Update README.md

README.md

@@ -52,7 +52,7 @@ git clone https://huggingface.co/datasets/allenai/nllb
 
 ### Supported Tasks and Leaderboards
 
-NA
+N/A
 
 ### Languages
 
@@ -81,6 +81,8 @@ Every instance for a language pair contains the following fields: 'translation'
 * Second sentence source
 * Second sentence URL if the source is crawl-data/\*; _ otherwise
 
+The lines are sorted by LASER3 score in decreasing order.
+
 Example:
 ```
 {'translation': {'ace_Latn': 'Gobnyan hana geupeukeucewa gata atawa geutinggai meunan mantong gata."',
@@ -96,14 +98,14 @@ Example:
 
 ### Data Splits
 
-The data is not split.  Given the noisy nature of the overall process, we recommend using the data only for training and use other datasets like [Flores-200](https://github.com/facebookresearch/flores) for the evaluation.
+The data is not split.  Given the noisy nature of the overall process, we recommend using the data only for training and use other datasets like [Flores-200](https://github.com/facebookresearch/flores) for the evaluation. The data includes some development and test sets from other datasets, such as xlsum.  In addition, sourcing data from multiple web crawls is likely to produce incidental overlap with other test sets.
 
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-Data was filtered based on language identification, emoji based filtering, and for some high-resource languages language model-based filtering. For more details on data filtering please refer to Section 5.2 (NLLB Team et al., 2022).
+Data was filtered based on language identification, emoji based filtering, and for some high-resource languages using a language model. For more details on data filtering please refer to Section 5.2 (NLLB Team et al., 2022).
 
 
 ### Source Data
@@ -152,7 +154,7 @@ The data was not human annotated.
 
 ### Personal and Sensitive Information
 
-Data may contain personally identifiable information, sensitive or toxic content that was publicly shared on the Internet. 
+Data may contain personally identifiable information, sensitive content, or toxic content that was publicly shared on the Internet.  
 
 ## Considerations for Using the Data
 
@@ -162,11 +164,11 @@ This dataset provides data for training machine learning systems for many langua
 
 ### Discussion of Biases
 
-Biases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques: lower resource languages may have lower accuracy while data filtering techniques may remove certain less natural utterances.
+Biases in the data have not been specifically studied, however as the original source of data is World Wide Web it is likely that the data has biases similar to those prevalent in the Internet. The data may also exhibit biases introduced by language identification and data filtering techniques; lower resource languages generally have lower accuracy.  
 
 ### Other Known Limitations
 
-Some of the translations are in fact machine translation.  Indeed, some sites have evidence of WordPress translation plugins in their HTML source.  These sites were not filtered out en mass because HTML source was not available in many cases. 
+Some of the translations are in fact machine translations.  While some website machine translation tools are identifiable from HTML source, these tools were not filtered out en mass because raw HTML was not available from some sources and CommonCrawl processing started from WET files. 
 
 ## Additional Information
 
@@ -181,8 +183,9 @@ The dataset is released under the terms of [ODC-BY](https://opendatacommons.org/
 
 ### Citation Information
 
-NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv, 2022.
+Hefferman et al, Bitext Mining Using Distilled Sentence Representations for Low-Resource Languages. Arxiv https://arxiv.org/abs/2205.12654, 2022.
+NLLB Team et al, No Language Left Behind: Scaling Human-Centered Machine Translation, Arxiv https://arxiv.org/abs/2207.04672, 2022
 
 ### Contributions
 
-We thank the AllenNLP team at AI2 for hosting and releasing this data, including [Akshita Bhagia](https://akshitab.github.io/) (for engineering efforts to create the huggingface dataset), and [Jesse Dodge](https://jessedodge.github.io/) (for organizing the connection).
+We thank the NLLB Meta AI team for open sourcing the meta data and instructions on how to use it with special thanks to  Bapi Akula, Pierre Andrews, Onur Çelebi, Sergey Edunov, Kenneth Heafield, Philipp Koehn, Alex Mourachko, Safiyyah Saleem, Holger Schwenk, and Guillaume Wenzek. We also thank the AllenNLP team at AI2 for hosting and releasing this data, including Akshita Bhagia (for engineering efforts to host the data, and create the huggingface dataset), and Jesse Dodge (for organizing the connection).