引言
本文将采用BERT+BiLSTM+CRF模型进行命名实体识别（Named Entity Recognition 简称NER），即实体识别。命名实体识别，是指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、专有名词等。
- BERT（Bidirectional Encoder Representation from Transformers），即双向Transformer的Encoder。模型的创新点在预训练方法上，即用了Masked LM和Next Sentence Prediction两种方法分别捕捉词语和句子级别的表示。
- BiLSTM是Bi-directional Long Short-Term Memory的缩写，是由前向LSTM与后向LSTM组合而成。
- CRF为条件随机场，可以用于构造在给定一组输入随机变量的条件下,另一组输出随机变量的条件概率分布模型。
环境
采用的Python包为：Kashgari，此包封装了NLP传统和前沿模型，可以快速调用，快速部署模型。
- Python: 3.6
- TensorFlow: 1.15
- Kashgari: 1.x
其中Kashgari1.x版本必须使用TensorFlow一代。
BERT中文预训练数据
谷歌提前训练好的数据，其中中文模型可以从https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip下载。
更多预训练模型参考：https://github.com/ymcui/Chinese-BERT-wwm
自带数据训练评价
数据为中国日报的NER语料库，代码自动下载。
训练集、测试集和验证集的存储格式：
train_x: [[char_seq1],[char_seq2],[char_seq3],..... ]
train_y:[[label_seq1],[label_seq2],[label_seq3],..... ]
其中 char_seq1:["我"，"爱"，"中"，"国"]
对应的的label_seq1:["O"，"O"，"B_LOC"，"I_LOC"]
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from kashgari.corpus import ChineseDailyNerCorpus from kashgari.embeddings import BERTEmbedding from kashgari.tasks.labeling import BiLSTM_CRF_Model import kashgari train_x, train_y = ChineseDailyNerCorpus.load_data('train') test_x, test_y = ChineseDailyNerCorpus.load_data('test') valid_x, valid_y = ChineseDailyNerCorpus.load_data('valid') embedding = BERTEmbedding("chinese", sequence_length=10, task=kashgari.LABELING) model = BiLSTM_CRF_Model(embedding) model.fit(train_x, train_y, x_validate=valid_x, y_validate=valid_y, epochs=1, batch_size=100) model.evaluate(test_x, test_y) # model.save('save') # loaded_model = kashgari.utils.load_model('xxx') |
最后注释的为模型保存和调用代码。
实例
此本分将用自己的数据来进行命名实体识别。train_x和y存储格式和上面相同。
可用的标注格式
BIO标注模式: (B-begin，I-inside，O-outside)
BIOES标注模式: (B-begin，I-inside，O-outside，E-end，S-single)
- B，即Begin，表示开始
- I，即Intermediate，表示中间
- E，即End，表示结尾
- S，即Single，表示单个字符
- O，即Other，表示其他，用于标记无关字符
代码
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from kashgari.embeddings import BERTEmbedding from kashgari.tasks.labeling import BiLSTM_CRF_Model import kashgari import re def text2array(text, sequence_length): textArr = re.findall('.{' + str(sequence_length) + '}', text) textArr.append(text[(len(textArr) * sequence_length):]) return [[c for c in text] for text in textArr] train_x = [['周', '志', '华', '，', '男', '，', '毕', '业', '于', '南', '京', '大', '学', '计', '算', '机', '科', '学', '与', '技', '术', '系', '（', '学', '士', '、', '硕', '士', '、', '博', '士', '）', '。'], ['现', '为', '南', '京', '大', '学', '教', '授', ',', '国', '家', '杰', '出', '青', '年', '基', '金', '获', '得', '者', '。']] train_y = [['B_PER', 'I_PER', 'I_PER', 'O', 'O', 'O', 'O', 'O', 'B_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'O', 'B_DEG', 'I_DEG', 'O', 'B_DEG', 'I_DEG', 'O', 'B_DEG', 'I_DEG', 'O', 'O'], ['O', 'O', 'B_AFF', 'I_AFF', 'I_AFF', 'I_AFF', 'O', 'O', 'O', 'B_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'I_HON', 'O']] embedding = BERTEmbedding("chinese", sequence_length=20, task=kashgari.LABELING) model = BiLSTM_CRF_Model(embedding) model.fit(train_x, train_y, epochs=10, batch_size=100) # model.save('save') sentences = '吴恩达在北京大学。' texts = text2array(sentences, sequence_length=20) ners = model.predict_entities(texts) print(ners) |
结果
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Model: "model_4" __________________________________________________________________________________________________ Layer (type) Output Shape Param # Connected to ================================================================================================== Input-Token (InputLayer) [(None, 20)] 0 __________________________________________________________________________________________________ Input-Segment (InputLayer) [(None, 20)] 0 __________________________________________________________________________________________________ Embedding-Token (TokenEmbedding [(None, 20, 768), (2 16226304 Input-Token[0][0] __________________________________________________________________________________________________ Embedding-Segment (Embedding) (None, 20, 768) 1536 Input-Segment[0][0] __________________________________________________________________________________________________ Embedding-Token-Segment (Add) (None, 20, 768) 0 Embedding-Token[0][0] Embedding-Segment[0][0] __________________________________________________________________________________________________ Embedding-Position (PositionEmb (None, 20, 768) 15360 Embedding-Token-Segment[0][0] __________________________________________________________________________________________________ Embedding-Dropout (Dropout) (None, 20, 768) 0 Embedding-Position[0][0] __________________________________________________________________________________________________ Embedding-Norm (LayerNormalizat (None, 20, 768) 1536 Embedding-Dropout[0][0] __________________________________________________________________________________________________ Encoder-1-MultiHeadSelfAttentio (None, 20, 768) 2362368 Embedding-Norm[0][0] __________________________________________________________________________________________________ Encoder-1-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-1-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-1-MultiHeadSelfAttentio (None, 20, 768) 0 Embedding-Norm[0][0] Encoder-1-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-1-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-1-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-1-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-1-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-1-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-1-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-1-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-1-MultiHeadSelfAttention- Encoder-1-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-1-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-1-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-2-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-1-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-2-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-2-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-2-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-1-FeedForward-Norm[0][0] Encoder-2-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-2-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-2-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-2-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-2-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-2-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-2-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-2-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-2-MultiHeadSelfAttention- Encoder-2-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