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README.md

@@ -11,7 +11,7 @@ library_name: transformers
 pipeline_tag: text-generation
 tags:
 - goldfish
-
+- arxiv:2408.10441
 ---
 
 # quz_latn_full
@@ -22,7 +22,7 @@ The Goldfish models are trained primarily for comparability across languages and
 
 Note: quz_latn is an [individual language](https://iso639-3.sil.org/code_tables/639/data) code. Macrolanguage code que_latn (Quechua) is included in Goldfish. Consider using that model depending on your use case.
 
-All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://github.com/tylerachang/goldfish/blob/main/goldfish_paper_20240815.pdf).
+All training and hyperparameter details are in our paper, [Goldfish: Monolingual Language Models for 350 Languages (Chang et al., 2024)](https://www.arxiv.org/abs/2408.10441).
 
 Training code and sample usage: https://github.com/tylerachang/goldfish
 
@@ -32,6 +32,7 @@ Sample usage also in this Google Colab: [link](https://colab.research.google.com
 
 To access all Goldfish model details programmatically, see https://github.com/tylerachang/goldfish/blob/main/model_details.json.
 All models are trained with a [CLS] (same as [BOS]) token prepended, and a [SEP] (same as [EOS]) token separating sequences.
+For best results, make sure that [CLS] is prepended to your input sequence (see sample usage linked above)!
 Details for this model specifically:
 
 * Architecture: gpt2
@@ -58,5 +59,6 @@ If you use this model, please cite:
   author={Chang, Tyler A. and Arnett, Catherine and Tu, Zhuowen and Bergen, Benjamin K.},
   journal={Preprint},
   year={2024},
+  url={https://www.arxiv.org/abs/2408.10441},
 }
 ```