!11723 bert ner for msra dataset

From: @shibeiji
Reviewed-by: @guoqi1024,@c_34,@linqingke
Signed-off-by:

model_zoo/official/nlp/bert/README.md

@@ -27,6 +27,7 @@
         - [Evaluation](#evaluation)
             - [evaluation on cola dataset when running on Ascend](#evaluation-on-cola-dataset-when-running-on-ascend)
             - [evaluation on cluener dataset when running on Ascend](#evaluation-on-cluener-dataset-when-running-on-ascend)
+            - [evaluation on msra dataset when running on Ascend](#evaluation-on-msra-dataset-when-running-on-ascend)
             - [evaluation on squad v1.1 dataset when running on Ascend](#evaluation-on-squad-v11-dataset-when-running-on-ascend)
     - [Model Description](#model-description)
     - [Performance](#performance)
@@ -215,7 +216,7 @@ For example, the schema file of cn-wiki-128 dataset for pretraining shows as fol
     ├─bert_for_finetune.py                    # backbone code of network
     ├─bert_for_pre_training.py                # backbone code of network
     ├─bert_model.py                           # backbone code of network
-    ├─clue_classification_dataset_precess.py  # data preprocessing
+    ├─finetune_data_preprocess.py             # data preprocessing
     ├─cluner_evaluation.py                    # evaluation for cluner
     ├─config.py                               # parameter configuration for pretraining
     ├─CRF.py                                  # assessment method for clue dataset
@@ -301,6 +302,7 @@ options:
     --load_finetune_checkpoint_path   give a finetuning checkpoint path if only do eval
     --train_data_file_path            ner tfrecord for training. E.g., train.tfrecord
     --eval_data_file_path             ner tfrecord for predictions if f1 is used to evaluate result, ner json for predictions if clue_benchmark is used to evaluate result
+    --dataset_format                  dataset format, support mindrecord or tfrecord
     --schema_file_path                path to datafile schema file
 
 usage: run_squad.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
@@ -544,6 +546,30 @@ Recall 0.948683
 F1 0.920507
 ```
 
+#### evaluation on msra dataset when running on Ascend
+
+For preprocess, you can first convert the original txt format of MSRA dataset into mindrecord by run the command as below:
+
+```python
+python src/finetune_data_preprocess.py ----data_dir=/path/msra_dataset.txt --vocab_file=/path/vacab_file --save_path=/path/msra_dataset.mindrecord --label2id=/path/label2id_file --max_seq_len=seq_len
+```
+
+For finetune and evaluation, just do
+
+```bash
+bash scripts/ner.sh
+```
+
+The command above will run in the background, you can view training logs in ner_log.txt.
+
+If you choose SpanF1 as assessment method and mode use_crf is set to be "true", the result will be as follows if evaluation is done after finetuning 10 epoches:
+
+```text
+Precision 0.953826
+Recall 0.957749
+F1 0.955784
+```
+
 #### evaluation on squad v1.1 dataset when running on Ascend
 
 ```bash

model_zoo/official/nlp/bert/README_CN.md

@@ -28,6 +28,7 @@
         - [用法](#用法-1)
             - [Ascend处理器上运行后评估cola数据集](#ascend处理器上运行后评估cola数据集)
             - [Ascend处理器上运行后评估cluener数据集](#ascend处理器上运行后评估cluener数据集)
+            - [Ascend处理器上运行后评估msra数据集](#ascend处理器上运行后评估msra数据集)
             - [Ascend处理器上运行后评估squad v1.1数据集](#ascend处理器上运行后评估squad-v11数据集)
     - [模型描述](#模型描述)
     - [性能](#性能)
@@ -215,7 +216,7 @@ For example, the schema file of cn-wiki-128 dataset for pretraining shows as fol
     ├─bert_for_finetune.py                    # 网络骨干编码
     ├─bert_for_pre_training.py                # 网络骨干编码
     ├─bert_model.py                           # 网络骨干编码
-    ├─clue_classification_dataset_precess.py  # 数据预处理
+    ├─finetune_data_preprocess.py             # 数据预处理
     ├─cluner_evaluation.py                    # 评估线索生成工具
     ├─config.py                               # 预训练参数配置
     ├─CRF.py                                  # 线索数据集评估方法
@@ -299,6 +300,7 @@ For example, the schema file of cn-wiki-128 dataset for pretraining shows as fol
     --load_finetune_checkpoint_path   如仅执行评估，提供微调检查点保存路径
     --train_data_file_path            用于保存训练数据的TFRecord文件，如train.tfrecord文件
     --eval_data_file_path             如采用f1来评估结果，则为TFRecord文件保存预测；如采用clue_benchmark来评估结果，则为JSON文件保存预测
+    --dataset_format                  数据集格式，支持tfrecord和mindrecord格式
     --schema_file_path                模式文件保存路径
 
