Update README.md

README.md

@@ -101,7 +101,7 @@ RoBERTaLexPT-base is a Portuguese Masked Language Model pretrained from scratch
 
 ## Evaluation
 
-The model was evaluated on ["PortuLex" benchmark](eduagarcia/PortuLex_benchmark), a four-task benchmark designed to evaluate the quality and performance of language models in the Portuguese legal domain.
+The model was evaluated on ["PortuLex" benchmark](https://huggingface.co/eduagarcia/PortuLex_benchmark), a four-task benchmark designed to evaluate the quality and performance of language models in the Portuguese legal domain.
 
 Macro F1-Score (\%) for multiple models evaluated on PortuLex benchmark test splits:
 
@@ -120,9 +120,9 @@ Macro F1-Score (\%) for multiple models evaluated on PortuLex benchmark test spl
 | [Legal-RoBERTa-PT-large](https://huggingface.co/joelniklaus/legal-portuguese-roberta-large)                 | 87.96     | 88.32/84.83     | 79.57       |   81.98   | 84.02           |
 | **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           |
+| [RoBERTaLegalPT-base](https://huggingface.co/eduagarcia/RoBERTaCrawlPT-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** (Trained on CrawlPT + LegalPT)                       | **90.73** | **88.56**/86.03 | **80.40**   |   83.22   | **85.41**       |
+| **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 larger models. The results highlight the importance of domain-diverse training data over sheer model scale.