[few-shot] Update README (#3104)

* [few-shot] add input_spec in train.py

* [few-shot] add early-stopping

* [few-shot] update readme

* [prompt] fix optimization problem in soft verbalizer

Author: LemonNoel

applications/text_classification/hierarchical/few-shot/README.md

@@ -18,9 +18,10 @@
 
 import paddle
 import paddle.nn.functional as F
+from paddle.static import InputSpec
 from paddlenlp.utils.log import logger
 from paddlenlp.transformers import AutoTokenizer, AutoModelForMaskedLM
-from paddlenlp.trainer import PdArgumentParser
+from paddlenlp.trainer import PdArgumentParser, EarlyStoppingCallback
 from paddlenlp.prompt import (
     AutoTemplate,
     SoftVerbalizer,
@@ -106,13 +107,20 @@ def compute_metrics(eval_preds):
             "macro_f1_score": macro_f1_score
         }
 
+    # Deine the early-stopping callback.
+    callbacks = [
+        EarlyStoppingCallback(early_stopping_patience=4,
+                              early_stopping_threshold=0.)
+    ]
+
     # Initialize the trainer.
     trainer = PromptTrainer(model=prompt_model,
                             tokenizer=tokenizer,
                             args=training_args,
                             criterion=criterion,
                             train_dataset=train_ds,
                             eval_dataset=dev_ds,
+                            callbacks=callbacks,
                             compute_metrics=compute_metrics)
 
     # Training.
@@ -131,8 +139,15 @@ def compute_metrics(eval_preds):
 
     # Export static model.
     if training_args.do_export:
+        input_spec = [
+            InputSpec(shape=[None, None], dtype="int64"),  # input_ids
+            InputSpec(shape=[None, None], dtype="int64"),  # mask_ids
+            InputSpec(shape=[None, None], dtype="int64"),  # soft_token_ids
+        ]
         export_path = os.path.join(training_args.output_dir, 'export')
-        trainer.export_model(export_path, export_type=model_args.export_type)
+        trainer.export_model(export_path,
+                             input_spec=input_sepc,
+                             export_type=model_args.export_type)
 
 
 if __name__ == '__main__':

applications/text_classification/hierarchical/few-shot/train.py

@@ -16,10 +16,11 @@
 import os
 
 import paddle
+from paddle.static import InputSpec
 from paddle.metric import Accuracy
 from paddlenlp.utils.log import logger
 from paddlenlp.transformers import AutoTokenizer, AutoModelForMaskedLM
-from paddlenlp.trainer import PdArgumentParser
+from paddlenlp.trainer import PdArgumentParser, EarlyStoppingCallback
 from paddlenlp.prompt import (
     AutoTemplate,
     SoftVerbalizer,
@@ -100,13 +101,20 @@ def compute_metrics(eval_preds):
         acc = metric.accumulate()
         return {'accuracy': acc}
 
+    # Deine the early-stopping callback.
+    callbacks = [
+        EarlyStoppingCallback(early_stopping_patience=4,
+                              early_stopping_threshold=0.)
+    ]
+
     # Initialize the trainer.
     trainer = PromptTrainer(model=prompt_model,
                             tokenizer=tokenizer,
                             args=training_args,
                             criterion=criterion,
                             train_dataset=train_ds,
                             eval_dataset=dev_ds,
+                            callbacks=callbacks,
                             compute_metrics=compute_metrics)
 
     # Traininig.
@@ -125,8 +133,15 @@ def compute_metrics(eval_preds):
 
     # Export static model.
     if training_args.do_export:
+        input_spec = [
+            InputSpec(shape=[None, None], dtype="int64"),  # input_ids
+            InputSpec(shape=[None, None], dtype="int64"),  # mask_ids
+            InputSpec(shape=[None, None], dtype="int64"),  # soft_token_ids
+        ]
         export_path = os.path.join(training_args.output_dir, 'export')
-        trainer.export_model(export_path, export_type=model_args.export_type)
+        trainer.export_model(export_path,
+                             input_spec=input_spec,
+                             export_type=model_args.export_type)
 
 
 if __name__ == '__main__':