saattrupdan commited on
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e5acaa3
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1 Parent(s): 637c71d

style: Rename "reasoning" to "common-sense reasoning"

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  1. app.py +10 -10
app.py CHANGED
@@ -100,7 +100,7 @@ the [MMLU](https://doi.org/10.48550/arXiv.2009.03300) and
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  [ARC](https://allenai.org/data/arc) datasets. We use the Matthews Correlation
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  Coefficient (MCC) as the evaluation metric.
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- ### Reasoning
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  Given a scenario and multiple possible endings, choose the correct ending. As with text
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  classification, we use the probabilities of the answer letter (a, b, c or d) to choose
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  the answer. The datasets in this task are machine translated versions of the
@@ -164,7 +164,7 @@ class Dataset(BaseModel):
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  SUMMARISATION = Task(name="summarisation", metric="bertscore")
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  KNOWLEDGE = Task(name="knowledge", metric="mcc")
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- REASONING = Task(name="reasoning", metric="mcc")
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  GRAMMAR = Task(name="grammar", metric="mcc")
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  READING_COMPREHENSION = Task(name="reading comprehension", metric="em")
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  TEXT_CLASSIFICATION = Task(name="text classification", metric="mcc")
@@ -246,14 +246,14 @@ DATASETS = [
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  Dataset(name="mmlu", language=ENGLISH, task=KNOWLEDGE),
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  Dataset(name="mmlu-fr", language=FRENCH, task=KNOWLEDGE),
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- Dataset(name="hellaswag-da", language=DANISH, task=REASONING),
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- Dataset(name="hellaswag-no", language=NORWEGIAN, task=REASONING),
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- Dataset(name="hellaswag-sv", language=SWEDISH, task=REASONING),
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- Dataset(name="winogrande-is", language=ICELANDIC, task=REASONING),
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- Dataset(name="hellaswag-de", language=GERMAN, task=REASONING),
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- Dataset(name="hellaswag-nl", language=DUTCH, task=REASONING),
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- Dataset(name="hellaswag", language=ENGLISH, task=REASONING),
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- Dataset(name="hellaswag-fr", language=FRENCH, task=REASONING),
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  ]
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  [ARC](https://allenai.org/data/arc) datasets. We use the Matthews Correlation
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  Coefficient (MCC) as the evaluation metric.
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+ ### Common-sense Reasoning
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  Given a scenario and multiple possible endings, choose the correct ending. As with text
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  classification, we use the probabilities of the answer letter (a, b, c or d) to choose
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  the answer. The datasets in this task are machine translated versions of the
 
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  SUMMARISATION = Task(name="summarisation", metric="bertscore")
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  KNOWLEDGE = Task(name="knowledge", metric="mcc")
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+ COMMON_SENSE_REASONING = Task(name="common-sense reasoning", metric="mcc")
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  GRAMMAR = Task(name="grammar", metric="mcc")
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  READING_COMPREHENSION = Task(name="reading comprehension", metric="em")
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  TEXT_CLASSIFICATION = Task(name="text classification", metric="mcc")
 
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  Dataset(name="mmlu", language=ENGLISH, task=KNOWLEDGE),
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  Dataset(name="mmlu-fr", language=FRENCH, task=KNOWLEDGE),
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+ Dataset(name="hellaswag-da", language=DANISH, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag-no", language=NORWEGIAN, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag-sv", language=SWEDISH, task=COMMON_SENSE_REASONING),
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+ Dataset(name="winogrande-is", language=ICELANDIC, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag-de", language=GERMAN, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag-nl", language=DUTCH, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag", language=ENGLISH, task=COMMON_SENSE_REASONING),
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+ Dataset(name="hellaswag-fr", language=FRENCH, task=COMMON_SENSE_REASONING),
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  ]
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