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+ ---
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+ license: mit
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+ datasets:
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+ - Superar/Puntuguese
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+ language:
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+ - pt
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+ pipeline_tag: token-classification
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+ tags:
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+ - humor
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+ - puns
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+ - pun-location
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+ ---
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+
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+ # "Não é medo, é recheio": Sequence Labeling for Pun Location and Detection in Portuguese
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+
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+ This repository contains the models fine-tuned for the task of Pun Location with Portuguese Language, trained with the [Puntuguese](https://huggingface.co/datasets/Superar/Puntuguese) dataset. There are several models available:
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+
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+ - `GlorIA-1.3B-all`
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+ - `GlorIA-1.3B-positive`
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+ - `albertina-900m-ptbr-all`
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+ - `albertina-900m-ptbr-positive`
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+ - `albertina-900m-ptpt-all`
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+ - `albertina-900m-ptpt-positive`
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+
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+ The `*-all` models were fine-tuned with all the data from the training portion of Puntuguese, including negative examples. Meanwhile, the `*-positive` models were trained only on texts that contain at least one pun sign.
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+
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+ We make available all of the models' checkpoints. Therefore, we encourage to walk through the files and find the one most suitable.
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+
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+ ## How to use
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+
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+ To load a model, use the `AutoModelForSequenceClassification.from_pretrained()` method with the `subfolder` argument.
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+
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+ For example, if we want to load the checkpoint 500 of `albertina-900m-ptbr-positive`, we need the following code:
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+
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+ ```python
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+ from transformers import AutoModelForSequenceClassification
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+
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+ model = AutoModelForSequenceClassification.from_pretrained('Superar/Portuguese-Pun-Location',
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+ subfolder='albertina-900m-ptbr-positive/checkpoint-500')
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+ ```
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+
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+ This should load the correct model.
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+
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+ ## How to cite
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+
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+ ```bibtex
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+ @inproceedings{gameiro_etal:epia2024,
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+ title = {Sequence Labeling for Pun Location and Detection in {{Portuguese}}},
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+ booktitle = {Proceedings of 23rd {{EPIA}} Conference on Artificial Intelligence, {{EPIA}} 2024},
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+ author = {Gameiro, Patr{\'{\i}}cia and In{\'a}cio, Marcio and Gon{\c c}alo Oliveira, Hugo and Alves, Ana},
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+ year = {2024},
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+ pages = {In press},
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+ address = {Viana do Castelo, Portugal}
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+ }
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+ ```