Update README.md

README.md

@@ -1,3 +1,72 @@
 ---
 license: cc-by-nc-sa-4.0
+language:
+- es
+pretty_name: AbstRCT-ES
 ---
+---
+dataset_info:
+- config_name: es
+  data_files:
+  - split: neoplasm_train
+    path: es/neoplasm_train-*
+  - split: neoplasm_dev
+    path: es/neoplasm_dev-*
+  - split: neoplasm_test
+    path: es/neoplasm_test-*
+  - split: glaucoma_test
+    path: es/glaucoma_test-*
+  - split: mixed_test
+    path: es/mixed_test-*
+license: apache-2.0
+task_categories:
+- token-classification
+language:
+- es
+tags:
+- biology
+- medical
+pretty_name: AbstRCT-ES
+---
+
+<p align="center">
+    <br>
+    <img src="http://www.ixa.eus/sites/default/files/anitdote.png" style="width: 30%;">
+    <h2 align="center">AbstRCT-ES</h2>
+    <be>
+
+
+We translate the [AbstRCT English Argument Mining Dataset](https://gitlab.com/tomaye/abstrct) to generate a parallel Spanish version
+using DeepL; labels are projected using [Easy Label Projection](https://github.com/ikergarcia1996/Easy-Label-Projection) and manually corrected.
+
+  - 📖 Paper: [Crosslingual Argument Mining in the Medical Domain](https://arxiv.org/abs/2301.10527)
+  - 🌐 Project Website: [https://univ-cotedazur.eu/antidote](https://univ-cotedazur.eu/antidote)
+  - Code: [https://github.com/ragerri/abstrct-projections/tree/final](https://github.com/ragerri/abstrct-projections/tree/final) 
+  - Funding: CHIST-ERA XAI 2019 call. Antidote (PCI2020-120717-2) funded by MCIN/AEI /10.13039/501100011033 and by European Union NextGenerationEU/PRTR
+
+## Labels
+```python
+{
+"O": 0,
+"B-Claim": 1,
+"I-Claim": 2,
+"B-Premise": 3,
+"I-Premise": 4,
+}
+```
+A `claim`  is a concluding statement made by the author about the outcome of the study. In the medical domain it may be an assertion of a diagnosis or a treatment. 
+A `premise` corresponds to an observation or measurement in the study (ground truth), which supports or attacks another argument component, usually a claim. 
+It is important that they are observed facts, therefore, credible without further evidence.
+
+## Citation
+
+````bibtex
+@misc{yeginbergen2024crosslingual,
+      title={Cross-lingual Argument Mining in the Medical Domain}, 
+      author={Anar Yeginbergen and Rodrigo Agerri},
+      year={2024},
+      eprint={2301.10527},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```