 用法：run_squad.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
@@ -508,6 +510,29 @@ Recall 0.948683
 F1 0.920507
 ```
 
+#### Ascend处理器上运行后评估msra数据集
+
+您可以采用如下方式，先将MSRA数据集的原始格式在预处理流程中转换为mindrecord格式以提升性能：
+
+```python
+python src/finetune_data_preprocess.py ----data_dir=/path/msra_dataset.txt --vocab_file=/path/vacab_file --save_path=/path/msra_dataset.mindrecord --label2id=/path/label2id_file --max_seq_len=seq_len
+```
+
+此后，您可以进行微调再训练和推理流程，
+
+```bash
+bash scripts/ner.sh
+```
+
+以上命令后台运行，您可以在ner_log.txt中查看训练日志。
+如您选择SpanF1作为评估方法并且模型结构中配置CRF模式，在微调训练10个epoch之后进行推理，可得到如下结果：
+
+```text
+Precision 0.953826
+Recall 0.957749
+F1 0.955784
+```
+
 #### Ascend处理器上运行后评估squad v1.1数据集
 
 ```bash

model_zoo/official/nlp/bert/run_ner.py

@@ -23,7 +23,7 @@ from src.bert_for_finetune import BertFinetuneCell, BertNER
 from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
 from src.dataset import create_ner_dataset
 from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate, convert_labels_to_index
-from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
+from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation, SpanF1
 import mindspore.common.dtype as mstype
 from mindspore import context
 from mindspore import log as logger
@@ -86,7 +86,7 @@ def eval_result_print(assessment_method="accuracy", callback=None):
     if assessment_method == "accuracy":
         print("acc_num {} , total_num {}, accuracy {:.6f}".format(callback.acc_num, callback.total_num,
                                                                   callback.acc_num / callback.total_num))
-    elif assessment_method == "f1":
+    elif assessment_method in ("f1", "spanf1"):
         print("Precision {:.6f} ".format(callback.TP / (callback.TP + callback.FP)))
         print("Recall {:.6f} ".format(callback.TP / (callback.TP + callback.FN)))
         print("F1 {:.6f} ".format(2 * callback.TP / (2 * callback.TP + callback.FP + callback.FN)))
@@ -118,6 +118,8 @@ def do_eval(dataset=None, network=None, use_crf="", num_class=41, assessment_met
             callback = Accuracy()
         elif assessment_method == "f1":
             callback = F1((use_crf.lower() == "true"), num_class)
+        elif assessment_method == "spanf1":
+            callback = SpanF1((use_crf.lower() == "true"), tag_to_index)
         elif assessment_method == "mcc":
             callback = MCC()
         elif assessment_method == "spearman_correlation":
@@ -143,8 +145,8 @@ def parse_args():
     parser = argparse.ArgumentParser(description="run ner")
     parser.add_argument("--device_target", type=str, default="Ascend", choices=["Ascend", "GPU"],
                         help="Device type, default is Ascend")
-    parser.add_argument("--assessment_method", type=str, default="F1", choices=["F1", "clue_benchmark"],
-                        help="assessment_method include: [F1, clue_benchmark], default is F1")
+    parser.add_argument("--assessment_method", type=str, default="F1", choices=["F1", "clue_benchmark", "SpanF1"],
+                        help="assessment_method include: [F1, clue_benchmark, SpanF1], default is F1")
     parser.add_argument("--do_train", type=str, default="false", choices=["true", "false"],
                         help="Eable train, default is false")
     parser.add_argument("--do_eval", type=str, default="false", choices=["true", "false"],
@@ -169,6 +171,8 @@ def parse_args():
                         help="Data path, it is better to use absolute path")
     parser.add_argument("--eval_data_file_path", type=str, default="",
                         help="Data path, it is better to use absolute path")
+    parser.add_argument("--dataset_format", type=str, default="mindrecord", choices=["mindrecord", "tfrecord"],
+                        help="Dataset format, support mindrecord or tfrecord")
     parser.add_argument("--schema_file_path", type=str, default="",
                         help="Schema path, it is better to use absolute path")
     args_opt = parser.parse_args()
@@ -225,7 +229,7 @@ def run_ner():
                               tag_to_index=tag_to_index, dropout_prob=0.1)
         ds = create_ner_dataset(batch_size=args_opt.train_batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.train_data_file_path,
-                                schema_file_path=args_opt.schema_file_path,
+                                schema_file_path=args_opt.schema_file_path, dataset_format=args_opt.dataset_format,
                                 do_shuffle=(args_opt.train_data_shuffle.lower() == "true"))
         do_train(ds, netwithloss, load_pretrain_checkpoint_path, save_finetune_checkpoint_path, epoch_num)
 
