Fill-Mask
Transformers
PyTorch
Portuguese
deberta-v2
albertina-pt*
albertina-100m-portuguese-ptpt
albertina-100m-portuguese-ptbr
albertina-900m-portuguese-ptpt
albertina-900m-portuguese-ptbr
albertina-1b5-portuguese-ptpt
albertina-1b5-portuguese-ptbr
bert
deberta
portuguese
encoder
foundation model
Inference Endpoints
jarodrigues
commited on
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README.md
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@@ -146,21 +146,6 @@ provides translation from English to PTPT or PTBR as possible options.
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**para modelo PTBR**
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| Model | RTE (Accuracy) | WNLI (Accuracy)| MRPC (F1) | STS-B (Pearson) | COPA (Accuracy) | CB (F1) | MultiRC (F1) | BoolQ (Accuracy) |
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| [**Albertina 1.5B PTBR**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder) | **0.8676** | 0.4742 | 0.8622 | **0.9007** | 0.7767 | 0.6372 | **0.7667** | **0.8654** |
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| [**Albertina 1.5B PTBR 256**](https://huggingface.co/PORTULAN/albertina-1b5-portuguese-ptbr-encoder-256) | 0.8123 | 0.4225 | 0.8638 | 0.8968 | **0.8533** | **0.6884** | 0.6799 | 0.8509 |
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| [**Albertina 900M PTBR**](https://huggingface.co/PORTULAN/albertina-900m-portuguese-ptbr-encoder) | 0.7545 | 0.4601 | **0.9071**| 0.8910 | 0.7767 | 0.5799 | 0.6731 | 0.8385 |
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| **BERTimbau (335M)** | 0.6446 | **0.5634** | 0.8873 | 0.8842 | 0.6933 | 0.5438 | 0.6787 | 0.7783 |
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| [**Albertina 100M PTBR**](https://huggingface.co/PORTULAN/albertina-100m-portuguese-ptbr-encoder) | 0.6582 | **0.5634** | 0.8149 | 0.8489 | n.a. | 0.4771 | 0.6469 | 0.7537 |
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| **DeBERTa 1.5B (English)** | 0.7112 | **0.5634** | 0.8545 | 0.0123 | 0.5700 | 0.4307 | 0.3639 | 0.6217 |
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| **DeBERTa 100M (English)** | 0.5716 | 0.5587 | 0.8060 | 0.8266 | n.a. | 0.4739 | 0.6391 | 0.6838 |
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# How to use
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# How to use
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