---
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
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

```
@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",
}
```