Update README.md

README.md

@@ -1,28 +1,69 @@
 ---
-dataset_info:
-  features:
-  - name: seq
-    dtype: string
-  - name: label
-    dtype: string
-  - name: Adj_Class
-    dtype: string
-  - name: Adj
-    dtype: string
-  - name: Nn
-    dtype: string
-  - name: Hy
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 28298734
-    num_examples: 300132
-  - name: test
-    num_bytes: 950124
-    num_examples: 10080
-  download_size: 4739984
-  dataset_size: 29248858
+license: cc-by-2.0
+task_categories:
+- text-classification
+task_ids:
+- natural-language-inference
+language:
+- en
+size_categories:
+- 100K<n<1M
 ---
-# Dataset Card for "PLANE-ood"
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+Preprocessed from https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/
+
+```python
+df=pd.read_json('https://huggingface.co/datasets/lorenzoscottb/PLANE-ood/resolve/main/PLANE_trntst-OoV_inftype-all.json')
+f = lambda df: pd.DataFrame(list(zip(*[df[c] for c in df.index])),columns=df.index)
+ds=DatasetDict()
+for split in ['train','test']:
+    dfs=pd.concat([f(df[c]) for c in df.columns if split in c.lower()]).reset_index(drop=True)
+    dfs['label']=dfs['label'].map(lambda x:{1:'entailment',0:'not-entailment'}[x])
+    ds[split]=Dataset.from_pandas(dfs,preserve_index=False)
+ds.push_to_hub('tasksource/PLANE-ood')
+```
+
+
+# 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/). 
+
+
+### Features
+
+Each entrance has 6 features: `seq, label, Adj_Class, Adj, Nn, Hy`
+- `seq`:test sequense
+- `label`: ground truth (1:entialment, 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
+
+Each sample in `seq` can take one of three forms (or inference types, in paper):
+
+- An *Adjective-Noun* is a *Noun* (e.g. A red car is a car)
+- An *Adjective-Noun* is a *Hypernym(Noun)* (e.g. A red car is a vehicle)
+- An *Adjective-Noun* is a *Adjective-Hypernym(Noun)* (e.g. A red car is a red vehicle)
+
+Please note that, as specified in the paper, the ground truth is automatically assigned based on the linguistic rule that governs the interaction between each adjective class and inference type – see the paper for more detail. 
+
+### 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",
+}
+
+```