add peoplesner, fix msraner (#5183)

add peoplesner, fix msraner (#5183)

PaddleNLP/examples/named_entity_recognition/msra_ner/README.md

@@ -5,11 +5,11 @@
 MSRA-NER 数据集由微软亚研院发布，其目标是识别文本中具有特定意义的实体，主要包括人名、地名、机构名等。示例如下：
 
 ```
-海钓比赛地点在厦门与金门之间的海域。    OOOOOOOB-LOCI-LOCOB-LOCI-LOCOOOOOO
-这座依山傍水的博物馆由国内一流的设计师主持设计，整个建筑群精美而恢宏。    OOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOOO
+不\002久\002前\002，\002中\002国\002共\002产\002党\002召\002开\002了\002举\002世\002瞩\002目\002的\002第\002十\002五\002次\002全\002国\002代\002表\002大\002会\002。    O\002O\002O\002O\002B-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002O\002O\002O\002O\002O\002O\002O\002O\002B-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002I-ORG\002O
+这\002次\002代\002表\002大\002会\002是\002在\002中\002国\002改\002革\002开\002放\002和\002社\002会\002主\002义\002现\002代\002化\002建\002设\002发\002展\002的\002关\002键\002时\002刻\002召\002开\002的\002历\002史\002性\002会\002议\002。    O\002O\002O\002O\002O\002O\002O\002O\002B-LOC\002I-LOC\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O\002O
 ```
 
-数据集中以特殊字符"\t"分隔文本、标签，以特殊字符"\002"分隔每个字。
+PaddleNLP集成的数据集MSRA-NER数据集对文件格式做了调整：每一行文本、标签以特殊字符"\t"进行分隔，每个字之间以特殊字符"\002"分隔。
 
 ## 2. 快速开始
 
@@ -52,22 +52,21 @@ python -u ./run_msra_ner.py \
 训练过程将按照 `logging_steps` 和 `save_steps` 的设置打印如下日志：
 
 ```
-global step 1496, epoch: 2, batch: 192, loss: 0.010747, speed: 4.77 step/s
-global step 1497, epoch: 2, batch: 193, loss: 0.004837, speed: 4.46 step/s
-global step 1498, epoch: 2, batch: 194, loss: 0.011281, speed: 4.24 step/s
-global step 1499, epoch: 2, batch: 195, loss: 0.005711, speed: 4.73 step/s
-global step 1500, epoch: 2, batch: 196, loss: 0.003150, speed: 4.52 step/s
-eval loss: 0.010307, precision: 0.884222, recall: 0.903190, f1: 0.893605
+global step 3996, epoch: 2, batch: 1184, loss: 0.008593, speed: 4.15 step/s
+global step 3997, epoch: 2, batch: 1185, loss: 0.008453, speed: 4.17 step/s
+global step 3998, epoch: 2, batch: 1186, loss: 0.002294, speed: 4.19 step/s
+global step 3999, epoch: 2, batch: 1187, loss: 0.005351, speed: 4.16 step/s
+global step 4000, epoch: 2, batch: 1188, loss: 0.004734, speed: 4.18 step/s
+eval loss: 0.006829, precision: 0.908957, recall: 0.926683, f1: 0.917734
 ```
 
 使用以上命令进行单卡 Fine-tuning ，在验证集上有如下结果：
  Metric                       | Result      |
 ------------------------------|-------------|
-precision                     | 0.884222    |
-recall                        | 0.903190    |
-f1                            | 0.893605    |
+precision                     | 0.908957    |
+recall                        | 0.926683    |
+f1                            | 0.917734    |
 
 ## 参考
 
-[Microsoft Research Asia Chinese Word-Segmentation Data Set](https://www.microsoft.com/en-us/download/details.aspx?id=52531)
 [The third international Chinese language processing bakeoff: Word segmentation and named entity recognition](https://faculty.washington.edu/levow/papers/sighan06.pdf)

PaddleNLP/examples/named_entity_recognition/msra_ner/run_msra_ner.py

@@ -245,8 +245,8 @@ def do_train(args):
     if paddle.distributed.get_world_size() > 1:
         paddle.distributed.init_parallel_env()
 
-    train_dataset, dev_dataset = ppnlp.datasets.MSRA_NER.get_datasets(
-        ["train", "dev"])
+    train_dataset, test_dataset = ppnlp.datasets.MSRA_NER.get_datasets(
+        ["train", "test"])
     tokenizer = BertTokenizer.from_pretrained(args.model_name_or_path)
 
     label_list = train_dataset.get_labels()
@@ -276,11 +276,11 @@ def do_train(args):
         num_workers=0,
         return_list=True)
 
