!6616 add tokenization and score file

Merge pull request !6616 from yoonlee666/token

model_zoo/official/nlp/bert/run_ner.py

@@ -18,12 +18,11 @@ Bert finetune and evaluation script.
 '''
 
 import os
-import json
 import argparse
 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
+from src.utils import make_directory, LossCallBack, LoadNewestCkpt, BertLearningRate, convert_labels_to_index
 from src.assessment_method import Accuracy, F1, MCC, Spearman_Correlation
 import mindspore.common.dtype as mstype
 from mindspore import context
@@ -99,7 +98,7 @@ def eval_result_print(assessment_method="accuracy", callback=None):
         raise ValueError("Assessment method not supported, support: [accuracy, f1, mcc, spearman_correlation]")
 
 def do_eval(dataset=None, network=None, use_crf="", num_class=2, assessment_method="accuracy", data_file="",
-            load_checkpoint_path="", vocab_file="", label2id_file="", tag_to_index=None):
+            load_checkpoint_path="", vocab_file="", label_file="", tag_to_index=None):
     """ do eval """
     if load_checkpoint_path == "":
         raise ValueError("Finetune model missed, evaluation task must load finetune model!")
@@ -114,7 +113,8 @@ def do_eval(dataset=None, network=None, use_crf="", num_class=2, assessment_meth
 
     if assessment_method == "clue_benchmark":
         from src.cluener_evaluation import submit
-        submit(model=model, path=data_file, vocab_file=vocab_file, use_crf=use_crf, label2id_file=label2id_file)
+        submit(model=model, path=data_file, vocab_file=vocab_file, use_crf=use_crf,
+               label_file=label_file, tag_to_index=tag_to_index)
     else:
         if assessment_method == "accuracy":
             callback = Accuracy()
@@ -161,7 +161,7 @@ def parse_args():
     parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
                         help="Enable eval data shuffle, default is false")
     parser.add_argument("--vocab_file_path", type=str, default="", help="Vocab file path, used in clue benchmark")
-    parser.add_argument("--label2id_file_path", type=str, default="", help="label2id file path, used in clue benchmark")
+    parser.add_argument("--label_file_path", type=str, default="", help="label file path, used in clue benchmark")
     parser.add_argument("--save_finetune_checkpoint_path", type=str, default="", help="Save checkpoint path")
     parser.add_argument("--load_pretrain_checkpoint_path", type=str, default="", help="Load checkpoint file path")
     parser.add_argument("--load_finetune_checkpoint_path", type=str, default="", help="Load checkpoint file path")
@@ -180,10 +180,10 @@ def parse_args():
         raise ValueError("'eval_data_file_path' must be set when do evaluation task")
     if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.vocab_file_path == "":
         raise ValueError("'vocab_file_path' must be set to do clue benchmark")
-    if args_opt.use_crf.lower() == "true" and args_opt.label2id_file_path == "":
-        raise ValueError("'label2id_file_path' must be set to use crf")
-    if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.label2id_file_path == "":
-        raise ValueError("'label2id_file_path' must be set to do clue benchmark")
+    if args_opt.use_crf.lower() == "true" and args_opt.label_file_path == "":
+        raise ValueError("'label_file_path' must be set to use crf")
+    if args_opt.assessment_method.lower() == "clue_benchmark" and args_opt.label_file_path == "":
+        raise ValueError("'label_file_path' must be set to do clue benchmark")
     return args_opt
 
 
@@ -205,11 +205,12 @@ def run_ner():
             bert_net_cfg.compute_type = mstype.float32
     else:
         raise Exception("Target error, GPU or Ascend is supported.")
-
-    tag_to_index = None
+    label_list = []
+    with open(args_opt.label_file_path) as f:
+        for label in f:
+            label_list.append(label.strip())
+    tag_to_index = convert_labels_to_index(label_list)
     if args_opt.use_crf.lower() == "true":
-        with open(args_opt.label2id_file_path) as json_file:
-            tag_to_index = json.load(json_file)
         max_val = max(tag_to_index.values())
         tag_to_index["<START>"] = max_val + 1
         tag_to_index["<STOP>"] = max_val + 2
@@ -240,7 +241,7 @@ def run_ner():
                                 schema_file_path=args_opt.schema_file_path,
                                 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, args_opt.label2id_file_path, tag_to_index)
+                load_finetune_checkpoint_path, args_opt.vocab_file_path, args_opt.label_file_path, tag_to_index)
 
 if __name__ == "__main__":
     run_ner()

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

@@ -38,7 +38,7 @@ python ${PROJECT_DIR}/../run_ner.py  \
     --train_data_shuffle="true" \
     --eval_data_shuffle="false" \
     --vocab_file_path="" \
-    --label2id_file_path="" \
+    --label_file_path="" \
     --save_finetune_checkpoint_path="" \
     --load_pretrain_checkpoint_path="" \
     --load_finetune_checkpoint_path="" \

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

@@ -23,9 +23,9 @@ from src import tokenization
 from src.sample_process import label_generation, process_one_example_p
 from src.CRF import postprocess
 from src.finetune_eval_config import bert_net_cfg
+from src.score import get_result
 
-
-def process(model=None, text="", tokenizer_=None, use_crf="", label2id_file=""):
+def process(model=None, text="", tokenizer_=None, use_crf="", tag_to_index=None, vocab=""):
     """
     process text.
     """
@@ -34,7 +34,7 @@ def process(model=None, text="", tokenizer_=None, use_crf="", label2id_file=""):
     res = []
     ids = []
     for i in data:
-        feature = process_one_example_p(tokenizer_, i, max_seq_len=bert_net_cfg.seq_length)
+        feature = process_one_example_p(tokenizer_, vocab, i, max_seq_len=bert_net_cfg.seq_length)
         features.append(feature)
         input_ids, input_mask, token_type_id = feature
         input_ids = Tensor(np.array(input_ids), mstype.int32)
@@ -52,10 +52,10 @@ def process(model=None, text="", tokenizer_=None, use_crf="", label2id_file=""):
             ids = logits.asnumpy()
             ids = np.argmax(ids, axis=-1)
             ids = list(ids)
-    res = label_generation(text=text, probs=ids, label2id_file=label2id_file)
+    res = label_generation(text=text, probs=ids, tag_to_index=tag_to_index)
     return res
 
