added description

README.md

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 license: cc-by-nc-sa-4.0
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+
+The [Climate-Fever dataset](https://huggingface.co/datasets/tdiggelm/climate_fever) was first collected and published by [Diggelmann et al, 2020](https://arxiv.org/abs/2012.00614).  
+For our study, we are interested in token-level rationales which are not available from the initial publication of Climate-Fever. Therefore, we manually selected a subset of 102 claims (510 claim-evidence pairs) based on clarity of the claim formulation and balanced claim labels. Each sample was annotated on token-level by 3 annotators as either supporting the claim (label=1), contradicting the claim (label=-1) or neutral (label=0). Annotations were then averaged (`rationale_numeric`) and majority-voted (`rationale_binary`).
+
+```python
+#column "label"
+claim_label_dict = {
+    '🙌 SUPPORT': 1, 
+    '👎 REFUTE/CONTRADICT': -1, 
+    '🤷 NOT ENOUGH INFO': 0, 
+    '🤨 DISPUTE (SUPPORT and REFUTE)': 2}
+
+#column "evidence_labels"
+evidence_labels_dict = {
+    0: "supports", 
+    1: "refutes",
+    2: "not enough info"}
+```
+
+![Example annotation](polarbear1.png)
+![Example annotation](polarbear2.png)
+
+
+More details can be found in  
+Stephanie Brandl\* & Oliver Eberle\* (2026) A Systematic Comparison between Extractive Self-Explanations and Human Rationales in Text Classification. Proceedings of the 6th Workshop on Trustworthy Natural Language Processing. (to appear)
+