Update README.md

README.md

@@ -1,148 +1,227 @@
----
-dataset_info:
-- config_name: 2022-digavis-bigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: string
-  - name: third
-    dtype: float64
-  - name: lang
-    dtype: float64
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 333236587265
-    num_examples: 6512311593
-  download_size: 14612656071
-  dataset_size: 333236587265
-- config_name: 2022-digavis-trigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: string
-  - name: third
-    dtype: string
-  - name: lang
-    dtype: float64
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 246462853849
-    num_examples: 5033888977
-  download_size: 13693035162
-  dataset_size: 246462853849
-- config_name: 2022-digavis-unigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: float64
-  - name: third
-    dtype: float64
-  - name: lang
-    dtype: float64
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 58804597958
-    num_examples: 1105162942
-  download_size: 2553129989
-  dataset_size: 58804597958
-- config_name: 2022-digibok-bigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: string
-  - name: third
-    dtype: float64
-  - name: lang
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 153849577631
-    num_examples: 3045132754
-  download_size: 6719771228
-  dataset_size: 153849577631
-- config_name: 2022-digibok-trigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: string
-  - name: third
-    dtype: string
-  - name: lang
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 90063218292
-    num_examples: 1894490751
-  download_size: 4515186829
-  dataset_size: 90063218292
-- config_name: 2022-digibok-unigram
-  features:
-  - name: first
-    dtype: string
-  - name: second
-    dtype: float64
-  - name: third
-    dtype: float64
-  - name: lang
-    dtype: string
-  - name: year
-    dtype: int64
-  - name: count
-    dtype: int64
-  splits:
-  - name: train
-    num_bytes: 22193315529
-    num_examples: 418986743
-  download_size: 978036048
-  dataset_size: 22193315529
-configs:
-- config_name: 2022-digavis-bigram
-  data_files:
-  - split: train
-    path: 2022-digavis-bigram/train-*
-- config_name: 2022-digavis-trigram
-  data_files:
-  - split: train
-    path: 2022-digavis-trigram/train-*
-- config_name: 2022-digavis-unigram
-  data_files:
-  - split: train
-    path: 2022-digavis-unigram/train-*
-- config_name: 2022-digibok-bigram
-  data_files:
-  - split: train
-    path: 2022-digibok-bigram/train-*
-- config_name: 2022-digibok-trigram
-  data_files:
-  - split: train
-    path: 2022-digibok-trigram/train-*
-- config_name: 2022-digibok-unigram
-  data_files:
-  - split: train
-    path: 2022-digibok-unigram/train-*
----
+---
+dataset_info:
+- config_name: 2022-digavis-bigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: string
+  - name: third
+    dtype: float64
+  - name: lang
+    dtype: float64
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 333236587265
+    num_examples: 6512311593
+  download_size: 14612656071
+  dataset_size: 333236587265
+- config_name: 2022-digavis-trigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: string
+  - name: third
+    dtype: string
+  - name: lang
+    dtype: float64
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 246462853849
+    num_examples: 5033888977
+  download_size: 13693035162
+  dataset_size: 246462853849
+- config_name: 2022-digavis-unigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: float64
+  - name: third
+    dtype: float64
+  - name: lang
+    dtype: float64
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 58804597958
+    num_examples: 1105162942
+  download_size: 2553129989
+  dataset_size: 58804597958
+- config_name: 2022-digibok-bigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: string
+  - name: third
+    dtype: float64
+  - name: lang
+    dtype: string
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 153849577631
+    num_examples: 3045132754
+  download_size: 6719771228
+  dataset_size: 153849577631
+- config_name: 2022-digibok-trigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: string
+  - name: third
+    dtype: string
+  - name: lang
+    dtype: string
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 90063218292
+    num_examples: 1894490751
+  download_size: 4515186829
+  dataset_size: 90063218292
+- config_name: 2022-digibok-unigram
+  features:
+  - name: first
+    dtype: string
+  - name: second
+    dtype: float64
+  - name: third
+    dtype: float64
+  - name: lang
+    dtype: string
+  - name: year
+    dtype: int64
+  - name: count
+    dtype: int64
+  splits:
+  - name: train
+    num_bytes: 22193315529
+    num_examples: 418986743
+  download_size: 978036048
+  dataset_size: 22193315529
+configs:
+- config_name: 2022-digavis-bigram
+  data_files:
+  - split: train
+    path: 2022-digavis-bigram/train-*
+- config_name: 2022-digavis-trigram
+  data_files:
+  - split: train
+    path: 2022-digavis-trigram/train-*
+- config_name: 2022-digavis-unigram
+  data_files:
+  - split: train
+    path: 2022-digavis-unigram/train-*
+- config_name: 2022-digibok-bigram
+  data_files:
+  - split: train
+    path: 2022-digibok-bigram/train-*
+- config_name: 2022-digibok-trigram
+  data_files:
+  - split: train
+    path: 2022-digibok-trigram/train-*
+- config_name: 2022-digibok-unigram
+  data_files:
+  - split: train
+    path: 2022-digibok-unigram/train-*
+license: cc0-1.0
+language:
+- nn
+- nb
+---
+
+# Dataset Card for N-grams from NBdigital
+
+This dataset contains n-grams (uni-, bi- and trigrams) from all books and newspapers digitized by the National Library of Norway before 2022-07-15.