-def submit(model=None, path="", vocab_file="", use_crf="", label2id_file=""):
+def submit(model=None, path="", vocab_file="", use_crf="", label_file="", tag_to_index=None):
     """
     submit task
     """
@@ -66,8 +66,11 @@ def submit(model=None, path="", vocab_file="", use_crf="", label2id_file=""):
             continue
         oneline = json.loads(line.strip())
         res = process(model=model, text=oneline["text"], tokenizer_=tokenizer_,
-                      use_crf=use_crf, label2id_file=label2id_file)
-        print("text", oneline["text"])
-        print("res:", res)
+                      use_crf=use_crf, tag_to_index=tag_to_index, vocab=vocab_file)
         data.append(json.dumps({"label": res}, ensure_ascii=False))
     open("ner_predict.json", "w").write("\n".join(data))
+    labels = []
+    with open(label_file) as f:
+        for label in f:
+            labels.append(label.strip())
+    get_result(labels, "ner_predict.json", path)

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

@@ -16,9 +16,9 @@
 """process txt"""
 
 import re
-import json
+from src.tokenization import convert_tokens_to_ids
 
-def process_one_example_p(tokenizer, text, max_seq_len=128):
+def process_one_example_p(tokenizer, vocab, text, max_seq_len=128):
     """process one testline"""
     textlist = list(text)
     tokens = []
@@ -37,7 +37,7 @@ def process_one_example_p(tokenizer, text, max_seq_len=128):
         segment_ids.append(0)
     ntokens.append("[SEP]")
     segment_ids.append(0)
-    input_ids = tokenizer.convert_tokens_to_ids(ntokens)
+    input_ids = convert_tokens_to_ids(vocab, ntokens)
     input_mask = [1] * len(input_ids)
     while len(input_ids) < max_seq_len:
         input_ids.append(0)
@@ -52,12 +52,12 @@ def process_one_example_p(tokenizer, text, max_seq_len=128):
     feature = (input_ids, input_mask, segment_ids)
     return feature
 
-def label_generation(text="", probs=None, label2id_file=""):
+def label_generation(text="", probs=None, tag_to_index=None):
     """generate label"""
     data = [text]
     probs = [probs]
     result = []
-    label2id = json.loads(open(label2id_file).read())
+    label2id = tag_to_index
     id2label = [k for k, v in label2id.items()]
 
     for index, prob in enumerate(probs):

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

@@ -0,0 +1,79 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+
+"""
+Calculate average F1 score among labels.
+"""
+
+import json
+
+def get_f1_score_for_each_label(pre_lines, gold_lines, label):
+    """
+    Get F1 score for each label.
+    Args:
+        pre_lines: listed label info from pre_file.
+        gold_lines: listed label info from gold_file.
+        label:
+
+    Returns:
+        F1 score for this label.
+    """
+    TP = 0
+    FP = 0
+    FN = 0
+    index = 0
+    while index < len(pre_lines):
+        pre_line = pre_lines[index].get(label, {})
+        gold_line = gold_lines[index].get(label, {})
+        for sample in pre_line:
+            if sample in gold_line:
+                TP += 1
+            else:
+                FP += 1
+        for sample in gold_line:
+            if sample not in pre_line:
+                FN += 1
+        index += 1
+    f1 = 2 * TP / (2 * TP + FP + FN)
+    return f1
+
+
+def get_f1_score(labels, pre_file, gold_file):
+    """
+    Get F1 scores for each label.
+    Args:
+        labels: list of labels.
+        pre_file: prediction file.
+        gold_file: ground truth file.
+
+    Returns:
+        average F1 score on all labels.
+    """
+    pre_lines = [json.loads(line.strip())['label'] for line in open(pre_file) if line.strip()]
+    gold_lines = [json.loads(line.strip())['label'] for line in open(gold_file) if line.strip()]
+    if len(pre_lines) != len(gold_lines):
+        raise ValueError("pre file and gold file have different line count.")
+    f1_sum = 0
+    for label in labels:
+        f1 = get_f1_score_for_each_label(pre_lines, gold_lines, label)
+        print('label: %s, F1: %.6f' % (label, f1))
+        f1_sum += f1
+
+    return f1_sum/len(labels)
+
+
+def get_result(labels, pre_file, gold_file):
+    avg = get_f1_score(labels, pre_file=pre_file, gold_file=gold_file)
+    print("avg F1: %.6f" % avg)

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

@@ -19,6 +19,7 @@ Functional Cells used in Bert finetune and evaluation.
 
 import os
 import math
+import collections
 import numpy as np
 import mindspore.nn as nn
 from mindspore import log as logger
@@ -213,3 +214,19 @@ class BertLearningRate(LearningRateSchedule):
         else:
             lr = decay_lr
         return lr
+
+
+def convert_labels_to_index(label_list):
+    """
+    Convert label_list to indices for NER task.
+    """
+    label2id = collections.OrderedDict()
+    label2id["O"] = 0
+    prefix = ["S_", "B_", "M_", "E_"]
+    index = 0
+    for label in label_list:
+        for pre in prefix:
+            index += 1
+            sub_label = pre + label
+            label2id[sub_label] = index
+    return label2id