---
language: en
pipeline_tag: fill-mask
license: cc-by-sa-4.0
tags:
- legal
- long-documents
model-index:
- name: lexlms/legal-longformer-large
  results: []
widget:
- text: "The applicant submitted that her husband was subjected to treatment amounting to <mask> whilst in the custody of police."
- text: "This <mask> Agreement is between General Motors and John Murray."
- text: "Establishing a system for the identification and registration of <mask> animals and regarding the labelling of beef and beef products."
- 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."
datasets:
- lexlms/lex_files
---

# Legal Longformer (large)

This is a derivative model based on the [LexLM (large)](https://huggingface.co/lexlms/legal-roberta-large) RoBERTa model. 
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).

## 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.*](https://arxiv.org/abs/2305.07507)
```
@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",
}
```