norallm
/

normistral-7b-warm

@@ -94,7 +94,7 @@ We release [our codebase here](https://github.com/ltgoslo/norallm). We compare a
 We use the binary formulation of this task (positive vs. negative).
 <details>
-<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".
@@ -127,7 +127,7 @@ We use the binary formulation of this task (positive vs. negative).
 [NorQuAD](https://huggingface.co/datasets/ltg/norquad) ([Ivanova et al., 2023](https://aclanthology.org/2023.nodalida-1.17/)) is a dataset for extractive question answering in Norwegian designed similarly to [SQuAD (Rajpurkar et al., 2016)](https://aclanthology.org/D16-1264/).
 <details>
-<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}"``` Based on [Brown et al. (2020)](https://arxiv.org/abs/2005.14165).
@@ -160,7 +160,7 @@ We use the binary formulation of this task (positive vs. negative).
 [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) [(Tiedemann, 2020)](https://aclanthology.org/2020.wmt-1.139/) is a benchmark for machine translation, which includes hundreds of language pairs. We consider six language pairs (English <-> Bokmål, English <-> Nynorsk, and Bokmål <-> Nynorsk).
 <details>
-<summary>Method</summary>
 * Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
 * Prompt: ```"{source_language}: {source_text}\n{target_language}:{target_text}"```, where the ```source_language``` and ```target_language``` are ```Engelsk```, ```Bokmål```, or ```Nynorsk```. Based on [Garcia et al. (2023)](https://arxiv.org/abs/2302.01398).

 We use the binary formulation of this task (positive vs. negative).
 <details>
+<summary>Method (click to expand)</summary>
 * Evaluation setting: zero-shot and few-shot perplexity-based evaluation.
 * Prompt: ```"Tekst: {text}\nSentiment:{label}"```, where the ```label``` is either "positiv" or "negativ".
 [NorQuAD](https://huggingface.co/datasets/ltg/norquad) ([Ivanova et al., 2023](https://aclanthology.org/2023.nodalida-1.17/)) is a dataset for extractive question answering in Norwegian designed similarly to [SQuAD (Rajpurkar et al., 2016)](https://aclanthology.org/D16-1264/).
 <details>
+<summary>Method (click to expand)</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}"``` Based on [Brown et al. (2020)](https://arxiv.org/abs/2005.14165).
 [Tatoeba](https://huggingface.co/datasets/Helsinki-NLP/tatoeba_mt) [(Tiedemann, 2020)](https://aclanthology.org/2020.wmt-1.139/) is a benchmark for machine translation, which includes hundreds of language pairs. We consider six language pairs (English <-> Bokmål, English <-> Nynorsk, and Bokmål <-> Nynorsk).
 <details>
+<summary>Method (click to expand)</summary>
 * Evaluation setting: zero-shot and few-shot settings via natural language generation using the greedy decoding strategy.
 * Prompt: ```"{source_language}: {source_text}\n{target_language}:{target_text}"```, where the ```source_language``` and ```target_language``` are ```Engelsk```, ```Bokmål```, or ```Nynorsk```. Based on [Garcia et al. (2023)](https://arxiv.org/abs/2302.01398).