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README.md

@@ -13,7 +13,7 @@ library_name: transformers
 pipeline_tag: text-generation
 tags:
 - goldfish
-
+- arxiv:2408.10441
 ---
 
 # aze_latn_1000mb
@@ -22,11 +22,11 @@ Goldfish is a suite of monolingual language models trained for 350 languages.
 This model is the <b>Azerbaijani</b> (Latin script) model trained on 1000MB of data, after accounting for an estimated byte premium of 1.30; content-matched text in Azerbaijani takes on average 1.30x as many UTF-8 bytes to encode as English.
 The Goldfish models are trained primarily for comparability across languages and for low-resource languages; Goldfish performance for high-resource languages is not designed to be comparable with modern large language models (LLMs).
 
-Note: This language is available in Goldfish with other scripts (writing systems). See: aze_arab, aze_cyrl.
+Note: This language is available in Goldfish with other scripts (writing systems). See: aze_cyrl, aze_arab.
 
 Note: aze_latn is a [macrolanguage](https://iso639-3.sil.org/code_tables/639/data) code. Individual language code azj_latn (North Azerbaijani) is included in Goldfish, although with less data.
 
-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
 
@@ -36,6 +36,7 @@ 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
@@ -62,5 +63,6 @@ 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},
 }
 ```