Update debug mode for relation prompt

model_zoo/uie/README.md

@@ -640,7 +640,7 @@ python finetune.py \
     --device gpu
 ```
 
-多卡启动：
+如果在GPU环境中使用，可以指定``gpus``参数进行多卡训练：
 
 ```shell
 python -u -m paddle.distributed.launch --gpus "0,1" finetune.py \
@@ -701,18 +701,24 @@ python evaluate.py \
 输出打印示例：
 
 ```text
-[2022-06-23 08:25:23,017] [    INFO] - -----------------------------
-[2022-06-23 08:25:23,017] [    INFO] - Class name: 时间
-[2022-06-23 08:25:23,018] [    INFO] - Evaluation precision: 1.00000 | recall: 1.00000 | F1: 1.00000
-[2022-06-23 08:25:23,145] [    INFO] - -----------------------------
-[2022-06-23 08:25:23,146] [    INFO] - Class name: 目的地
-[2022-06-23 08:25:23,146] [    INFO] - Evaluation precision: 0.64286 | recall: 0.90000 | F1: 0.75000
-[2022-06-23 08:25:23,272] [    INFO] - -----------------------------
-[2022-06-23 08:25:23,273] [    INFO] - Class name: 费用
-[2022-06-23 08:25:23,273] [    INFO] - Evaluation precision: 0.11111 | recall: 0.10000 | F1: 0.10526
-[2022-06-23 08:25:23,399] [    INFO] - -----------------------------
-[2022-06-23 08:25:23,399] [    INFO] - Class name: 出发地
-[2022-06-23 08:25:23,400] [    INFO] - Evaluation precision: 1.00000 | recall: 1.00000 | F1: 1.00000
+[2022-09-14 03:13:58,877] [    INFO] - -----------------------------
+[2022-09-14 03:13:58,877] [    INFO] - Class Name: 疾病
+[2022-09-14 03:13:58,877] [    INFO] - Evaluation Precision: 0.89744 | Recall: 0.83333 | F1: 0.86420
+[2022-09-14 03:13:59,145] [    INFO] - -----------------------------
+[2022-09-14 03:13:59,145] [    INFO] - Class Name: 手术治疗
+[2022-09-14 03:13:59,145] [    INFO] - Evaluation Precision: 0.90000 | Recall: 0.85714 | F1: 0.87805
+[2022-09-14 03:13:59,439] [    INFO] - -----------------------------
+[2022-09-14 03:13:59,440] [    INFO] - Class Name: 检查
+[2022-09-14 03:13:59,440] [    INFO] - Evaluation Precision: 0.77778 | Recall: 0.56757 | F1: 0.65625
+[2022-09-14 03:13:59,708] [    INFO] - -----------------------------
+[2022-09-14 03:13:59,709] [    INFO] - Class Name: X的手术治疗
+[2022-09-14 03:13:59,709] [    INFO] - Evaluation Precision: 0.90000 | Recall: 0.85714 | F1: 0.87805
+[2022-09-14 03:13:59,893] [    INFO] - -----------------------------
+[2022-09-14 03:13:59,893] [    INFO] - Class Name: X的实验室检查
+[2022-09-14 03:13:59,894] [    INFO] - Evaluation Precision: 0.71429 | Recall: 0.55556 | F1: 0.62500
+[2022-09-14 03:14:00,057] [    INFO] - -----------------------------
+[2022-09-14 03:14:00,058] [    INFO] - Class Name: X的影像学检查
+[2022-09-14 03:14:00,058] [    INFO] - Evaluation Precision: 0.69231 | Recall: 0.45000 | F1: 0.54545
 ```
 
