Include Lucy in acknowledgments, extend

README.md

@@ -1,15 +1,16 @@
 # BERT-Wiki-Paragraphs
 
-Authors: Satya Almasian*, Dennis Aumiller*, Michael Gertz  
+Authors: Satya Almasian\*, Dennis Aumiller\*, Lucienne-Sophie Marmé, Michael Gertz  
+Contact us at `<lastname>@informatik.uni-heidelberg.de`  
 Details for the training method can be found in our work [Structural Text Segmentation of Legal Documents](https://arxiv.org/abs/2012.03619).
 The training procedure follows the same setup, but we substitute legal documents for Wikipedia in this model.
 
 Training is performed in a form of weakly-supervised fashion to determine whether paragraphs topically belong together or not.
-We utilize automatically generated samples from Wikipedia for training,
-where paragrahs from within the same section are assumed to be topically coherent.  
-We use the same articles as ([Koshorek et al., 2018](https://arxiv.org/abs/1803.09337)),
+We utilize automatically generated samples from Wikipedia for training, where paragraphs from within the same section are assumed to be topically coherent.  
+We use the same articles as ([Koshorek et al., 2018](https://arxiv.org/abs/1803.09337)), 
 albeit from a 2021 dump of Wikpeida, and split at paragraph boundaries instead of the sentence level.
 
 ## Training Setup
-The model was trained for 3 epochs from the "bert-base-uncased" checkpoint on paragraph pairs (truncated to 512 max length).
+The model was trained for 3 epochs from `bert-base-uncased` on paragraph pairs (limited to 512 subwork with the `longest_first` truncation strategy).
+We use a batch size of 24 wit 2 iterations gradient accumulation (effective batch size of 48), and a learning rate of 1e-4, with gradient clipping at 5.
 Training was performed on a single Titan RTX GPU over the duration of 3 weeks.