Update README.md

README.md

@@ -1,12 +1,52 @@
 ---
-license: cc-by-nc-sa-4.0
-pipeline_tag: fill-mask
 language: en
+pipeline_tag: fill-mask
+license: cc-by-sa-4.0
 tags:
 - legal
 - long-documents
+model-index:
+- name: lexlms/legal-longformer-base
+  results: []
+widget:
+- text: "The applicant submitted that her husband was subjected to treatment amounting to [MASK] whilst in the custody of police."
+datasets:
+- lexlms/lex_files
 ---
 
 # Legal Longformer (base)
 
-This is a derivative model based on the [LexLM (base)](https://huggingface.co/lexlms/roberta-base-cased) 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).
+This is a derivative model based on the [LexLM (base)](https://huggingface.co/lexlms/legal-roberta-base) 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 61th 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 61h Annual Meeting of the Association for Computational Linguistics",
+    month = june,
+    year = "2023",
+    address = "Toronto, Canada",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2305.07507",
+}
+```