Update README.md

README.md

@@ -1,3 +1,52 @@
 ---
 license: cc-by-2.0
+task_categories:
+- text-classification
+language:
+- en
+size_categories:
+- 100K<n<1M
 ---
+
+# PLANE Out-of-Distribution Sets
+
+PLANE (phrase-level adjective-noun entailment) is a benchmark to test models on fine-grained compositional inference. 
+The current dataset contains five sampled splits, used in the supervised experiments of [Bertolini et al., 22](https://aclanthology.org/2022.coling-1.359/). 
+
+## Data Structure
+
+The data are organised around the five `Train/test_split#`, each containing a training and test set of circa 60K and 2K.
+
+### Features
+
+Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
+- `seq`:test sequense
+- `label`: ground truth (1:entail, 0:no-entailment)
+- `Adj_Class`: the class of the sequence adjectives
+- `Adj`: the adjective of the sequence (I:intersective, S:subsective, O:intensional)
+- `N`n: the noun
+- `Hy`: the noun's hypericum
+
+### Trained Model
+
+You can find a pre-trend BERT model (on the 2nd out-of-distribution split) [here](https://huggingface.co/lorenzoscottb/bert-base-cased-PLANE-ood-2?text=A+fake+smile+is+a+smile).
+
+### Cite
+
+If you use PLANE for your work, please cite the main COLING 2022 paper.
+```
+@inproceedings{bertolini-etal-2022-testing,
+    title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment",
+    author = "Bertolini, Lorenzo  and
+      Weeds, Julie  and
+      Weir, David",
+    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
+    month = oct,
+    year = "2022",
+    address = "Gyeongju, Republic of Korea",
+    publisher = "International Committee on Computational Linguistics",
+    url = "https://aclanthology.org/2022.coling-1.359",
+    pages = "4084--4100",
+}
+
+```