diff --git a/extensions/tokenizers/src/main/java/ai/djl/huggingface/translator/TextEmbeddingTranslator.java b/extensions/tokenizers/src/main/java/ai/djl/huggingface/translator/TextEmbeddingTranslator.java
index 81e69b25e5d..1ef7f433ba7 100644
--- a/extensions/tokenizers/src/main/java/ai/djl/huggingface/translator/TextEmbeddingTranslator.java
+++ b/extensions/tokenizers/src/main/java/ai/djl/huggingface/translator/TextEmbeddingTranslator.java
@@ -177,6 +177,9 @@ private NDArray processEmbedding(NDList list, NDArray attentionMask) {
             case "cls":
                 embedding = embedding.get(new NDIndex(":, 0"));
                 break;
+            case "lasttoken":
+                embedding = lastTokenPool(embedding, attentionMask);
+                break;
             default:
                 throw new AssertionError("Unexpected pooling mode: " + pooling);
         }
@@ -239,6 +242,20 @@ private static NDArray weightedMeanPool(NDArray embeddings, NDArray attentionMas
         return embeddingSum.div(maskSum);
     }
 
+    private static NDArray lastTokenPool(NDArray embeddings, NDArray attentionMask) {
+        long sum = attentionMask.get(":, -1").sum().getLong();
+        if (sum == attentionMask.getShape().get(0)) {
+            // left padding
+            return embeddings.get(":, -1");
+        }
+
+        long sequenceLength = attentionMask.sum(new int[] {1}).getLong() - 1;
+        long batchSize = embeddings.getShape().get(0);
+        embeddings = embeddings.get(":, " + sequenceLength);
+        NDArray index = embeddings.getManager().arange(batchSize);
+        return embeddings.get(index);
+    }
+
     /**
      * Creates a builder to build a {@code TextEmbeddingTranslator}.
      *
@@ -313,10 +330,11 @@ public Builder optPoolingMode(String poolingMode) {
                     && !"max".equals(poolingMode)
                     && !"cls".equals(poolingMode)
                     && !"mean_sqrt_len".equals(poolingMode)
+                    && !"lasttoken".equals(poolingMode)
                     && !"weightedmean".equals(poolingMode)) {
                 throw new IllegalArgumentException(
                         "Invalid pooling model, must be one of [mean, max, cls, mean_sqrt_len,"
-                                + " weightedmean].");
+                                + " weightedmean, lasttoken].");
             }
             this.pooling = poolingMode;
             return this;
diff --git a/extensions/tokenizers/src/test/java/ai/djl/huggingface/tokenizers/TextEmbeddingTranslatorTest.java b/extensions/tokenizers/src/test/java/ai/djl/huggingface/tokenizers/TextEmbeddingTranslatorTest.java
index 15a670860f7..68a677202df 100644
--- a/extensions/tokenizers/src/test/java/ai/djl/huggingface/tokenizers/TextEmbeddingTranslatorTest.java
+++ b/extensions/tokenizers/src/test/java/ai/djl/huggingface/tokenizers/TextEmbeddingTranslatorTest.java
@@ -163,6 +163,52 @@ public void testTextEmbeddingTranslator()
             Assertions.assertAlmostEquals(res[0], 0.05103104);
         }
 
+        // pooling_lasttokens with left padding
+        criteria =
+                Criteria.builder()
+                        .setTypes(String.class, float[].class)
+                        .optModelPath(modelDir)
+                        .optArgument("blockFactory", "ai.djl.nn.OnesBlockFactory")
+                        .optArgument("block_shapes", "(1,7,384)")
+                        .optArgument("block_names", "last_hidden_state")
+                        .optEngine("PyTorch")
+                        .optArgument("tokenizer", "intfloat/e5-mistral-7b-instruct")
+                        .optArgument("pooling", "lasttoken")
+                        .optOption("hasParameter", "false")
+                        .optTranslatorFactory(new TextEmbeddingTranslatorFactory())
+                        .build();
+
+        try (ZooModel<String, float[]> model = criteria.loadModel();
+                Predictor<String, float[]> predictor = model.newPredictor()) {
+            float[] res = predictor.predict(text);
+            Assert.assertEquals(res.length, 384);
+            Assertions.assertAlmostEquals(res[0], 0.05103104);
+        }
+
+        // pooling_lasttokens
+        criteria =
+                Criteria.builder()
+                        .setTypes(String.class, float[].class)
+                        .optModelPath(modelDir)
+                        .optArgument("blockFactory", "ai.djl.nn.OnesBlockFactory")
+                        .optArgument("block_shapes", "(1,7,384)")
+                        .optArgument("block_names", "last_hidden_state")
+                        .optEngine("PyTorch")
+                        .optArgument("tokenizer", "bert-base-uncased")
+                        .optArgument("pooling", "lasttoken")
+                        .optArgument("padding", "max_length")
+                        .optArgument("maxLength", 10)
+                        .optOption("hasParameter", "false")
+                        .optTranslatorFactory(new TextEmbeddingTranslatorFactory())
+                        .build();
+
+        try (ZooModel<String, float[]> model = criteria.loadModel();
+                Predictor<String, float[]> predictor = model.newPredictor()) {
+            float[] res = predictor.predict(text);
+            Assert.assertEquals(res.length, 384);
+            Assertions.assertAlmostEquals(res[0], 0.05103104);
+        }
+
         // dense and layerNorm
         criteria =
                 Criteria.builder()