diff --git a/colpali_engine/loss/colbert_loss.py b/colpali_engine/loss/colbert_loss.py
index c0dad1eb..655237f3 100644
--- a/colpali_engine/loss/colbert_loss.py
+++ b/colpali_engine/loss/colbert_loss.py
@@ -154,3 +154,36 @@ def forward(self, query_embeddings, doc_embeddings):
         loss = F.softplus(neg_scores - pos_scores).mean()
 
         return loss
+
+
+class BiPairwiseNegativeCELoss(torch.nn.Module):
+    def __init__(self, in_batch_term=False):
+        super().__init__()
+        self.ce_loss = CrossEntropyLoss()
+        self.in_batch_term = in_batch_term
+
+    def forward(self, query_embeddings, doc_embeddings, neg_doc_embeddings):
+        """
+        query_embeddings: (batch_size, dim)
+        doc_embeddings: (batch_size, dim)
+        neg_doc_embeddings: (batch_size, dim)
+        """
+
+        # Compute the ColBERT scores
+        pos_scores = torch.einsum("bd,cd->bc", query_embeddings, doc_embeddings)
+        neg_scores = torch.einsum("bd,cd->bc", query_embeddings, neg_doc_embeddings)
+        loss = F.softplus(neg_scores - pos_scores).mean()
+
+        if self.in_batch_term:
+            scores = (
+                torch.einsum("bnd,csd->bcns", query_embeddings, doc_embeddings).max(dim=3)[0].sum(dim=2)
+            )  # (batch_size, batch_size)
+
+            # Positive scores are the diagonal of the scores matrix.
+            pos_scores = scores.diagonal()  # (batch_size,)
+            neg_scores = scores - torch.eye(scores.shape[0], device=scores.device) * 1e6  # (batch_size, batch_size)
+            neg_scores = neg_scores.max(dim=1)[0]  # (batch_size,)
+
+            loss += F.softplus(neg_scores - pos_scores).mean()
+
+        return loss / 2
\ No newline at end of file
diff --git a/scripts/configs/pali/train_bipali_pairwise_model.yaml b/scripts/configs/pali/train_bipali_pairwise_model.yaml
new file mode 100644
index 00000000..9bb85442
--- /dev/null
+++ b/scripts/configs/pali/train_bipali_pairwise_model.yaml
@@ -0,0 +1,41 @@
+config:
+  (): colpali_engine.utils.train_colpali_engine_models.ColModelTrainingConfig
+  output_dir: !path ../../../models/right_pad/train_bipali_pairwise
+  processor:
+    () : colpali_engine.utils.wrapper.AutoProcessorWrapper
+    pretrained_model_name_or_path: "./models/paligemma-3b-mix-448"
+    max_length: 50
+  model:
+    (): colpali_engine.utils.wrapper.AutoColModelWrapper
+    pretrained_model_name_or_path: "./models/paligemma-3b-mix-448"
+    training_objective: "biencoder_mean"
+    # attn_implementation: "eager"
+    torch_dtype:  !ext torch.bfloat16
+#    device_map: "auto"
+#    quantization_config:
+#      (): transformers.BitsAndBytesConfig
+#      load_in_4bit: true
+#      bnb_4bit_quant_type: "nf4"
+#      bnb_4bit_compute_dtype:  "bfloat16"
+#      bnb_4bit_use_double_quant: true
+
+  dataset_loading_func: !ext colpali_engine.utils.dataset_transformation.load_train_set
+  eval_dataset_loader: !import ../data/test_data.yaml
+
+  max_length: 50
+  run_eval: true
+  add_suffix: true
+  loss_func:
+    (): colpali_engine.loss.colbert_loss.BiPairwiseCELoss
+  tr_args: !import ../tr_args/default_tr_args.yaml
+  peft_config:
+    (): peft.LoraConfig
+    r: 32
+    lora_alpha: 32
+    lora_dropout: 0.1
+    init_lora_weights: "gaussian"
+    bias: "none"
+    task_type: "FEATURE_EXTRACTION"
+    target_modules: '(.*(language_model).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$)'
+    # target_modules: '(.*(language_model).*(down_proj|gate_proj|up_proj|k_proj|q_proj|v_proj|o_proj).*$'
+