feat: add CustomRetrievalEvaluator as a mteb wrapper + update `Co…

…lModelTraining`

colpali_engine/trainer/colmodel_training.py

@@ -25,6 +25,7 @@
     ColbertPairwiseNegativeCELoss,
 )
 from colpali_engine.trainer.contrastive_trainer import ContrastiveNegativeTrainer, ContrastiveTrainer
+from colpali_engine.trainer.eval_utils import CustomRetrievalEvaluator
 from colpali_engine.utils.gpu_stats import print_gpu_utilization, print_summary
 
 
@@ -117,13 +118,14 @@ def __init__(self, config: ColModelTrainingConfig) -> None:
                 max_length=self.config.max_length,
             )
         self.current_git_hash = os.popen("git rev-parse HEAD").read().strip()
-        self.retriever_evaluator = RetrievalScorer(
+        self.retriever_scorer = RetrievalScorer(
             is_multi_vector=(
                 isinstance(self.config.loss_func, ColbertLoss)
                 or isinstance(self.config.loss_func, ColbertPairwiseCELoss)
                 or isinstance(self.config.loss_func, ColbertPairwiseNegativeCELoss)
             )
         )
+        self.retrieval_evaluator = CustomRetrievalEvaluator()
 
     def train(self) -> None:
         if isinstance(self.collator, HardNegCollator):
@@ -231,7 +233,7 @@ def eval_dataset(self, test_dataset):
                         qs.extend(list(torch.unbind(query.to("cpu"))))
 
         print("Embeddings computed, evaluating")
-        scores = self.retriever_evaluator.evaluate(qs, ps)
+        scores = self.retriever_scorer.evaluate(qs, ps)
         # scores is 2d array of shape (n_queries, n_docs)
         # turn it into a dict
         results = {}
@@ -242,8 +244,9 @@ def eval_dataset(self, test_dataset):
             }
 
         # evaluate
-        metrics = self.retriever_evaluator.compute_metrics(relevant_docs, results)
-        print(metrics)
+        metrics = self.retrieval_evaluator.compute_mteb_metrics(relevant_docs, results)
+        print("MTEB metrics:", metrics)
+
         return metrics
 
     def eval(self) -> None:

colpali_engine/trainer/eval_utils.py

@@ -0,0 +1,37 @@
+from typing import Dict
+
+from mteb.evaluation.evaluators import RetrievalEvaluator
+
+
+class CustomRetrievalEvaluator(RetrievalEvaluator):
+    """
+    Wrapper class for the MTEB retrieval evaluator.
+    """
+
+    def compute_mteb_metrics(
+        self,
+        relevant_docs: Dict[str, dict[str, int]],
+        results: Dict[str, dict[str, float]],
+        **kwargs,
+    ) -> Dict[str, float]:
+        """
+        Compute the MTEB retrieval metrics.
+        """
+        ndcg, _map, recall, precision, naucs = self.evaluate(
+            relevant_docs,
+            results,
+            self.k_values,
+            ignore_identical_ids=kwargs.get("ignore_identical_ids", True),
+        )
+
+        mrr = self.evaluate_custom(relevant_docs, results, self.k_values, "mrr")
+
+        scores = {
+            **{f"ndcg_at_{k.split('@')[1]}": v for (k, v) in ndcg.items()},
+            **{f"map_at_{k.split('@')[1]}": v for (k, v) in _map.items()},
+            **{f"recall_at_{k.split('@')[1]}": v for (k, v) in recall.items()},
+            **{f"precision_at_{k.split('@')[1]}": v for (k, v) in precision.items()},
+            **{f"mrr_at_{k.split('@')[1]}": v for (k, v) in mrr[0].items()},
+            **{f"naucs_at_{k.split('@')[1]}": v for (k, v) in naucs.items()},
+        }
+        return scores