@@ -240,7 +244,7 @@ def run_ner():
     if args_opt.do_eval.lower() == "true":
         ds = create_ner_dataset(batch_size=args_opt.eval_batch_size, repeat_count=1,
                                 assessment_method=assessment_method, data_file_path=args_opt.eval_data_file_path,
-                                schema_file_path=args_opt.schema_file_path,
+                                schema_file_path=args_opt.schema_file_path, dataset_format=args_opt.dataset_format,
                                 do_shuffle=(args_opt.eval_data_shuffle.lower() == "true"))
         do_eval(ds, BertNER, args_opt.use_crf, number_labels, assessment_method,
                 args_opt.eval_data_file_path, load_finetune_checkpoint_path, args_opt.vocab_file_path,

model_zoo/official/nlp/bert/scripts/run_ner.sh

@@ -18,7 +18,7 @@ echo "==========================================================================
 echo "Please run the script as: "
 echo "bash scripts/run_ner.sh"
 echo "for example: bash scripts/run_ner.sh"
-echo "assessment_method include: [F1, clue_benchmark]"
+echo "assessment_method include: [F1, SpanF1, clue_benchmark]"
 echo "=============================================================================================================="
 
 mkdir -p ms_log
@@ -46,4 +46,5 @@ python ${PROJECT_DIR}/../run_ner.py  \
     --load_finetune_checkpoint_path="" \
     --train_data_file_path="" \
     --eval_data_file_path="" \
+    --dataset_format="tfrecord" \
     --schema_file_path="" > ner_log.txt 2>&1 &

model_zoo/official/nlp/bert/src/assessment_method.py

@@ -68,6 +68,74 @@ class F1():
         self.FP += np.sum(pos_eva&(~pos_label))
         self.FN += np.sum((~pos_eva)&pos_label)
 
+
+class SpanF1():
+    '''
+    calculate F1、precision and recall score in span manner for NER
+    '''
+    def __init__(self, use_crf=False, label2id=None):
+        self.TP = 0
+        self.FP = 0
+        self.FN = 0
+        self.use_crf = use_crf
+        self.label2id = label2id
+        if label2id is None:
+            raise ValueError("label2id info should not be empty")
+        self.id2label = {}
+        for key, value in label2id.items():
+            self.id2label[value] = key
+
+    def tag2span(self, ids):
+        '''
+        conbert ids list to span mode
+        '''
+        labels = np.array([self.id2label[id] for id in ids])
+        spans = []
+        prev_label = None
+        for idx, tag in enumerate(labels):
+            tag = tag.lower()
+            cur_label, label = tag[:1], tag[2:]
+            if cur_label in ('b', 's'):
+                spans.append((label, [idx, idx]))
+            elif cur_label in ('m', 'e') and prev_label in ('b', 'm') and label == spans[-1][0]:
+                spans[-1][1][1] = idx
+            elif cur_label == 'o':
+                pass
+            else:
+                spans.append((label, [idx, idx]))
+            prev_label = cur_label
+        return [(span[0], (span[1][0], span[1][1] + 1)) for span in spans]
+
+
+    def update(self, logits, labels):
+        '''
+        update span F1 score
+        '''
+        labels = labels.asnumpy()
+        labels = np.reshape(labels, -1)
+        if self.use_crf:
+            backpointers, best_tag_id = logits
+            best_path = postprocess(backpointers, best_tag_id)
+            logit_id = []
+            for ele in best_path:
+                logit_id.extend(ele)
+        else:
+            logits = logits.asnumpy()
+            logit_id = np.argmax(logits, axis=-1)
+            logit_id = np.reshape(logit_id, -1)
+
+        label_spans = self.tag2span(labels)
+        pred_spans = self.tag2span(logit_id)
+        for span in pred_spans:
+            if span in label_spans:
+                self.TP += 1
+                label_spans.remove(span)
+            else:
+                self.FP += 1
+        for span in label_spans:
+            self.FN += 1
+
+
 class MCC():
     '''
     Calculate Matthews Correlation Coefficient

model_zoo/official/nlp/bert/src/dataset.py

@@ -52,25 +52,30 @@ def create_bert_dataset(device_num=1, rank=0, do_shuffle="true", data_dir=None,
     return data_set
 
