fix

Author: lugimzzz

paddlenlp/experimental/autonlp/text_classification.py

@@ -135,7 +135,7 @@ def _model_candidates(self) -> List[Dict[str, Any]]:
         chinese_finetune_models = hp.choice(
             "finetune_models",
             [
-                "ernie-1.0-large-zh-cw"  # 24-layer, 1024-hidden, 16-heads, 272M parameters.
+                "ernie-1.0-large-zh-cw",  # 24-layer, 1024-hidden, 16-heads, 272M parameters.
                 "ernie-3.0-xbase-zh",  # 20-layer, 1024-hidden, 16-heads, 296M parameters.
                 "ernie-3.0-tiny-base-v2-zh",  # 12-layer, 768-hidden, 12-heads, 118M parameters.
                 "ernie-3.0-tiny-medium-v2-zh",  # 6-layer, 768-hidden, 12-heads, 75M parameters.
@@ -152,7 +152,6 @@ def _model_candidates(self) -> List[Dict[str, Any]]:
                 "roberta-large",  # 24-layer, 1024-hidden, 16-heads, 334M parameters. Case-sensitive
                 "roberta-base",  # 12-layer, 768-hidden, 12-heads, 110M parameters. Case-sensitive
                 "distilroberta-base",  # 6-layer, 768-hidden, 12-heads, 66M parameters. Case-sensitive
-                "ernie-3.0-tiny-mini-v2-en",  # 6-layer, 384-hidden, 12-heads, 27M parameters
                 "ernie-2.0-base-en",  # 12-layer, 768-hidden, 12-heads, 103M parameters. Trained on lower-cased English text.
                 "ernie-2.0-large-en",  # 24-layer, 1024-hidden, 16-heads, 336M parameters. Trained on lower-cased English text.
             ],
@@ -523,7 +522,9 @@ def export(self, export_path, trial_id=None):
             trainer.export_model(export_path)
             trainer.model.plm.save_pretrained(os.path.join(export_path, "plm"))
             mode = "prompt"
-            max_length = model_config.get("PreprocessArguments.max_length", 128)
+            max_length = model_config.get(
+                "PreprocessArguments.max_length", trainer.model.plm.config.max_position_embeddings
+            )
         else:
             if trainer.model.init_config["init_class"] in ["ErnieMForSequenceClassification"]:
                 input_spec = [paddle.static.InputSpec(shape=[None, None], dtype="int64", name="input_ids")]
@@ -534,7 +535,9 @@ def export(self, export_path, trial_id=None):
                 ]
             export_model(model=trainer.model, input_spec=input_spec, path=export_path)
             mode = "finetune"
-            max_length = trainer.model.config.max_position_embeddings
+            max_length = model_config.get(
+                "PreprocessArguments.max_length", trainer.model.config.max_position_embeddings
+            )
 
         # save tokenizer
         trainer.tokenizer.save_pretrained(export_path)
@@ -553,7 +556,7 @@ def export(self, export_path, trial_id=None):
         with open(os.path.join(export_path, "taskflow_config.json"), "w", encoding="utf-8") as f:
             json.dump(taskflow_config, f, ensure_ascii=False)
         logger.info(
-            f"taskflow config saved to {export_path}. You can use the taskflow config to create a Taskflow instance for inference"
+            f"Taskflow config saved to {export_path}. You can use the Taskflow config to create a Taskflow instance for inference"
         )
 
         if os.path.exists(self.training_path):

tests/experimental/autonlp/test_text_classification.py

@@ -263,11 +263,18 @@ def test_multilabel(self, custom_model_candidate, hp_overrides):
             # test training_path
             self.assertFalse(os.path.exists(os.path.join(auto_trainer.training_path)))
 
-    @slow
     @parameterized.expand(
         [
             (
-                None,
+                "Chinese",
+                {
+                    "TrainingArguments.max_steps": 2,
+                    "TrainingArguments.per_device_train_batch_size": 1,
+                    "TrainingArguments.per_device_eval_batch_size": 1,
+                },
+            ),
+            (
+                "English",
                 {
                     "TrainingArguments.max_steps": 2,
                     "TrainingArguments.per_device_train_batch_size": 1,
@@ -276,7 +283,8 @@ def test_multilabel(self, custom_model_candidate, hp_overrides):
             ),
         ]
     )
-    def test_default_model_candidate(self, custom_model_candidate, hp_overrides):
+    @slow
+    def test_default_model_candidate(self, language, hp_overrides):
         with TemporaryDirectory() as temp_dir_path:
             train_ds = copy.deepcopy(self.multi_class_train_ds)
             dev_ds = copy.deepcopy(self.multi_class_dev_ds)
@@ -287,7 +295,7 @@ def test_default_model_candidate(self, custom_model_candidate, hp_overrides):
                 eval_dataset=dev_ds,
                 label_column="label_desc",
                 text_column="sentence",
-                language="Chinese",
+                language=language,
                 output_dir=temp_dir_path,
                 problem_type="multi_class",
             )
@@ -296,7 +304,6 @@ def test_default_model_candidate(self, custom_model_candidate, hp_overrides):
                 num_gpus=1,
                 max_concurrent_trials=1,
                 num_models=num_models,
-                custom_model_candidates=custom_model_candidate,
                 hp_overrides=hp_overrides,
             )