--- dataset_info: - config_name: LeNER-Br features: - name: idx dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-ORGANIZACAO '2': I-ORGANIZACAO '3': B-PESSOA '4': I-PESSOA '5': B-TEMPO '6': I-TEMPO '7': B-LOCAL '8': I-LOCAL '9': B-LEGISLACAO '10': I-LEGISLACAO '11': B-JURISPRUDENCIA '12': I-JURISPRUDENCIA splits: - name: train num_bytes: 3953896 num_examples: 7825 - name: validation num_bytes: 715819 num_examples: 1177 - name: test num_bytes: 819242 num_examples: 1390 download_size: 1049906 dataset_size: 5488957 - config_name: UlyssesNER-Br-PL-coarse features: - name: idx dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DATA '2': I-DATA '3': B-EVENTO '4': I-EVENTO '5': B-FUNDAMENTO '6': I-FUNDAMENTO '7': B-LOCAL '8': I-LOCAL '9': B-ORGANIZACAO '10': I-ORGANIZACAO '11': B-PESSOA '12': I-PESSOA '13': B-PRODUTODELEI '14': I-PRODUTODELEI splits: - name: train num_bytes: 1511905 num_examples: 2271 - name: validation num_bytes: 305472 num_examples: 489 - name: test num_bytes: 363207 num_examples: 524 download_size: 431964 dataset_size: 2180584 - config_name: UlyssesNER-Br-PL-fine features: - name: idx dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-DATA '2': I-DATA '3': B-EVENTO '4': I-EVENTO '5': B-FUNDapelido '6': I-FUNDapelido '7': B-FUNDlei '8': I-FUNDlei '9': B-FUNDprojetodelei '10': I-FUNDprojetodelei '11': B-LOCALconcreto '12': I-LOCALconcreto '13': B-LOCALvirtual '14': I-LOCALvirtual '15': B-ORGgovernamental '16': I-ORGgovernamental '17': B-ORGnaogovernamental '18': I-ORGnaogovernamental '19': B-ORGpartido '20': I-ORGpartido '21': B-PESSOAcargo '22': I-PESSOAcargo '23': B-PESSOAgrupocargo '24': I-PESSOAgrupocargo '25': B-PESSOAindividual '26': I-PESSOAindividual '27': B-PRODUTOoutros '28': I-PRODUTOoutros '29': B-PRODUTOprograma '30': I-PRODUTOprograma '31': B-PRODUTOsistema '32': I-PRODUTOsistema splits: - name: train num_bytes: 1511905 num_examples: 2271 - name: validation num_bytes: 305472 num_examples: 489 - name: test num_bytes: 363207 num_examples: 524 download_size: 437232 dataset_size: 2180584 - config_name: fgv-coarse features: - name: idx dtype: int32 - name: tokens sequence: string - name: ner_tags sequence: class_label: names: '0': O '1': B-Academic_Citation '2': I-Academic_Citation '3': B-Legislative_Reference '4': I-Legislative_Reference '5': B-Person '6': I-Person '7': B-Precedent '8': I-Precedent splits: - name: train num_bytes: 19490545 num_examples: 415 - name: validation num_bytes: 3934464 num_examples: 60 - name: test num_bytes: 6080343 num_examples: 119 download_size: 3917469 dataset_size: 29505352 - config_name: rrip features: - name: idx dtype: int32 - name: sentence dtype: string - name: label dtype: class_label: names: '0': '1' '1': '2' '2': '3' '3': '4' '4': '5' '5': '6' '6': '7' '7': '8' splits: - name: train num_bytes: 1174840 num_examples: 8257 - name: validation num_bytes: 184668 num_examples: 1053 - name: test num_bytes: 235217 num_examples: 1474 download_size: 929466 dataset_size: 1594725 configs: - config_name: LeNER-Br data_files: - split: train path: LeNER-Br/train-* - split: validation path: LeNER-Br/validation-* - split: test path: LeNER-Br/test-* - config_name: UlyssesNER-Br-PL-coarse data_files: - split: train path: UlyssesNER-Br-PL-coarse/train-* - split: validation path: UlyssesNER-Br-PL-coarse/validation-* - split: test path: UlyssesNER-Br-PL-coarse/test-* - config_name: UlyssesNER-Br-PL-fine data_files: - split: train path: UlyssesNER-Br-PL-fine/train-* - split: validation path: UlyssesNER-Br-PL-fine/validation-* - split: test path: UlyssesNER-Br-PL-fine/test-* - config_name: fgv-coarse data_files: - split: train path: fgv-coarse/train-* - split: validation path: fgv-coarse/validation-* - split: test path: fgv-coarse/test-* - config_name: rrip data_files: - split: train path: rrip/train-* - split: validation path: rrip/validation-* - split: test path: rrip/test-* task_categories: - token-classification - text-classification language: - pt tags: - legal pretty_name: PortuLex benchmark size_categories: - 10K- The PortuLex benchmark includes datasets with specific access requirements: 1. RRI dataset requires the acceptance of these terms: https://bit.ly/rhetoricalrole. 