-    dev_dataset = dev_dataset.apply(trans_func, lazy=True)
+    test_dataset = test_dataset.apply(trans_func, lazy=True)
     dev_batch_sampler = paddle.io.BatchSampler(
-        dev_dataset, batch_size=args.batch_size, shuffle=False, drop_last=True)
-    dev_data_loader = DataLoader(
-        dataset=dev_dataset,
+        test_dataset, batch_size=args.batch_size, shuffle=False, drop_last=True)
+    test_data_loader = DataLoader(
+        dataset=test_dataset,
         batch_sampler=dev_batch_sampler,
         collate_fn=batchify_fn,
         num_workers=0,
@@ -336,7 +336,7 @@ def do_train(args):
             lr_scheduler.step()
             optimizer.clear_gradients()
             if global_step % args.save_steps == 0:
-                evaluate(model, loss_fct, metric, dev_data_loader, label_num)
+                evaluate(model, loss_fct, metric, test_data_loader, label_num)
                 if (not args.n_gpu > 1) or paddle.distributed.get_rank() == 0:
                     paddle.save(model.state_dict(),
                                 os.path.join(args.output_dir,

PaddleNLP/paddlenlp/datasets/__init__.py

@@ -17,6 +17,7 @@ from .dataset import *
 from .glue import *
 from .lcqmc import *
 from .msra_ner import *
+from .peoples_daily_ner import *
 from .ptb import *
 from .squad import *
 from .translation import *

PaddleNLP/paddlenlp/datasets/msra_ner.py

@@ -27,7 +27,7 @@ __all__ = ['MSRA_NER']
 
 
 class MSRA_NER(TSVDataset):
-    URL = "https://bj.bcebos.com/paddlehub-dataset/msra_ner.tar.gz"
+    URL = "https://paddlenlp.bj.bcebos.com/datasets/msra_ner.tar.gz"
     MD5 = None
     META_INFO = collections.namedtuple(
         'META_INFO', ('file', 'md5', 'field_indices', 'num_discard_samples'))
@@ -37,11 +37,6 @@ class MSRA_NER(TSVDataset):
             '67d3c93a37daba60ef43c03271f119d7',
             (0, 1),
             1, ),
-        'dev': META_INFO(
-            os.path.join('msra_ner', 'dev.tsv'),
-            'ec772f3ba914bca5269f6e785bb3375d',
-            (0, 1),
-            1, ),
         'test': META_INFO(
             os.path.join('msra_ner', 'test.tsv'),
             '2f27ae68b5f61d6553ffa28bb577c8a7',

PaddleNLP/paddlenlp/datasets/peoples_daily_ner.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import collections
+import os
+import warnings
+
+from paddle.io import Dataset
+from paddle.dataset.common import md5file
+from paddle.utils.download import get_path_from_url
+from paddlenlp.utils.env import DATA_HOME
+
+from .dataset import TSVDataset
+
+__all__ = ['PeoplesDailyNER']
+
+
+class PeoplesDailyNER(TSVDataset):
+    URL = "https://paddlenlp.bj.bcebos.com/datasets/peoples_daily_ner.tar.gz"
+    MD5 = None
+    META_INFO = collections.namedtuple(
+        'META_INFO', ('file', 'md5', 'field_indices', 'num_discard_samples'))
+    SPLITS = {
+        'train': META_INFO(
+            os.path.join('peoples_daily_ner', 'train.tsv'),
+            '67d3c93a37daba60ef43c03271f119d7',
+            (0, 1),
+            1, ),
+        'dev': META_INFO(
+            os.path.join('peoples_daily_ner', 'dev.tsv'),
+            'ec772f3ba914bca5269f6e785bb3375d',
+            (0, 1),
+            1, ),
+        'test': META_INFO(
+            os.path.join('peoples_daily_ner', 'test.tsv'),
+            '2f27ae68b5f61d6553ffa28bb577c8a7',
+            (0, 1),
+            1, ),
+    }
+
+    def __init__(self, mode='train', root=None, **kwargs):
+        default_root = os.path.join(DATA_HOME, 'peoples_daily_ner')
+        filename, data_hash, field_indices, num_discard_samples = self.SPLITS[
+            mode]
+        fullname = os.path.join(default_root,
+                                filename) if root is None else os.path.join(
+                                    os.path.expanduser(root), filename)
+        if not os.path.exists(fullname) or (data_hash and
+                                            not md5file(fullname) == data_hash):
+            if root is not None:  # not specified, and no need to warn
+                warnings.warn(
+                    'md5 check failed for {}, download {} data to {}'.format(
+                        filename, self.__class__.__name__, default_root))
+            path = get_path_from_url(self.URL, default_root, self.MD5)
+            fullname = os.path.join(default_root, filename)
+        super(PeoplesDailyNER, self).__init__(
+            fullname,
+            field_indices=field_indices,
+            num_discard_samples=num_discard_samples,
+            **kwargs)
+
+    def get_labels(self):
+        """
+        Return labels of the GlueCoLA object.
+        """
+        return ["B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC", "O"]