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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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model-index: |
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- name: bert-base-cased-PLANE-ood-2 |
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results: [] |
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language: |
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- en |
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pipeline_tag: text-classification |
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widget: |
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- text: "A fake smile is a smile" |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# BERT for PLANE classification |
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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/) |
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It achieves the following results on the evaluation set: |
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- Accuracy: 0.9043 |
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## Model description |
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The model is trained to perform a sequence classification task over phrase-level adjective-noun inferences (e.g., "A red car is a vehicle"). |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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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. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 16 |
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- eval_batch_size: 16 |
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- seed: 42 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 1 |
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### Framework versions |
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- Transformers 4.25.1 |
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- Pytorch 1.12.1 |
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- Datasets 2.5.1 |
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- Tokenizers 0.12.1 |
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# Cite |
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if you want to use the model or data in your work please reference the paper too |
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``` |
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@inproceedings{bertolini-etal-2022-testing, |
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title = "Testing Large Language Models on Compositionality and Inference with Phrase-Level Adjective-Noun Entailment", |
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author = "Bertolini, Lorenzo and |
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Weeds, Julie and |
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Weir, David", |
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booktitle = "Proceedings of the 29th International Conference on Computational Linguistics", |
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month = oct, |
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year = "2022", |
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address = "Gyeongju, Republic of Korea", |
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publisher = "International Committee on Computational Linguistics", |
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url = "https://aclanthology.org/2022.coling-1.359", |
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pages = "4084--4100", |
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} |
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``` |