 可配置参数说明：

model_zoo/uie/data_distill/README.md

@@ -146,13 +146,6 @@ python train.py \
           'text': '登革热'}]}]
 ```
 
-## 效果验证
-
-| 模型 |  Entity-F1  | SPO-F1 |
-| :---: | :--------: | :--------: |
-| UIE-Finetune | 78.57 | 56.25 |
-| GPLinker-ernie-3.0-mini-zh | 68.18 | 47.06 |
-| GPLinker-ernie-3.0-mini-zh + UIE数据蒸馏 | 76.38 | 50.42 |
 
 # References
 

model_zoo/uie/data_distill/data_distill.py

@@ -85,7 +85,7 @@ def do_data_distill():
         for text in tqdm(infer_texts, desc="Predicting: ", leave=False):
             infer_results.extend(uie(text))
 
-        train_synthetic_lines = synthetic2distill(texts, infer_results,
+        train_synthetic_lines = synthetic2distill(infer_texts, infer_results,
                                                   args.task_type)
 
         # Concat origin and synthetic data

model_zoo/uie/evaluate.py

@@ -23,7 +23,7 @@
 from paddlenlp.utils.log import logger
 
 from model import UIE
-from utils import convert_example, reader, unify_prompt_name
+from utils import convert_example, reader, unify_prompt_name, get_relation_type_dict, create_data_loader
 
 
 @paddle.no_grad()
@@ -60,28 +60,34 @@ def do_eval():
                            max_seq_len=args.max_seq_len,
                            lazy=False)
     class_dict = {}
+    relation_data = []
     if args.debug:
         for data in test_ds:
             class_name = unify_prompt_name(data['prompt'])
             # Only positive examples are evaluated in debug mode
             if len(data['result_list']) != 0:
-                class_dict.setdefault(class_name, []).append(data)
+                if "的" not in data['prompt']:
+                    class_dict.setdefault(class_name, []).append(data)
+                else:
+                    relation_data.append((data['prompt'], data))
+        relation_type_dict = get_relation_type_dict(relation_data)
     else:
         class_dict["all_classes"] = test_ds
+
+    trans_fn = partial(convert_example,
+                       tokenizer=tokenizer,
+                       max_seq_len=args.max_seq_len)
+
     for key in class_dict.keys():
         if args.debug:
             test_ds = MapDataset(class_dict[key])
         else:
             test_ds = class_dict[key]
-        test_ds = test_ds.map(
-            partial(convert_example,
-                    tokenizer=tokenizer,
-                    max_seq_len=args.max_seq_len))
-        test_batch_sampler = paddle.io.BatchSampler(dataset=test_ds,
-                                                    batch_size=args.batch_size,
-                                                    shuffle=False)
-        test_data_loader = paddle.io.DataLoader(
-            dataset=test_ds, batch_sampler=test_batch_sampler, return_list=True)
+
+        test_data_loader = create_data_loader(test_ds,
+                                              mode="test",
+                                              batch_size=args.batch_size,
+                                              trans_fn=trans_fn)
 
         metric = SpanEvaluator()
         precision, recall, f1 = evaluate(model, metric, test_data_loader)
@@ -90,6 +96,22 @@ def do_eval():
         logger.info("Evaluation Precision: %.5f | Recall: %.5f | F1: %.5f" %
                     (precision, recall, f1))
 
+    if args.debug and len(relation_type_dict.keys()) != 0:
+        for key in relation_type_dict.keys():
+            test_ds = MapDataset(relation_type_dict[key])
+
+            test_data_loader = create_data_loader(test_ds,
+                                                  mode="test",
+                                                  batch_size=args.batch_size,
+                                                  trans_fn=trans_fn)
+
+            metric = SpanEvaluator()
+            precision, recall, f1 = evaluate(model, metric, test_data_loader)
+            logger.info("-----------------------------")
+            logger.info("Class Name: X的%s" % key)
+            logger.info("Evaluation Precision: %.5f | Recall: %.5f | F1: %.5f" %
+                        (precision, recall, f1))
+
 
 if __name__ == "__main__":
     # yapf: disable

model_zoo/uie/finetune.py

@@ -26,7 +26,7 @@
 
 from model import UIE
 from evaluate import evaluate
-from utils import set_seed, convert_example, reader, MODEL_MAP
+from utils import set_seed, convert_example, reader, MODEL_MAP, create_data_loader
 
 
 def do_train():
@@ -57,28 +57,18 @@ def do_train():
                           max_seq_len=args.max_seq_len,
                           lazy=False)
 
-    train_ds = train_ds.map(
-        partial(convert_example,
-                tokenizer=tokenizer,
-                max_seq_len=args.max_seq_len))
-    dev_ds = dev_ds.map(
-        partial(convert_example,
-                tokenizer=tokenizer,
-                max_seq_len=args.max_seq_len))
-
-    train_batch_sampler = paddle.io.BatchSampler(dataset=train_ds,
-                                                 batch_size=args.batch_size,
-                                                 shuffle=True)
-    train_data_loader = paddle.io.DataLoader(dataset=train_ds,
-                                             batch_sampler=train_batch_sampler,
-                                             return_list=True)
-
-    dev_batch_sampler = paddle.io.BatchSampler(dataset=dev_ds,
-                                               batch_size=args.batch_size,
-                                               shuffle=False)
-    dev_data_loader = paddle.io.DataLoader(dataset=dev_ds,
-                                           batch_sampler=dev_batch_sampler,
-                                           return_list=True)
+    trans_fn = partial(convert_example,
+                       tokenizer=tokenizer,
+                       max_seq_len=args.max_seq_len)
+
+    train_data_loader = create_data_loader(train_ds,
+                                           mode="train",
+                                           batch_size=args.batch_size,
+                                           trans_fn=trans_fn)
+    dev_data_loader = create_data_loader(dev_ds,
