update

README.md

@@ -59,26 +59,37 @@ This is a classification dataset created from a subset of the [Talk of Norway](h
 
 This dataset is meant for classification. Results can for instance be viewed in [this article](https://arxiv.org/abs/2104.09617).
 
+The dataset was also evaluated as part of the [North-T5](https://huggingface.co/north/t5_large_NCC) training. It then gave these results:
+ 
+|**Model:** | **F1** |
+|:-----------|:------------|
+|mT5-base|73.2 |
+|mBERT-base|78.4 |
+|NorBERT-base|78.2 |
+|North-T5-small|80.5 |
+|nb-bert-base|81.8 |
+|North-T5-base|85.3 |
+|North-T5-large|86.7 |
+|North-T5-xl|88.7 |
+|North-T5-xxl|91.8|
+
+
 ### Languages
 
 The text in the dataset is in Norwegian.
 
 ## Dataset Structure
 
-### Data Instances
-
-Example of one instance in the dataset.
-
-```{'label': 0, 'text': 'Verre er det med slagsmålene .', 'date': '2016-01-15'}```
-
 ### Data Fields
 
-- `id`: index of the example
 - `text`: Text of a speech
-- `date`: Date (`YYYY-MM-DD`) the speech was produced
+- `date`: Date (`YYYY-MM-DD`) the speech was held
 - `label`: Political party the speaker was associated with at the time
-  - 0 = Fremskrittspartiet
-  - 1 = Sosialistisk Venstreparti
+  - 0
+  - 1
+- `full_label`: Political party the speaker was associated with at the time
+  - Fremskrittspartiet
+  - Sosialistisk Venstreparti
 
 ### Data Splits
 
@@ -95,7 +106,7 @@ The dataset is balanced on political party.
 This dataset is based on the publicly available information by Norwegian Parliament (Storting) and created by the National Library of Norway AI-Lab to benchmark their language models.
 
 ## Additional Information
-The [Talk of Norway dataset] is also available [here](https://www.nb.no/sprakbanken/ressurskatalog/oai-repo-clarino-uib-no-11509-123/).
+The [Talk of Norway dataset](https://www.nb.no/sprakbanken/ressurskatalog/oai-repo-clarino-uib-no-11509-123/) is also available at the [LTG Talk-of-Norway Github](https://github.com/ltgoslo/talk-of-norway).
 
 ### Licensing Information
 
@@ -123,4 +134,4 @@ in several token and sequence classification tasks for both Norwegian Bokm{aa}l
 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.",
 }
-```
+```