Update README.md

README.md

@@ -57,7 +57,7 @@ metrics:
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 # RoBERTaLexPT-base
 
-RoBERTaLexPT-base is pretrained from LegalPT and CrawlPT corpora, using [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base), introduced by [Liu et al. (2019)](https://arxiv.org/abs/1907.11692).
+RoBERTaLexPT-base is a Portuguese Masked Language Model pretrained from scratch from the [LegalPT](https://huggingface.co/datasets/eduagarcia/LegalPT) and CrawlPT corpora, using the same architecture as [RoBERTa-base](https://huggingface.co/FacebookAI/roberta-base), introduced by [Liu et al. (2019)](https://arxiv.org/abs/1907.11692).
 
 - **Language(s) (NLP):** Brazilian Portuguese (pt-BR)
 - **License:** [Creative Commons Attribution 4.0 International Public License](https://creativecommons.org/licenses/by/4.0/deed.en)
@@ -83,9 +83,11 @@ Macro F1-Score (\%) for multiple models evaluated on PortuLex benchmark test spl
 | [Legal-XLM-R-base](https://arxiv.org/abs/2306.02069)                       | 87.48     | 83.49/83.16     | 79.79       |   82.35   | 83.24           |
 | [Legal-XLM-R-large](https://arxiv.org/abs/2306.02069)                      | 88.39     | 84.65/84.55     | 79.36       |   81.66   | 83.50           |
 | [Legal-RoBERTa-PT-large](https://arxiv.org/abs/2306.02069)                 | 87.96     | 88.32/84.83     | 79.57       |   81.98   | 84.02           |
-| RoBERTaTimbau-base                                                         | 89.68     | 87.53/85.74     | 78.82       |   82.03   | 84.29           |
-| RoBERTaLegalPT-base                                                        | 90.59     | 85.45/84.40     | 79.92       |   82.84   | 84.57           |
-| RoBERTaLexPT-base                                                          | **90.73** | **88.56**/86.03 | **80.40**   |   83.22   | **85.41**       |
+| **Ours**                                                                   |           |                 |             |           |                 |
+| RoBERTaTimbau-base (Reproduction of BERTimbau)                             | 89.68     | 87.53/85.74     | 78.82       |   82.03   | 84.29           |
+| RoBERTaLegalPT-base (Trained on LegalPT)                                   | 90.59     | 85.45/84.40     | 79.92       |   82.84   | 84.57           |
+| RoBERTaCrawlPT-base  (Trained on CrawlPT)                                  | 89.24     | 88.22/86.58     | 79.88       |   82.80   | 84.83           |
+| RoBERTaLexPT-base (this) (Trained on CrawlPT + LegalPT)                    | **90.73** | **88.56**/86.03 | **80.40**   |   83.22   | **85.41**       |
 
 In summary, RoBERTaLexPT consistently achieves top legal NLP effectiveness despite its base size. 
 With sufficient pre-training data, it can surpass overparameterized models. The results highlight the importance of domain-diverse training data over sheer model scale.