Legal Longformer Base

🚀 Legal Longformer (base)

This is a derivative model based on the LexLM RoBERTa model, designed for legal long - document processing.

🚀 Quick Start

You can use the following examples to quickly test the model:

# Example 1
text1 = "The applicant submitted that her husband was subjected to treatment amounting to <mask> whilst in the custody of police."
# Example 2
text2 = "This <mask> Agreement is between General Motors and John Murray."
# Example 3
text3 = "Establishing a system for the identification and registration of <mask> animals and regarding the labelling of beef and beef products."
# Example 4
text4 = "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants’ appeals."

✨ Features

• Derivative Model: Based on the [LexLM (base)](https://huggingface.co/lexlms/legal - roberta - base) RoBERTa model.
• Extended Positional Embeddings: The positional embeddings were extended by cloning the original embeddings multiple times following Beltagy et al. (2020).

📚 Documentation

Model description

LexLM (Base/Large) are our newly released RoBERTa models. We follow 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.

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.

Additional Information