Legal Roberta Large

🚀 LexLM large

LexLM large is a model continued pre - trained from RoBERTa large on the LeXFiles corpus, designed for legal language processing.

🚀 Quick Start

The model can be used for fill - mask tasks. For example, you can input masked legal texts and let the model predict the masked words. Here are some sample texts:

• "The applicant submitted that her husband was subjected to treatment amounting to whilst in the custody of police."
• "This Agreement is between General Motors and John Murray."
• "Establishing a system for the identification and registration of animals and regarding the labelling of beef and beef products."
• "Because the Court granted before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants’ appeals."

✨ Features

• Pre - training on Legal Corpus: This model was continued pre - trained from RoBERTa large (https://huggingface.co/roberta - large) on the LeXFiles corpus (https://huggingface.co/datasets/lexlms/lex_files), which makes it more suitable for legal language tasks.
• Following Best - practices: LexLM (Base/Large) follow a series of best - practices in language model development:
  • Warm - start from the original RoBERTa checkpoints (base or large) of Liu et al. (2019).
  • Train a new tokenizer of 50k BPEs, and reuse the original embeddings for all lexically overlapping tokens (Pfeiffer et al., 2021).
  • Continue pre - training on the diverse LeXFiles corpus for additional 1M steps with batches of 512 samples, and a 20/30% masking rate (Wettig et al., 2022), for base/large models, respectively.
  • Use a sentence sampler with exponential smoothing of the sub - corpora sampling rate following Conneau et al. (2019) to preserve per - corpus capacity.
  • Consider mixed cased models, similar to all recently developed large PLMs.

📦 Installation

No specific installation steps are provided in the original README.

📚 Documentation

Model description

LexLM (Base/Large) are newly released RoBERTa models. The development process adheres to a series of best - practices in language model development:

• We warm - start (initialize) our models from the original RoBERTa checkpoints (base or large) of Liu et al. (2019).
• We train a new tokenizer of 50k BPEs, but we reuse the original embeddings for all lexically overlapping tokens (Pfeiffer et al., 2021).
• We continue pre - training our models on the diverse LeXFiles corpus for additional 1M steps with batches of 512 samples, and a 20/30% masking rate (Wettig et al., 2022), for base/large models, respectively.
• We use a sentence sampler with exponential smoothing of the sub - corpora sampling rate following Conneau et al. (2019) since there is a disparate proportion of tokens across sub - corpora and we aim to preserve per - corpus capacity (avoid overfitting).
• We consider mixed cased models, similar to all recently developed large PLMs.

Intended uses & limitations

More information needed

Training and evaluation data

The model was trained on the LeXFiles corpus (https://huggingface.co/datasets/lexlms/lexfiles). For evaluation results, please consider our work "LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development" (Chalkidis* et al, 2023).

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 0.0001
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• distributed_type: tpu
• num_devices: 8
• gradient_accumulation_steps: 4
• total_train_batch_size: 256
• total_eval_batch_size: 64
• optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.05
• training_steps: 1000000

Training results

Framework versions

• Transformers 4.20.0
• Pytorch 1.12.0+cu102
• Datasets 2.7.0
• Tokenizers 0.12.0

Citation

Ilias Chalkidis*, Nicolas Garneau*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard. LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development. 2022. In the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.

@inproceedings{chalkidis-garneau-etal-2023-lexlms,
    title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}},
    author = "Chalkidis*, Ilias and 
              Garneau*, Nicolas and
              Goanta, Catalina and 
              Katz, Daniel Martin and 
              Søgaard, Anders",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics",
    month = july,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/2305.07507",
}

📄 License

This model is licensed under cc - by - sa - 4.0.

📋 Information Table