[utc] fix loading local model in taskflow

applications/zero_shot_text_classification/README.md

@@ -114,7 +114,8 @@ python run_train.py  \
     --disable_tqdm True \
     --metric_for_best_model macro_f1 \
     --load_best_model_at_end  True \
-    --save_total_limit 1
+    --save_total_limit 1 \
+    --save_plm
 ```
 
 如果在GPU环境中使用，可以指定gpus参数进行多卡训练：
@@ -143,7 +144,8 @@ python -u -m paddle.distributed.launch --gpus "0,1" run_train.py \
     --disable_tqdm True \
     --metric_for_best_model macro_f1 \
     --load_best_model_at_end  True \
-    --save_total_limit 1
+    --save_total_limit 1 \
+    --save_plm
 ```
 
 该示例代码中由于设置了参数 `--do_eval`，因此在训练完会自动进行评估。
@@ -204,7 +206,7 @@ python run_eval.py \
 >>> from pprint import pprint
 >>> from paddlenlp import Taskflow
 >>> schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"]
->>> my_cls = Taskflow("zero_shot_text_classification", schema=schema, task_path='./checkpoint/model_best', precision="fp16")
+>>> my_cls = Taskflow("zero_shot_text_classification", schema=schema, task_path='./checkpoint/model_best/plm', precision="fp16")
 >>> pprint(my_cls("中性粒细胞比率偏低"))
 ```
 
@@ -221,7 +223,7 @@ from paddlenlp import SimpleServer, Taskflow
 schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议"]
 utc = Taskflow("zero_shot_text_classification",
                schema=schema,
-               task_path="../../checkpoint/model_best/",
+               task_path="../../checkpoint/model_best/plm",
                precision="fp32")
 app = SimpleServer()
 app.register_taskflow("taskflow/utc", utc)

applications/zero_shot_text_classification/deploy/simple_serving/README.md

@@ -41,15 +41,15 @@ schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就
 
 ```python
 # Default task_path
-utc = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/", schema=schema)
+utc = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/plm", schema=schema)
 ```
 
 #### 多卡服务化预测
 PaddleNLP SimpleServing 支持多卡负载均衡预测，主要在服务化注册的时候，注册两个Taskflow的task即可，下面是示例代码
 
 ```python
-utc1 = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/", schema=schema)
-utc2 = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/", schema=schema)
+utc1 = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/plm", schema=schema)
+utc2 = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/plm", schema=schema)
 service.register_taskflow("taskflow/utc", [utc1, utc2])
 ```
 

applications/zero_shot_text_classification/deploy/simple_serving/server.py

@@ -17,7 +17,7 @@
 # The schema changed to your defined schema
 schema = ["病情诊断", "治疗方案", "病因分析", "指标解读", "就医建议", "疾病表述", "后果表述", "注意事项", "功效作用", "医疗费用", "其他"]
 # The task path changed to your best model path
-utc = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/", schema=schema)
+utc = Taskflow("zero_shot_text_classification", task_path="../../checkpoint/model_best/plm", schema=schema)
 # If you want to define the finetuned utc service
 app = SimpleServer()
 app.register_taskflow("taskflow/utc", utc)

paddlenlp/taskflow/zero_shot_text_classification.py

@@ -54,6 +54,30 @@ class ZeroShotTextClassificationTask(Task):
         "special_tokens_map": "special_tokens_map.json",
         "tokenizer_config": "tokenizer_config.json",
     }
+    resource_files_urls = {
+        "utc-large": {
+            "model_state": [
+                "https://bj.bcebos.com/paddlenlp/taskflow/zero_shot_text_classification/utc-large/model_state.pdparams",
+                "71eb9a732c743a513b84ca048dc4945b",
+            ],
+            "config": [
+                "https://bj.bcebos.com/paddlenlp/taskflow/zero_shot_text_classification/utc-large/config.json",
+                "9496be2cc99f7e6adf29280320274142",
+            ],
+            "vocab_file": [
+                "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/vocab.txt",
+                "afc01b5680a53525df5afd7518b42b48",
+            ],
+            "special_tokens_map": [
+                "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/special_tokens_map.json",
+                "2458e2131219fc1f84a6e4843ae07008",
+            ],
+            "tokenizer_config": [
+                "https://bj.bcebos.com/paddlenlp/taskflow/zero_text_classification/utc-large/tokenizer_config.json",
+                "dcb0f3257830c0eb1f2de47f2d86f89a",
+            ],
+        },
+    }
 
     def __init__(self, task: str, model: str = "utc-large", schema: list = None, **kwargs):
         super().__init__(task=task, model=model, **kwargs)
@@ -64,6 +88,7 @@ def __init__(self, task: str, model: str = "utc-large", schema: list = None, **k
         self._pred_threshold = kwargs.get("pred_threshold", 0.5)
         self._num_workers = kwargs.get("num_workers", 0)
 
+        self._check_task_files()
         self._construct_tokenizer()
         self._check_predictor_type()
         self._get_inference_model()
@@ -102,10 +127,7 @@ def _construct_model(self, model):
         """
         Construct the inference model for the predictor.
         """
-        if self.from_hf_hub:
-            model_instance = UTC.from_pretrained(self._task_path, from_hf_hub=self.from_hf_hub)
-        else:
-            model_instance = UTC.from_pretrained(model)
+        model_instance = UTC.from_pretrained(self._task_path, from_hf_hub=self.from_hf_hub)
         self._model = model_instance
         self._model.eval()