diff --git a/models/spring-ai-transformers/src/main/java/org/springframework/ai/transformers/TransformersEmbeddingModel.java b/models/spring-ai-transformers/src/main/java/org/springframework/ai/transformers/TransformersEmbeddingModel.java
index d86460526..f09d8a22e 100644
--- a/models/spring-ai-transformers/src/main/java/org/springframework/ai/transformers/TransformersEmbeddingModel.java
+++ b/models/spring-ai-transformers/src/main/java/org/springframework/ai/transformers/TransformersEmbeddingModel.java
@@ -58,9 +58,24 @@ import ai.onnxruntime.OrtSession;
 import io.micrometer.observation.ObservationRegistry;
 
 /**
- * https://www.sbert.net/index.html https://www.sbert.net/docs/pretrained_models.html
+ * An implementation of the AbstractEmbeddingModel that uses ONNX-based Transformer models
+ * for text embeddings.
+ *
+ * <p>
+ * By default, it uses the all-MiniLM-L6-v2 model, but can be configured to use other
+ * ONNX-compatible models. The class supports both CPU and GPU inference, caching of model
+ * resources, and various tokenization options.
+ * </p>
+ *
+ * <p>
+ * For more information on the underlying SBERT framework, see:
+ * <a href="https://www.sbert.net/index.html">SBERT Documentation</a>
+ * <a href="https://www.sbert.net/docs/pretrained_models.html">SBERT Pre-trained
+ * Models</a>
+ * </p>
  *
  * @author Christian Tzolov
+ * @since 1.0.0
  */
 public class TransformersEmbeddingModel extends AbstractEmbeddingModel implements InitializingBean {
 
@@ -209,13 +224,14 @@ public class TransformersEmbeddingModel extends AbstractEmbeddingModel implement
 		// onnxruntime
 		this.environment = OrtEnvironment.getEnvironment();
 
-		var sessionOptions = new OrtSession.SessionOptions();
-		if (this.gpuDeviceId >= 0) {
-			sessionOptions.addCUDA(this.gpuDeviceId); // Run on a GPU or with another
-														// provider
+		try (var sessionOptions = new OrtSession.SessionOptions()) {
+			if (this.gpuDeviceId >= 0) {
+				sessionOptions.addCUDA(this.gpuDeviceId); // Run on a GPU or with another
+				// provider
+			}
+			this.session = this.environment.createSession(getCachedResource(this.modelResource).getContentAsByteArray(),
+					sessionOptions);
 		}
-		this.session = this.environment.createSession(getCachedResource(this.modelResource).getContentAsByteArray(),
-				sessionOptions);
 
 		this.onnxModelInputs = this.session.getInputNames();
 		Set<String> onnxModelOutputs = this.session.getOutputNames();
@@ -291,39 +307,44 @@ public class TransformersEmbeddingModel extends AbstractEmbeddingModel implement
 						token_type_ids0[i] = encodings[i].getTypeIds();
 					}
 
-					OnnxTensor inputIds = OnnxTensor.createTensor(this.environment, input_ids0);
-					OnnxTensor attentionMask = OnnxTensor.createTensor(this.environment, attention_mask0);
-					OnnxTensor tokenTypeIds = OnnxTensor.createTensor(this.environment, token_type_ids0);
+					try (OnnxTensor inputIds = OnnxTensor.createTensor(this.environment, input_ids0);
+							OnnxTensor attentionMask = OnnxTensor.createTensor(this.environment, attention_mask0);
+							OnnxTensor tokenTypeIds = OnnxTensor.createTensor(this.environment, token_type_ids0);) {
 
-					Map<String, OnnxTensor> modelInputs = Map.of("input_ids", inputIds, "attention_mask", attentionMask,
-							"token_type_ids", tokenTypeIds);
+						Map<String, OnnxTensor> modelInputs = Map.of("input_ids", inputIds, "attention_mask",
+								attentionMask, "token_type_ids", tokenTypeIds);
 
-					modelInputs = removeUnknownModelInputs(modelInputs);
+						modelInputs = removeUnknownModelInputs(modelInputs);
 
-					// The Run result object is AutoCloseable to prevent references from
-					// leaking
-					// out. Once the Result object is
-					// closed, all it’s child OnnxValues are closed too.
-					try (OrtSession.Result results = this.session.run(modelInputs)) {
+						// The Run result object is AutoCloseable to prevent references
+						// from leaking out. Once the Result object is
+						// closed, all it’s child OnnxValues are closed too.
+						try (OrtSession.Result results = this.session.run(modelInputs)) {
 
-						// OnnxValue lastHiddenState = results.get(0);
-						OnnxValue lastHiddenState = results.get(this.modelOutputName).get();
+							// OnnxValue lastHiddenState = results.get(0);
+							OnnxValue lastHiddenState = results.get(this.modelOutputName).get();
 
-						// 0 - batch_size (1..x)
-						// 1 - sequence_length (128)
-						// 2 - embedding dimensions (384)
-						float[][][] tokenEmbeddings = (float[][][]) lastHiddenState.getValue();
+							// 0 - batch_size (1..x)
+							// 1 - sequence_length (128)
+							// 2 - embedding dimensions (384)
+							float[][][] tokenEmbeddings = (float[][][]) lastHiddenState.getValue();
 
-						try (NDManager manager = NDManager.newBaseManager()) {
-							NDArray ndTokenEmbeddings = create(tokenEmbeddings, manager);
-							NDArray ndAttentionMask = manager.create(attention_mask0);
+							try (NDManager manager = NDManager.newBaseManager()) {
+								NDArray ndTokenEmbeddings = create(tokenEmbeddings, manager);
+								NDArray ndAttentionMask = manager.create(attention_mask0);
 
-							NDArray embedding = meanPooling(ndTokenEmbeddings, ndAttentionMask);
+								NDArray embedding = meanPooling(ndTokenEmbeddings, ndAttentionMask);
 
-							for (int i = 0; i < embedding.size(0); i++) {
-								resultEmbeddings.add(embedding.get(i).toFloatArray());
+								for (int i = 0; i < embedding.size(0); i++) {
+									resultEmbeddings.add(embedding.get(i).toFloatArray());
+								}
 							}
 						}
+						finally {
+							inputIds.close();
+							attentionMask.close();
+							tokenTypeIds.close();
+						}
 					}
 				}
 				catch (OrtException ex) {