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- text-classification
+task_ids: []
+pretty_name: EnvironmentalClaims
 dataset_info:
   features:
   - name: text
@@ -22,6 +39,127 @@ dataset_info:
   download_size: 307811
   dataset_size: 494007
 ---
-# Dataset Card for "environmental_claims"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+# Dataset Card for environmental_claims
+
+## Dataset Description
+
+- **Homepage:** [climatebert.ai](https://climatebert.ai)
+- **Repository:**
+- **Paper:** [arxiv.org/abs/2209.00507](https://arxiv.org/abs/2209.00507)
+- **Leaderboard:**
+- **Point of Contact:** [Dominik Stammbach](mailto:[email protected])
+
+### Dataset Summary
+
+We introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.
+
+### Supported Tasks and Leaderboards
+
+The dataset supports a binary classification task of whether a given sentence is an environmental claim or not.
+
+### Languages
+
+The text in the dataset is in English.
+
+## Dataset Structure
+
+### Data Instances
+
+```
+{
+    "text": "It will enable E.ON to acquire and leverage a comprehensive understanding of the transfor- mation of the energy system and the interplay between the individual submarkets in regional and local energy supply sys- tems.",
+    "label": 0
+}
+```
+
+### Data Fields
+
+- text: a sentence extracted from corporate annual reports, sustainability reports and earning calls transcripts
+- label: the label (0 -> no environmental claim, 1 -> environmental claim)
+
+### Data Splits
+
+The dataset is split into:
+- train: 2,400
+- validation: 300
+- test: 300
+
+## Dataset Creation
+
+### Curation Rationale
+
+[More Information Needed]
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Our dataset contains environmental claims by firms, often in the financial domain. We collect text from corporate annual reports, sustainability reports, and earning calls transcripts.
+
+For more information regarding our sample selection, please refer to Appendix B of our paper, which is provided for [citation](#citation-information).
+
+#### Who are the source language producers?
+
+Mainly large listed companies.
+
+### Annotations
+
+#### Annotation process
+
+For more information on our annotation process and annotation guidelines, please refer to Appendix C of our paper, which is provided for [citation](#citation-information).
+
+#### Who are the annotators?
+
+The authors and students at University of Zurich with majors in finance and sustainable finance.
+
+### Personal and Sensitive Information
+
+Since our text sources contain public information, no personal and sensitive information should be included.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+- Dominik Stammbach
+- Nicolas Webersinke
+- Julia Anna Bingler
+- Mathias Kraus
+- Markus Leippold
+
+### Licensing Information
+
+This dataset is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International license (cc-by-nc-sa-4.0). To view a copy of this license, visit [creativecommons.org/licenses/by-nc-sa/4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+
+If you are interested in commercial use of the dataset, please contact [[email protected]](mailto:[email protected]).
+
+### Citation Information
+
+```bibtex
+@misc{stammbach2022environmentalclaims,
+  title = {A Dataset for Detecting Real-World Environmental Claims},
+  author = {Stammbach, Dominik and Webersinke, Nicolas and Bingler, Julia Anna and Kraus, Mathias and Leippold, Markus},
+  year = {2022},
+  doi = {10.48550/ARXIV.2209.00507},
+  url = {https://arxiv.org/abs/2209.00507},
+  publisher = {arXiv},
+}
+```
+
+### Contributions
+
+Thanks to [@webersni](https://github.com/webersni) for adding this dataset.