2. For the FGV-STF corpus, you must request it directly from the original authors: https://www.sciencedirect.com/science/article/abs/pii/S0306457321002727. extra_gated_fields: Full Name: text Official Email Address: text Affiliation: text Country: text I accepted the RRIP Terms of Commitment: checkbox I have obtained permission to access the FGV-STF benchmark directly from the original authors: checkbox --- # PortuLex_benchmark "PortuLex" benchmark is a four-task benchmark designed to evaluate the quality and performance of language models in the Portuguese legal domain. | Dataset | Task | Train | Dev | Test | |---------------|------|-------|-------|-------| | RRI | CLS | 8.26k | 1.05k | 1.47k | | LeNER-Br | NER | 7.83k | 1.18k | 1,39k | | UlyssesNER-Br | NER | 3.28k | 489 | 524 | | FGV-STF | NER | 415 | 60 | 119 | ## Dataset Details PortuLex is composed by: [LeNER-Br](http://link.springer.com/10.1007/978-3-319-99722-3_32), [Rhetorical Role Identification (RRI)](https://dl.acm.org/doi/abs/10.1007/978-3-030-91699-2_38), [FGV-STF](https://www.sciencedirect.com/science/article/pii/S0306457321002727), [UlyssesNER-Br](https://dl.acm.org/doi/abs/10.1007/978-3-030-98305-5_1). - **LeNER-Br**: the first Named Entity Recognition (NER) corpus for the legal domain in Brazilian Portuguese from higher and state-level courts. - **RRI**: rhetorical annotations from judicial sentences from the Court of Justice of Mato Grosso do Sul (Brazil). - **FGV-STF**: decisions from the Supreme Federal Court for entity extraction. - **UlyssesNER-Br**: NER corpus of bills and legislative queries from the Chamber of Deputies of Brazil. ### Dataset Description - **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) - **Repository:** https://github.com/eduagarcia/roberta-legal-portuguese - **Paper:** [More Information Needed] ## Dataset Evaluation Macro F1-Score (\%) for multiple models evaluated on PortuLex benchmark test splits: | **Model** | **LeNER** | **UlyNER-PL** | **FGV-STF** | **RRIP** | **Average (%)** | |----------------------------------------------------------------------------|-----------|-----------------|-------------|:---------:|-----------------| | | | Coarse/Fine | Coarse | | | | BERTimbau-based | 88.34 | 86.39/83.83 | 79.34 | 82.34 | 83.78 | | BERTimbau-large | 88.64 | 87.77/84.74 | 79.71 | **83.79** | 84.60 | | Albertina-PT-BR-base | 89.26 | 86.35/84.63 | 79.30 | 81.16 | 83.80 | | Albertina-PT-BR-xlarge | 90.09 | 88.36/**86.62** | 79.94 | 82.79 | 85.08 | | BERTikal-base | 83.68 | 79.21/75.70 | 77.73 | 81.11 | 79.99 | | JurisBERT-base | 81.74 | 81.67/77.97 | 76.04 | 80.85 | 79.61 | | BERTimbauLAW-base | 84.90 | 87.11/84.42 | 79.78 | 82.35 | 83.20 | | Legal-XLM-R-base | 87.48 | 83.49/83.16 | 79.79 | 82.35 | 83.24 | | Legal-XLM-R-large | 88.39 | 84.65/84.55 | 79.36 | 81.66 | 83.50 | | Legal-RoBERTa-PT-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 | | [RoBERTaCrawlPT-base](https://huggingface.co/eduagarcia/RoBERTaCrawlPT-base) (Trained on CrawlPT) | 89.24 | 88.22/86.58 | 79.88 | 82.80 | 84.83 | | [RoBERTaLexPT-base](https://huggingface.co/eduagarcia/RoBERTaLexPT-base) (Trained on CrawlPT + LegalPT) | **90.73** | **88.56**/86.03 | **80.40** | 83.22 | **85.41** | ## Citation ```bibtex @InProceedings{garcia2024_roberlexpt, author="Garcia, Eduardo A. S. and Silva, N{\'a}dia F. F. and Siqueira, Felipe and Gomes, Juliana R. S. and Albuqueruqe, Hidelberg O. and Souza, Ellen and Lima, Eliomar and De Carvalho, André", title="RoBERTaLexPT: A Legal RoBERTa Model pretrained with deduplication for Portuguese", booktitle="Computational Processing of the Portuguese Language", year="2024", publisher="Association for Computational Linguistics" } ``` ## Acknowledgment This work has been supported by the AI Center of Excellence (Centro de Excelência em Inteligência Artificial – CEIA) of the Institute of Informatics at the Federal University of Goiás (INF-UFG).