Update README.md

README.md

@@ -48,7 +48,7 @@ task_ids:
 - **Point of Contact:** [Per Erik Solberg](mailto:per.solberg@nb.no)
 
 The Norwegian Parliament Speech Corpus (NPSC) is a corpus for training a Norwegian ASR (Automatic Speech Recognition) models.
-**!!!NEDS TO BE UPDATED!!! We need a real description here. About one paragraph summing up that is on the main web page, and telling how this dataset is the same but different - ie it is in a streaming format...**
+**!!!PER ERIK - COULD YOU PLEASE UPDATE THIS? 1-2 paragraphs. FOR PEOPLE NOT BOTHERING TO READ THE ARTICLE!!! We need a real description here. About one paragraph summing up that is on the main web page, and telling how this dataset is the same but different - ie it is in a streaming format... This is X days of transcripts of parliamentary speeches. Each day XX hours. Most without any manuscript. They are split into XXXXX based on.... The id allows you to combine this into single sound files....**
 
 ## How to Use
 ```python
@@ -81,15 +81,15 @@ The NPSC dataset contains json lines with language training data. Here is an exa
 
 
 ```
-## Data Fields
+## Data Fields (!!!!PER ERIK - We really need a descrption here - and maybe some cleaning!!!)
 |**id:** | String with id to source of line and a unique identifier|
 |:-----------|:------------|
 |**sentence_order** | String with order of sentence |
 |**speaker id** | Integer id of speaker |
 | **speaker_name** | String name of speaker |
 | **sentence_text** | String sentence text |
-| **sentence_language_code** | String sentence text |
-| **text** | String sentence text |
+| **sentence_language_code** | String sentence text (!!!PER ERIK - LIST ALL ALTERNATIVES!!!)|
+| **text** | String sentence text. This is a copy of "sentence_text". It is included here to make it more convenient to interleave with other datasets.|
 | **start_time** | int start time |
 | **end_time** | int end time |
 | **normsentence_text** | String normalised sentence text |
@@ -101,11 +101,10 @@ The NPSC dataset contains json lines with language training data. Here is an exa
 
 ### Dataset Creation
 We are providing a **train** and a **validation** split. The standard size of the validation is a single 1GB file, while train is sharded in 1GB chunks.
-All files are gzipped. There is also a **test** split available for the dataset but this is hidden. Please contact [Per Erik Solberg](mailto:per.erik.solberg@nb.no) for access to the test set.
+All files are gzipped. There is also a **test** split available for the dataset but this is hidden. Please contact [Per Erik Solberg](mailto:per.erik.solberg@nb.no) for access to the test set. !!!!!PER ERIK - Verify that this is correct!!!!!!!!!!!!!!!
 
-Build date: 22012022
 
-#### Initial Data Collection and Curation
+#### Initial Data Collection
 The procedure for the dataset creation is described in detail in our paper.
 
 
@@ -117,43 +116,32 @@ The procedure for the dataset creation is described in detail in our paper.
 | Word count     | 1,2 million | 
 | Sentence count     | 64.531 | 
 | Language distribution     | Nynorsk: 12,8%| 
-|      | Bokmål: 87,2%%| 
+|      | Bokmål: 87,2%| 
 | Gender distribution     | Female: 38,3% | 
 |      | Male: 61.7% | 
 
-## Considerations for Using the Data
-This corpus contains speech data and is allowed to be used outside the National Library of Norway for speech recognition technology purposes.
 
-### Dataset Curators
-[Freddy Wetjen](mailto:Freddy.wetjen@nb.no) and [Andre Kaasen](mailto:andre.kasen@nb.no) **!!!NEDS TO BE UPDATED!!! Underline the origin of the data and the project. Add Javier de la Rosa and Per Egil Kummervold to the people having worked on this!!!**
+!!!PER ERIK - It would be great to know how many hours of Bokmål/Nynorsk/English(?) here!!!!!
+
+## Considerations for Using the Data
+This corpus contains speech data. All recordings are of parliament members in a public setting, and can be distributed without any restrains.
 
-### Licensing Information
-Licensed for use outside the National Library of Norway.
+### Dataset Creators and Curators
+The content of the dataset was done by Språkbanken. !!!!!FILL IN HERE PER ERIK!!!!!!!!!!!!!!!. [Javier de la Rosa](mailto:javier.rosa@nb.no), [Freddy Wetjen](mailto:Freddy.wetjen@nb.no), [Per Egil Kummervold](mailto:per.kummervold@nb.no), and [Andre Kaasen](mailto:andre.kasen@nb.no) all contributed in making this into a HuggingFace Dataset. Thanks to the HuggingFace team for assistance.
 
 ## License
-The dataset is released under the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/). The curation of the data
+The sound and the transcriptions are released under the [CC-ZERO-license](https://creativecommons.org/publicdomain/zero/1.0/). The curation of the HuggingFace Dataset is released under [CC-BY-SA-3-license](https://creativecommons.org/licenses/by-sa/3.0/).
 
 ### Citation Information
-We are preparing an article with detailed information about this corpus. Until it is published, please cite out paper discussing the first version of this corpus:
+The following article gives detailed information about the corpus. Please refer to the article and this page if you are using this dataset:
 ```
 
-**!!!NEDS TO BE UPDATED!!!**
-@inproceedings{kummervold-etal-2021-operationalizing,
-title = {Operationalizing a National Digital Library: The Case for a {N}orwegian Transformer Model},
-author = {Kummervold, Per E  and
-De la Rosa, Javier  and
-Wetjen, Freddy  and
-Brygfjeld, Svein Arne",
-booktitle = {Proceedings of the 23rd Nordic Conference on Computational Linguistics (NoDaLiDa)},
-year = "2021",
-address = "Reykjavik, Iceland (Online)",
-publisher = {Link{"o}ping University Electronic Press, Sweden},
-url = "https://aclanthology.org/2021.nodalida-main.3",
-pages = "20--29",
-abstract = "In this work, we show the process of building a large-scale training set from digital and digitized collections at a national library.
-The resulting Bidirectional Encoder Representations from Transformers (BERT)-based language model for Norwegian outperforms multilingual BERT (mBERT) models
-in several token and sequence classification tasks for both Norwegian Bokm{aa}l and Norwegian Nynorsk. Our model also improves the mBERT performance for other
-languages present in the corpus such as English, Swedish, and Danish. For languages not included in the corpus, the weights degrade moderately while keeping strong multilingual properties. Therefore,
-we show that building high-quality models within a memory institution using somewhat noisy optical character recognition (OCR) content is feasible, and we hope to pave the way for other memory institutions to follow.",
+@misc{solberg2022norwegian,
+      title={The Norwegian Parliamentary Speech Corpus},
+      author={Per Erik Solberg and Pablo Ortiz},
+      year={2022},
+      eprint={2201.10881},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
 }
 ```