Update README.md

README.md

@@ -96,7 +96,7 @@ We use the binary formulation of this task (positive vs. negative).
 <summary>Method</summary>
   
 * Evaluation setting: zero-shot and few-shot perplexity-based evaluation.
-* Prompt: ```"Tekst: {text}\nSentiment:{label}"```, where the ```label``` is either "positiv" or "negativ". Based on [Brown et al. (2020)](https://arxiv.org/abs/2005.14165).
+* Prompt: ```"Tekst: {text}\nSentiment:{label}"```, where the ```label``` is either "positiv" or "negativ".
 * Few-shot results show the average scores across 5 repetitions
 * Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/sentiment_analysis.py
 * Performance metric: macro-averaged F1-score.
@@ -129,7 +129,7 @@ We use the binary formulation of this task (positive vs. negative).
 <summary>Method</summary>
   
 * Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
-* Prompt: ```"Tittel: {title}\n\nTekst: {text}\n\nSpørsmål: {question}\n\nSvar:{answer}"```
+* Prompt: ```"Tittel: {title}\n\nTekst: {text}\n\nSpørsmål: {question}\n\nSvar:{answer}"``` Based on [Brown et al. (2020)](https://arxiv.org/abs/2005.14165).
 * Few-shot results show the average scores across 5 repetitions
 * Evaluation script: https://github.com/ltgoslo/norallm/blob/main/initial_evaluation/norquad.py
 * Performance metrics: macro-averaged F1-score and exact match (EM).