+                                         mode="dev",
+                                         batch_size=args.batch_size,
+                                         trans_fn=trans_fn)
 
     if args.init_from_ckpt and os.path.isfile(args.init_from_ckpt):
         state_dict = paddle.load(args.init_from_ckpt)
@@ -95,7 +85,6 @@ def do_train():
 
     loss_list = []
     global_step = 0
-    best_step = 0
     best_f1 = 0
     tic_train = time.time()
     for epoch in range(1, args.num_epochs + 1):

model_zoo/uie/utils.py

@@ -118,6 +118,35 @@ def set_seed(seed):
     np.random.seed(seed)
 
 
+def create_data_loader(dataset, mode="train", batch_size=1, trans_fn=None):
+    """
+    Create dataloader.
+    Args:
+        dataset(obj:`paddle.io.Dataset`): Dataset instance.
+        mode(obj:`str`, optional, defaults to obj:`train`): If mode is 'train', it will shuffle the dataset randomly.
+        batch_size(obj:`int`, optional, defaults to 1): The sample number of a mini-batch.
+        trans_fn(obj:`callable`, optional, defaults to `None`): function to convert a data sample to input ids, etc.
+    Returns:
+        dataloader(obj:`paddle.io.DataLoader`): The dataloader which generates batches.
+    """
+    if trans_fn:
+        dataset = dataset.map(trans_fn)
+
+    shuffle = True if mode == 'train' else False
+    if mode == "train":
+        sampler = paddle.io.DistributedBatchSampler(dataset=dataset,
+                                                    batch_size=batch_size,
+                                                    shuffle=shuffle)
+    else:
+        sampler = paddle.io.BatchSampler(dataset=dataset,
+                                         batch_size=batch_size,
+                                         shuffle=shuffle)
+    dataloader = paddle.io.DataLoader(dataset,
+                                      batch_sampler=sampler,
+                                      return_list=True)
+    return dataloader
+
+
 def convert_example(example, tokenizer, max_seq_len):
     """
     example: {
@@ -267,6 +296,48 @@ def unify_prompt_name(prompt):
     return prompt
 
 
+def get_relation_type_dict(relation_data):
+
+    def compare(a, b):
+        a = a[::-1]
+        b = b[::-1]
+        res = ''
+        for i in range(min(len(a), len(b))):
+            if a[i] == b[i]:
+                res += a[i]
+            else:
+                break
+        if res == "":
+            return res
+        elif res[::-1][0] == "的":
+            return res[::-1][1:]
+        return ""
+
+    relation_type_dict = {}
+    added_list = []
+    for i in range(len(relation_data)):
+        added = False
+        if relation_data[i][0] not in added_list:
+            for j in range(i + 1, len(relation_data)):
+                match = compare(relation_data[i][0], relation_data[j][0])
+                if match != "":
+                    match = unify_prompt_name(match)
+                    if relation_data[i][0] not in added_list:
+                        added_list.append(relation_data[i][0])
+                        relation_type_dict.setdefault(match, []).append(
+                            relation_data[i][1])
+                    added_list.append(relation_data[j][0])
+                    relation_type_dict.setdefault(match, []).append(
+                        relation_data[j][1])
+                    added = True
+            if not added:
+                added_list.append(relation_data[i][0])
+                suffix = relation_data[i][0].rsplit("的", 1)[1]
+                suffix = unify_prompt_name(suffix)
+                relation_type_dict[suffix] = relation_data[i][1]
+    return relation_type_dict
+
+
 def add_entity_negative_example(examples, texts, prompts, label_set,
                                 negative_ratio):
     negative_examples = []
@@ -610,26 +681,31 @@ def _sep_cls_label(label, separator):
                     redundants1 = inverse_relation_list[i]
 
                     # 2. entity_name_set ^ subject_goldens[i]
-                    nonentity_list = list(
-                        set(entity_name_set) ^ set(subject_goldens[i]))
-                    nonentity_list.sort()
-
-                    redundants2 = [
-                        nonentity + "的" + predicate_list[i][random.randrange(
-                            len(predicate_list[i]))]
-                        for nonentity in nonentity_list
-                    ]
+                    redundants2 = []
+                    if len(predicate_list[i]) != 0:
+                        nonentity_list = list(
+                            set(entity_name_set) ^ set(subject_goldens[i]))
+                        nonentity_list.sort()
+
+                        redundants2 = [
+                            nonentity + "的" +
+                            predicate_list[i][random.randrange(
+                                len(predicate_list[i]))]
+                            for nonentity in nonentity_list
+                        ]
 
                     # 3. entity_label_set ^ entity_prompts[i]
-                    non_ent_label_list = list(
-                        set(entity_label_set) ^ set(entity_prompts[i]))
-                    non_ent_label_list.sort()
-
-                    redundants3 = [
-                        subject_goldens[i][random.randrange(
-                            len(subject_goldens[i]))] + "的" + non_ent_label
-                        for non_ent_label in non_ent_label_list
-                    ]
+                    redundants3 = []
+                    if len(subject_goldens[i]) != 0:
+                        non_ent_label_list = list(
+                            set(entity_label_set) ^ set(entity_prompts[i]))
+                        non_ent_label_list.sort()
+
+                        redundants3 = [
+                            subject_goldens[i][random.randrange(
+                                len(subject_goldens[i]))] + "的" + non_ent_label
+                            for non_ent_label in non_ent_label_list
+                        ]
 
                     redundants_list = [redundants1, redundants2, redundants3]