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	---
	license: apache-2.0
	tags:
	- generated_from_trainer
	metrics:
	- accuracy
	model-index:
	- name: bert-base-cased-PLANE-ood-2
	results: []
	language:
	- en
	pipeline_tag: text-classification
	widget:
	- text: A fake smile is a smile
	- text: An alleged thief is an alleged criminal
	- text: A small cat is an animal
	- text: A small cat is a small mammal
	datasets:
	- lorenzoscottb/PLANE-ood
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# BERT for PLANE classification

	This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on one of the PLANE's dataset split (no.2), introduced in [Bertolini et al., COLING 2022](https://aclanthology.org/2022.coling-1.359/)
	It achieves the following results on the evaluation set:

	- Accuracy: 0.9043

	## Model description

	The model is trained to perform a sequence classification task over phrase-level adjective-noun inferences (e.g., "A red car is a vehicle").

	## Intended uses & limitations

	The scope of the model is not to run lexical entailment (i.e., hypernym detection). The model is trained solely to perform a very specific subset of phrase-level entailment, based on adjective-nouns phrases. The type of question you should ask the model are limited, and should have one of three forms:
	- 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)

	Linguistically speaking, adjectives belong to three macro classes (intersective, subsective, and intensional). From a linguistic and logical stand, these class shape the truth value of the three forms above. For instance, since red is an intersective adjective, the three from are all true. A subjective adjective like small allows just the first two, but not the last – that is, logically speaking, a small car is not a small vehicle.

	In other words, the model was built to study out-of-distribution compositional generalisation with respect to a very specific set of compositional phenomena.

	This poses clear limitations to the question you can ask the model. For instance, if you had to query the model with a basic (false) hypernym detection task (e.g., A dog is a cat), the model will consider it as true.

	## Training and evaluation data

	The data used for training and testing, as well as the other splits used for the experiments, are available on the paper's git page [here](https://github.com/lorenzoscottb/PLANE). The reported accuracy reference to out-of-distribution evaluation. that is, the model was tested to perform text classification as presented but on unknown adjectives and nouns.

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 16
	- eval_batch_size: 16
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 1

	### Framework versions

	- Transformers 4.25.1
	- Pytorch 1.12.1
	- Datasets 2.5.1
	- Tokenizers 0.12.1

	# Cite

	if you want to use the model or data in your work please reference the paper too

	```bibtex
	@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",
	}
	```