Add trust remote code

autointent/_embedder.py

@@ -73,7 +73,10 @@ def __init__(self, embedder_config: EmbedderConfig) -> None:
         self.config = embedder_config
 
         self.embedding_model = SentenceTransformer(
-            self.config.model_name, device=self.config.device, prompts=embedder_config.get_prompt_config()
+            self.config.model_name,
+            device=self.config.device,
+            prompts=embedder_config.get_prompt_config(),
+            trust_remote_code=self.config.trust_remote_code,
         )
 
         self._logger = logging.getLogger(__name__)

autointent/_ranker.py

@@ -111,7 +111,7 @@ def __init__(
         self.config = CrossEncoderConfig.from_search_config(cross_encoder_config)
         self.cross_encoder = st.CrossEncoder(
             self.config.model_name,
-            trust_remote_code=True,
+            trust_remote_code=self.config.trust_remote_code,
             device=self.config.device,
             max_length=self.config.tokenizer_config.max_length,  # type: ignore[arg-type]
         )

autointent/configs/_transformers.py

@@ -19,6 +19,7 @@ class HFModelConfig(BaseModel):
     batch_size: PositiveInt = Field(32, description="Batch size for model inference.")
     device: str | None = Field(None, description="Torch notation for CPU or CUDA.")
     tokenizer_config: TokenizerConfig = Field(default_factory=TokenizerConfig)
+    trust_remote_code: bool = Field(False, description="Whether to trust the remote code when loading the model.")
 
     @classmethod
     def from_search_config(cls, values: dict[str, Any] | str | BaseModel | None) -> Self:

autointent/modules/scoring/_bert.py

@@ -89,6 +89,7 @@ def fit(
 
         self._model = AutoModelForSequenceClassification.from_pretrained(
             model_name,
+            trust_remote_code=self.classification_model_config.trust_remote_code,
             num_labels=self._n_classes,
             label2id=label2id,
             id2label=id2label,

docs/optimizer_config.schema.json

@@ -32,6 +32,12 @@
                 "tokenizer_config": {
                     "$ref": "#/$defs/TokenizerConfig"
                 },
+                "trust_remote_code": {
+                    "default": false,
+                    "description": "Whether to trust the remote code when loading the model.",
+                    "title": "Trust Remote Code",
+                    "type": "boolean"
+                },
                 "train_head": {
                     "default": false,
                     "description": "Whether to train the head of the model. If False, LogReg will be trained.",
@@ -122,6 +128,12 @@
                 "tokenizer_config": {
                     "$ref": "#/$defs/TokenizerConfig"
                 },
+                "trust_remote_code": {
+                    "default": false,
+                    "description": "Whether to trust the remote code when loading the model.",
+                    "title": "Trust Remote Code",
+                    "type": "boolean"
+                },
                 "default_prompt": {
                     "anyOf": [
                         {
@@ -370,6 +382,7 @@
                     "padding": true,
                     "truncation": true
                 },
+                "trust_remote_code": false,
                 "default_prompt": null,
                 "classifier_prompt": null,
                 "cluster_prompt": null,
@@ -390,6 +403,7 @@
                     "padding": true,
                     "truncation": true
                 },
+                "trust_remote_code": false,
                 "train_head": false
             }
         },

tests/callback/test_callback.py

@@ -146,6 +146,7 @@ def test_pipeline_callbacks(dataset):
                         "query_prompt": None,
                         "sts_prompt": None,
                         "use_cache": False,
+                        "trust_remote_code": False,
                     },
                     "k": 1,
                     "weights": "uniform",
@@ -180,6 +181,7 @@ def test_pipeline_callbacks(dataset):
                         "query_prompt": None,
                         "sts_prompt": None,
                         "use_cache": False,
+                        "trust_remote_code": False,
                     },
                     "k": 1,
                     "weights": "distance",
@@ -214,6 +216,7 @@ def test_pipeline_callbacks(dataset):
                         "query_prompt": None,
                         "sts_prompt": None,
                         "use_cache": False,
+                        "trust_remote_code": False,
                     },
                 },
                 "module_name": "linear",

tests/configs/test_combined_config.py

@@ -75,17 +75,17 @@ def test_invalid_optimizer_config_missing_field():
 def test_invalid_optimizer_config_wrong_type():
     """Test that an invalid field type raises ValidationError."""
     invalid_config = {
-            "node_type": "scoring",
-            "target_metric": "scoring_roc_auc",
-            "search_space": [
-                {
-                    "module_name": "dnnc",
-                    "cross_encoder_name": "cross-encoder/ms-marco-MiniLM-L-6-v2",  # Should be a list
-                    "k": "wrong_type",  # Should be a list of integers
-                    "train_head": "true",  # Should be a boolean, not a string
-                }
-            ],
-        }
+        "node_type": "scoring",
+        "target_metric": "scoring_roc_auc",
+        "search_space": [
+            {
+                "module_name": "dnnc",
+                "cross_encoder_name": "cross-encoder/ms-marco-MiniLM-L-6-v2",  # Should be a list
+                "k": "wrong_type",  # Should be a list of integers
+                "train_head": "true",  # Should be a boolean, not a string
+            }
+        ],
+    }
 
     with pytest.raises(TypeError):
         NodeOptimizer(**invalid_config)

tests/modules/scoring/test_bert.py

@@ -51,7 +51,7 @@ def test_bert_scorer_dump_load(dataset):
 
     finally:
         # Clean up
-        shutil.rmtree(temp_dir_path, ignore_errors=True) # workaround for windows permission error
+        shutil.rmtree(temp_dir_path, ignore_errors=True)  # workaround for windows permission error
 
 
 def test_bert_prediction(dataset):