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--- |
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language: |
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- pt |
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--- |
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Sabiá-7B is Portuguese language model developed by [Maritaca AI](https://www.maritaca.ai/). |
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**Input:** The model accepts only text input. |
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**Output:** The Model generates text only. |
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**Model Architecture:** Sabiá-7B is an auto-regressive language model that uses the same architecture of LLaMA-1-7B. |
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**Tokenizer:** It uses the same tokenizer as LLaMA-1-7B. |
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**Maximum sequence length:** 2048 tokens. |
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**Pretraining data:** The model was pretrained on 7 billion tokens from the Portuguese subset of ClueWeb22, starting with the weights of LLaMA-1-7B and further trained for an additional 10 billion tokens, approximately 1.4 epochs of the training dataset. |
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**Data Freshness:** The pretraining data has a cutoff of mid-2022. |
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**License:** The licensing is the same as LLaMA-1's, restricting the model's use to research purposes only. |
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**Paper:** For more details, please refer to our paper: [Sabiá: Portuguese Large Language Models](https://arxiv.org/pdf/2304.07880.pdf) |
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Given that Sabiá-7B was trained solely on a language modeling objective without fine-tuning for instruction following, it is recommended for few-shot tasks rather than zero-shot tasks. |
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**Results in Portuguese** |
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Below we show the results on the Poeta benchmark, which consists of 14 Portuguese datasets. |
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For more information on the Normalized Preferred Metric (NPM), please refer to our paper. |
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|Model | NPM | |
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|--|--| |
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|LLaMA-1-7B| 33.0| |
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|LLaMA-2-7B| 43.7| |
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|Sabiá-7B| 48.5| |
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**Results in English** |
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Below we show the average results on 6 English datasets: PIQA, HellaSwag, WinoGrande, ARC-e, ARC-c, and OpenBookQA. |
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|Model | NPM | |
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|--|--| |
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|LLaMA-1-7B| 50.1| |
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|Sabiá-7B| 49.0| |
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Please use the following bibtex to cite our paper: |
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``` |
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@InProceedings{10.1007/978-3-031-45392-2_15, |
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author="Pires, Ramon |
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and Abonizio, Hugo |
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and Almeida, Thales Sales |
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and Nogueira, Rodrigo", |
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editor="Naldi, Murilo C. |
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and Bianchi, Reinaldo A. C.", |
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title="Sabi{\'a}: Portuguese Large Language Models", |
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booktitle="Intelligent Systems", |
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year="2023", |
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publisher="Springer Nature Switzerland", |
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address="Cham", |
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pages="226--240", |
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isbn="978-3-031-45392-2" |
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} |
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``` |
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