readme: ---
  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
  
  ```
  @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",
  }
  ```