feat: add asian language support to CorpusLevelTranslationMetric

src/lighteval/metrics/metrics_corpus.py

@@ -26,6 +26,7 @@
 """
 import logging
 import math
+from typing import Literal
 
 import numpy as np
 import sacrebleu
@@ -89,33 +90,38 @@ def compute(self, items: list[LogprobCorpusMetricInput]):
 
 
 class CorpusLevelTranslationMetric:
-    def __init__(self, metric_type: str):
+    def __init__(self, metric_type: str, lang: Literal["zh", "ja", "ko", ""] = ""):
         """Stores the relevant parameters for a corpus level translation metric.
 
         Args:
             metric_type (str): Can be any of bleu, chrf, or ter depending on the metric to use.
         """
-        if metric_type == "bleu":
-            self.metric = sacrebleu.corpus_bleu
-        elif metric_type == "chrf":
-            self.metric = sacrebleu.corpus_chrf
-        elif metric_type == "ter":
-            self.metric = sacrebleu.corpus_ter
+        self.metric_type = metric_type
+        self.lang = lang
+
+    def get_metric(self):
+        if self.metric_type == "bleu":
+            return sacrebleu.BLEU(trg_lang=self.lang)
+        elif self.metric_type == "chrf":
+            return sacrebleu.CHRF()
+        elif self.metric_type == "ter":
+            return sacrebleu.TER(asian_support=True if self.lang != "" else False)
         else:
-            raise ValueError(f"Unknown corpus level translation metric type : {metric_type}")
+            raise ValueError(f"Unknown corpus level translation metric type : {self.metric_type}")
 
     def compute(self, items: list[GenerativeCorpusMetricInput]) -> float:
         """Computes the metric score over all the corpus generated items, by using the sacrebleu implementation."""
+        metric = self.get_metric()
         golds = [i.golds for i in items]
         preds = []
         for i in items:
             pred = as_list(i.preds)
             if len(pred) > 1:
                 logger.info(
-                    f"Multiple predictions present, keeping only the first prediction (when computing sacrebleu.{self.metric.__name__})."
+                    f"Multiple predictions present, keeping only the first prediction (when computing sacrebleu.{metric.__name__})."
                 )
             preds.append(pred[0])
-        return float(self.metric(hypotheses=preds, references=golds).score)
+        return float(metric.corpus_score(hypotheses=preds, references=golds).score)
 
 
 class CorpusLevelPerplexityMetric: