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--- |
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language: en |
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pipeline_tag: fill-mask |
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license: cc-by-sa-4.0 |
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tags: |
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- legal |
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- long-documents |
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model-index: |
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- name: lexlms/legal-longformer-large |
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results: [] |
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widget: |
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- text: "The applicant submitted that her husband was subjected to treatment amounting to <mask> whilst in the custody of police." |
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- text: "This <mask> Agreement is between General Motors and John Murray." |
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- text: "Establishing a system for the identification and registration of <mask> animals and regarding the labelling of beef and beef products." |
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- text: "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants’ appeals." |
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datasets: |
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- lexlms/lex_files |
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--- |
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# Legal Longformer (large) |
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This is a derivative model based on the [LexLM (large)](https://huggingface.co/lexlms/legal-roberta-large) RoBERTa model. |
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All model parameters where cloned from the original model, while the positional embeddings were extended by cloning the original embeddings multiple times following [Beltagy et al. (2020)](https://arxiv.org/abs/2004.05150) using a python script similar to this one (https://github.com/allenai/longformer/blob/master/scripts/convert_model_to_long.ipynb). |
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## Model description |
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LexLM (Base/Large) are our newly released RoBERTa models. We follow a series of best-practices in language model development: |
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* We warm-start (initialize) our models from the original RoBERTa checkpoints (base or large) of Liu et al. (2019). |
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* We train a new tokenizer of 50k BPEs, but we reuse the original embeddings for all lexically overlapping tokens (Pfeiffer et al., 2021). |
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* 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. |
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* 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). |
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* We consider mixed cased models, similar to all recently developed large PLMs. |
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### Citation |
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[*Ilias Chalkidis\*, Nicolas Garneau\*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard.* |
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*LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development.* |
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*2022. In the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.*](https://arxiv.org/abs/2305.07507) |
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``` |
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@inproceedings{chalkidis-garneau-etal-2023-lexlms, |
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title = {{LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development}}, |
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author = "Chalkidis*, Ilias and |
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Garneau*, Nicolas and |
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Goanta, Catalina and |
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Katz, Daniel Martin and |
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Søgaard, Anders", |
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booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics", |
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month = july, |
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year = "2023", |
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address = "Toronto, Canada", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/2305.07507", |
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} |
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``` |