 
-def create_ner_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy",
-                       data_file_path=None, schema_file_path=None, do_shuffle=True):
+def create_ner_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy", data_file_path=None,
+                       dataset_format="mindrecord", schema_file_path=None, do_shuffle=True):
     """create finetune or evaluation dataset"""
     type_cast_op = C.TypeCast(mstype.int32)
-    data_set = ds.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
-                                  columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
-                                  shuffle=do_shuffle)
+    if dataset_format == "mindrecord":
+        dataset = ds.MindDataset([data_file_path],
+                                 columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
+                                 shuffle=do_shuffle)
+    else:
+        dataset = ds.TFRecordDataset([data_file_path], schema_file_path if schema_file_path != "" else None,
+                                     columns_list=["input_ids", "input_mask", "segment_ids", "label_ids"],
+                                     shuffle=do_shuffle)
     if assessment_method == "Spearman_correlation":
         type_cast_op_float = C.TypeCast(mstype.float32)
-        data_set = data_set.map(operations=type_cast_op_float, input_columns="label_ids")
+        dataset = dataset.map(operations=type_cast_op_float, input_columns="label_ids")
     else:
-        data_set = data_set.map(operations=type_cast_op, input_columns="label_ids")
-    data_set = data_set.map(operations=type_cast_op, input_columns="segment_ids")
-    data_set = data_set.map(operations=type_cast_op, input_columns="input_mask")
-    data_set = data_set.map(operations=type_cast_op, input_columns="input_ids")
-    data_set = data_set.repeat(repeat_count)
+        dataset = dataset.map(operations=type_cast_op, input_columns="label_ids")
+    dataset = dataset.map(operations=type_cast_op, input_columns="segment_ids")
+    dataset = dataset.map(operations=type_cast_op, input_columns="input_mask")
+    dataset = dataset.map(operations=type_cast_op, input_columns="input_ids")
+    dataset = dataset.repeat(repeat_count)
     # apply batch operations
-    data_set = data_set.batch(batch_size, drop_remainder=True)
-    return data_set
+    dataset = dataset.batch(batch_size, drop_remainder=True)
+    return dataset
 
 
 def create_classification_dataset(batch_size=1, repeat_count=1, assessment_method="accuracy",

model_zoo/official/nlp/bert/src/clue_classification_dataset_process.py → model_zoo/official/nlp/bert/src/finetune_data_preprocess.py

@@ -18,12 +18,14 @@ sample script of processing CLUE classification dataset using mindspore.dataset.
 """
 
 import os
+import argparse
 import numpy as np
 
 import mindspore.common.dtype as mstype
 import mindspore.dataset as ds
 import mindspore.dataset.text as text
 import mindspore.dataset.transforms.c_transforms as ops
+from utils import convert_labels_to_index
 
