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README.md

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----
-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",
-}
-
-```

PLANE_trntst-OoV_inftype-all.json → lorenzoscottb--PLANE-ood/json-train.parquet

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