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---
license: cc-by-2.0
task_categories:
- text-classification
language:
- en
size_categories:
- 100K<n<1M
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: 26047744
num_examples: 300132
- name: test
num_bytes: 874524
num_examples: 10080
download_size: 4721262
dataset_size: 26922268
---
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",
}
``` |