 
 def process_tnews_clue_dataset(data_dir, label_list, bert_vocab_path, data_usage='train', shuffle_dataset=False,
@@ -135,3 +137,93 @@ def process_cmnli_clue_dataset(data_dir, label_list, bert_vocab_path, data_usage
     dataset = dataset.map(operations=ops.Mask(ops.Relational.NE, 0, mstype.int32), input_columns=["mask_ids"])
     dataset = dataset.batch(batch_size, drop_remainder=drop_remainder)
     return dataset
+
+
+def process_cluener_msra(data_file):
+    """process MSRA dataset for CLUE"""
+    content = []
+    labels = []
+    for line in open(data_file):
+        line = line.strip()
+        if line:
+            word = line.split("\t")[0]
+            if len(line.split("\t")) == 1:
+                label = "O"
+            else:
+                label = line.split("\t")[1].split("\n")[0]
+                if label[0] != "O":
+                    label = label[0] + "_" + label[2:]
+                if label[0] == "I":
+                    label = "M" + label[1:]
+            content.append(word)
+            labels.append(label)
+        else:
+            for i in range(1, len(labels) - 1):
+                if labels[i][0] == "B" and labels[i+1][0] != "M":
+                    labels[i] = "S" + labels[i][1:]
+                elif labels[i][0] == "M" and labels[i+1][0] != labels[i][0]:
+                    labels[i] = "E" + labels[i][1:]
+            last = len(labels) - 1
+            if labels[last][0] == "B":
+                labels[last] = "S" + labels[last][1:]
+            elif labels[last][0] == "M":
+                labels[last] = "E" + labels[last][1:]
+
+            yield (np.array("".join(content)), np.array(list(labels)))
+            content.clear()
+            labels.clear()
+            continue
+
+
+def process_msra_clue_dataset(data_dir, label_list, bert_vocab_path, max_seq_len=128):
+    """Process MSRA dataset"""
+    ### Loading MSRA from CLUEDataset
+    dataset = ds.GeneratorDataset(process_cluener_msra(data_dir), column_names=['text', 'label'])
+
+    ### Processing label
+    label_vocab = text.Vocab.from_list(label_list)
+    label_lookup = text.Lookup(label_vocab)
+    dataset = dataset.map(operations=label_lookup, input_columns="label", output_columns="label_ids")
+    dataset = dataset.map(operations=ops.Concatenate(prepend=np.array([0], dtype='i')),
+                          input_columns=["label_ids"])
+    dataset = dataset.map(operations=ops.Slice(slice(0, max_seq_len)), input_columns=["label_ids"])
+    dataset = dataset.map(operations=ops.PadEnd([max_seq_len], 0), input_columns=["label_ids"])
+    ### Processing sentence
+    vocab = text.Vocab.from_file(bert_vocab_path)
+    lookup = text.Lookup(vocab, unknown_token='[UNK]')
+    unicode_char_tokenizer = text.UnicodeCharTokenizer()
+    dataset = dataset.map(operations=unicode_char_tokenizer, input_columns=["text"], output_columns=["sentence"])
+    dataset = dataset.map(operations=ops.Slice(slice(0, max_seq_len-2)), input_columns=["sentence"])
+    dataset = dataset.map(operations=ops.Concatenate(prepend=np.array(["[CLS]"], dtype='S'),
+                                                     append=np.array(["[SEP]"], dtype='S')), input_columns=["sentence"])
+    dataset = dataset.map(operations=lookup, input_columns=["sentence"], output_columns=["input_ids"])
+    dataset = dataset.map(operations=ops.PadEnd([max_seq_len], 0), input_columns=["input_ids"])
+    dataset = dataset.map(operations=ops.Duplicate(), input_columns=["input_ids"],
+                          output_columns=["input_ids", "input_mask"],
+                          column_order=["input_ids", "input_mask", "label_ids"])
+    dataset = dataset.map(operations=ops.Mask(ops.Relational.NE, 0, mstype.int32), input_columns=["input_mask"])
+    dataset = dataset.map(operations=ops.Duplicate(), input_columns=["input_ids"],
+                          output_columns=["input_ids", "segment_ids"],
+                          column_order=["input_ids", "input_mask", "segment_ids", "label_ids"])
+    dataset = dataset.map(operations=ops.Fill(0), input_columns=["segment_ids"])
+    return dataset
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="create mindrecord")
+    parser.add_argument("--data_dir", type=str, default="", help="dataset path")
+    parser.add_argument("--vocab_file", type=str, default="", help="Vocab file path")
+    parser.add_argument("--max_seq_len", type=int, default=128, help="Sequence length")
+    parser.add_argument("--save_path", type=str, default="./my.mindrecord", help="Path to save mindrecord")
+    parser.add_argument("--label2id", type=str, default="",
+                        help="Label2id file path, must be set for cluener2020 task")
+    args_opt = parser.parse_args()
+    if args_opt.label2id == "":
+        raise ValueError("label2id should not be empty")
+    labels_list = []
+    with open(args_opt.label2id) as f:
+        for tag in f:
+            labels_list.append(tag.strip())
+    tag_to_index = list(convert_labels_to_index(labels_list).keys())
+    ds = process_msra_clue_dataset(args_opt.data_dir, tag_to_index, args_opt.vocab_file, args_opt.max_seq_len)
+    ds.save(args_opt.save_path)