Moved unpack to the pipeline code.

community_tasks/swiss_legal_evals.py

@@ -45,6 +45,7 @@
 from nltk.translate import meteor_score
 from packaging import version
 from sacrebleu import sentence_bleu, sentence_chrf, sentence_ter
+from tqdm import tqdm
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
 from lighteval.metrics.imports.bert_scorer import BERTScorer
@@ -520,7 +521,10 @@ def compute(
 
         # Process in batches
         all_scores = []
-        for i in range(0, len(references), self.batch_size):
+        for i in tqdm(
+            range(0, len(references), self.batch_size),
+            desc=f"Processing batches of size {self.batch_size} with {self.metric_name}",
+        ):
             batch_refs = references[i : i + self.batch_size]
             batch_preds = predictions[i : i + self.batch_size]
             try:
@@ -589,15 +593,7 @@ def compute(
         predictions = [response[0].result for response in responses]
         sources = [kwargs["formatted_doc"].specific["source"] for kwargs["formatted_doc"] in formatted_docs]
 
-        def unpack(x):
-            if isinstance(x, str):
-                return x
-            elif isinstance(x, (list, tuple)):
-                return unpack(x[0])
-            else:
-                raise ValueError(f"Unknown type {type(x)} of prediction {x}")
-
-        data = [{"src": src, "mt": unpack(pred), "ref": gold} for src, pred, gold in zip(sources, predictions, golds)]
+        data = [{"src": src, "mt": pred, "ref": gold} for src, pred, gold in zip(sources, predictions, golds)]
         model_output = self.model.predict(
             data,
             batch_size=self.batch_size,