+The N-grams are made based on approximately 610 000 books and 4 000 000 newspapers.
+In total, it's about 138.5 billion "tokens" (i.e. words and punctuation).
+
+## Dataset Details
+
+### Dataset Description
+
+This dataset contains frequency information on word and punctuation usage in Norwegian.
+In particular, it contains uni- bi and trigram counts for each year.
+
+- **Curated by:** [The Norwegian Language Bank](https://www.nb.no/sprakbanken/en/) at [the National Library of Norway](https://www.nb.no/)
+- **Shared by:** [The Norwegian Language Bank](https://www.nb.no/sprakbanken/en/) at [the National Library of Norway](https://www.nb.no/)
+- **Language(s) (NLP):** nob, nno
+- **License:** CC-0
+
+### Dataset Sources
+
+- **Source dataset:** https://www.nb.no/sprakbanken/ressurskatalog/oai-nb-no-sbr-76/
+- **Paper:** Birkenes MB, Johnsen LG, Lindstad AM, Ostad J. From digital library to n-grams: NB N-gram. InProceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015) 2015 May (pp. 293-295).
+- **Demo:** https://dh.nb.no/run/corpus-webapp/app/
+
+## Dataset Structure
+
+The N-gram dataset is separated into several subsets for different corpora (`digavis` and `digibok`). The `digavis` dataset contains N-gram counts from newspapers and the  `digibok` datasets contains N-gram counts from books. Moreover, the dataset contains the following fields:
+
+ * `first`: The first token in the N-gram
+ * `second` (optional): The second token in the N-gram (for bi- and trigrams)
+ * `third` (optional): The third token in the N-gram (for trigrams)
+ * `lang` (optional): The language of the documents the N-grams are counted from (only for `digibok`)
+ * `year`: The year
+ * `count`: The number of times the given N-gram occures in the given year
+
+For example, in the `2022-digavis-unigram` dataset, we have the row
+
+```raw
+first="hei", year=1972, count=4460
+```
+
+This means that the word "hei" occured 4460 times in the National Library of Norway's digitized newspapers from 1972.
+Similarly, if we consider the row
+
+```raw
+first="hei", second="til", lang="nob", year=1990, count=60
+```
+
+in the `2022-digibok-trigram` dataset, then that means that the phrase "hei til" occured 60 times in the National Library of Norway's digitized books in Norwegian Bokmål from 1990.
+
+## Dataset Creation
+
+This data stems from all books and newspapers in the National Library of Norway's digitized collection, which consists of almost all books and newspapers published in Norway.
+Note that much of the text stems from automatic text recognition of scanned book and newspaper pages, which may contain mistakes.
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@inproceedings{birkenes2015digital,
+  title={From digital library to n-grams: NB N-gram},
+  author={Birkenes, Magnus Breder and Johnsen, Lars G and Lindstad, Arne Martinus and Ostad, Johanne},
+  booktitle={Proceedings of the 20th Nordic Conference of Computational Linguistics (NODALIDA 2015)},
+  pages={293--295},
+  year={2015}
+}
+```
+
+## Dataset Card Contact
+
+```
+sprakbanken [at] nb [dot] no
+```