-2-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-2-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-3-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-2-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-3-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-3-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-3-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-2-FeedForward-Norm[0][0] Encoder-3-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-3-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-3-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-3-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-3-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-3-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-3-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-3-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-3-MultiHeadSelfAttention- Encoder-3-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-3-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-3-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-4-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-3-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-4-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-4-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-4-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-3-FeedForward-Norm[0][0] Encoder-4-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-4-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-4-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-4-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-4-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-4-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-4-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-4-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-4-MultiHeadSelfAttention- Encoder-4-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-4-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-4-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-5-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-4-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-5-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-5-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-5-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-4-FeedForward-Norm[0][0] Encoder-5-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-5-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-5-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-5-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-5-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-5-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-5-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-5-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-5-MultiHeadSelfAttention- Encoder-5-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-5-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-5-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-6-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-5-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-6-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-6-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-6-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-5-FeedForward-Norm[0][0] Encoder-6-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-6-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-6-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-6-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-6-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-6-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-6-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-6-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-6-MultiHeadSelfAttention- Encoder-6-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-6-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-6-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-7-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-6-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-7-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-7-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-7-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-6-FeedForward-Norm[0][0] Encoder-7-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-7-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-7-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-7-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-7-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-7-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-7-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-7-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-7-MultiHeadSelfAttention- Encoder-7-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-7-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-7-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-8-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-7-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-8-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-8-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-8-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-7-FeedForward-Norm[0][0] Encoder-8-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-8-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-8-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-8-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-8-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-8-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-8-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-8-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-8-MultiHeadSelfAttention- Encoder-8-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-8-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-8-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-9-MultiHeadSelfAttentio (None, 20, 768) 2362368 Encoder-8-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-9-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-9-MultiHeadSelfAttention[ __________________________________________________________________________________________________ Encoder-9-MultiHeadSelfAttentio (None, 20, 768) 0 Encoder-8-FeedForward-Norm[0][0] Encoder-9-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-9-MultiHeadSelfAttentio (None, 20, 768) 1536 Encoder-9-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-9-FeedForward (FeedForw (None, 20, 768) 4722432 Encoder-9-MultiHeadSelfAttention- __________________________________________________________________________________________________ Encoder-9-FeedForward-Dropout ( (None, 20, 768) 0 Encoder-9-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-9-FeedForward-Add (Add) (None, 20, 768) 0 Encoder-9-MultiHeadSelfAttention- Encoder-9-FeedForward-Dropout[0][ __________________________________________________________________________________________________ Encoder-9-FeedForward-Norm (Lay (None, 20, 768) 1536 Encoder-9-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-10-MultiHeadSelfAttenti (None, 20, 768) 2362368 Encoder-9-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-10-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-10-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-10-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-9-FeedForward-Norm[0][0] Encoder-10-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-10-MultiHeadSelfAttenti (None, 20, 768) 1536 Encoder-10-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-10-FeedForward (FeedFor (None, 20, 768) 4722432 Encoder-10-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-10-FeedForward-Dropout (None, 20, 768) 0 Encoder-10-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-10-FeedForward-Add (Add (None, 20, 768) 0 Encoder-10-MultiHeadSelfAttention Encoder-10-FeedForward-Dropout[0] __________________________________________________________________________________________________ Encoder-10-FeedForward-Norm (La (None, 20, 768) 1536 Encoder-10-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-11-MultiHeadSelfAttenti (None, 20, 768) 2362368 Encoder-10-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-11-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-11-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-11-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-10-FeedForward-Norm[0][0] Encoder-11-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-11-MultiHeadSelfAttenti (None, 20, 768) 1536 Encoder-11-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-11-FeedForward (FeedFor (None, 20, 768) 4722432 Encoder-11-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-11-FeedForward-Dropout (None, 20, 768) 0 Encoder-11-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-11-FeedForward-Add (Add (None, 20, 768) 0 Encoder-11-MultiHeadSelfAttention Encoder-11-FeedForward-Dropout[0] __________________________________________________________________________________________________ Encoder-11-FeedForward-Norm (La (None, 20, 768) 1536 Encoder-11-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-12-MultiHeadSelfAttenti (None, 20, 768) 2362368 Encoder-11-FeedForward-Norm[0][0] __________________________________________________________________________________________________ Encoder-12-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-12-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-12-MultiHeadSelfAttenti (None, 20, 768) 0 Encoder-11-FeedForward-Norm[0][0] Encoder-12-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-12-MultiHeadSelfAttenti (None, 20, 768) 1536 Encoder-12-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-12-FeedForward (FeedFor (None, 20, 768) 4722432 Encoder-12-MultiHeadSelfAttention __________________________________________________________________________________________________ Encoder-12-FeedForward-Dropout (None, 20, 768) 0 Encoder-12-FeedForward[0][0] __________________________________________________________________________________________________ Encoder-12-FeedForward-Add (Add (None, 20, 768) 0 Encoder-12-MultiHeadSelfAttention Encoder-12-FeedForward-Dropout[0] __________________________________________________________________________________________________ Encoder-12-FeedForward-Norm (La (None, 20, 768) 1536 Encoder-12-FeedForward-Add[0][0] __________________________________________________________________________________________________ Encoder-Output (Concatenate) (None, 20, 3072) 0 Encoder-9-FeedForward-Norm[0][0] Encoder-10-FeedForward-Norm[0][0] Encoder-11-FeedForward-Norm[0][0] Encoder-12-FeedForward-Norm[0][0] __________________________________________________________________________________________________ non_masking_layer (NonMaskingLa (None, 20, 3072) 0 Encoder-Output[0][0] __________________________________________________________________________________________________ layer_blstm (Bidirectional) (None, 20, 256) 3277824 non_masking_layer[0][0] __________________________________________________________________________________________________ layer_dense (Dense) (None, 20, 64) 16448 layer_blstm[0][0] __________________________________________________________________________________________________ layer_crf_dense (Dense) (None, 20, 10) 650 layer_dense[0][0] __________________________________________________________________________________________________ layer_crf (CRF) (None, 20, 10) 100 layer_crf_dense[0][0] ================================================================================================== Total params: 104,594,222 Trainable params: 3,295,022 Non-trainable params: 101,299,200 __________________________________________________________________________________________________ Epoch 1/10 1/1 [==============================] - 6s 6s/step - loss: 52.2269 - accuracy: 0.1250 Epoch 2/10 1/1 [==============================] - 1s 687ms/step - loss: 22.6029 - accuracy: 0.6750 Epoch 3/10 1/1 [==============================] - 1s 754ms/step - loss: 12.7078 - accuracy: 0.8500 Epoch 4/10 1/1 [==============================] - 1s 767ms/step - loss: 12.8406 - accuracy: 0.8250 Epoch 5/10 1/1 [==============================] - 1s 717ms/step - loss: 10.0257 - accuracy: 0.8750 Epoch 6/10 1/1 [==============================] - 1s 638ms/step - loss: 7.3283 - accuracy: 0.8750 Epoch 7/10 1/1 [==============================] - 1s 738ms/step - loss: 4.4533 - accuracy: 0.9500 Epoch 8/10 1/1 [==============================] - 1s 734ms/step - loss: 5.0040 - accuracy: 0.9750 Epoch 9/10 1/1 [==============================] - 1s 698ms/step - loss: 2.6457 - accuracy: 1.0000 Epoch 10/10 1/1 [==============================] - 1s 743ms/step - loss: 2.0256 - accuracy: 1.0000 [{'text': '吴 恩 达 在 北 京 大 学 。', 'text_raw': ['吴', '恩', '达', '在', '北', '京', '大', '学', '。'], 'labels': [{'entity': 'B_PER', 'start': 0, 'end': 0, 'value': '吴'}, {'entity': 'I_PER', 'start': 1, 'end': 2, 'value': '恩 达'}, {'entity': 'I_AFF', 'start': 4, 'end': 7, 'value': '北 京 大 学'}]}] |
输出展示了BERT的12层Transformer结构，以及它的参数量。最后是NER的结果。
参考资料
- https://kashgari-zh.bmio.net/
- https://www.jianshu.com/p/1d6689851622
- https://blog.csdn.net/ctwy291314/article/details/102819221
您好~想请教一下这个模型evaluate后，关于p、r、f1的micro avg和macro avg这两个值怎么解读，为什么我看有的关于kashgari的使用教程最后出来的有avg / total（p、r、f1）值，即有个总值，我评估出来的没有这个呀（因为没法贴图不知道您是否可以理解我的疑问
macro avg:对所有类别的平均。
micro avg:对数据集中的每⼀个实例不分类别进⾏统计建⽴全局混淆矩阵，然后计算相应指标。
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re.error: nothing to repeat at position 0；运用以上代码跑出来是这个结果，对比你给出的结果，那个结果中最后一行没有[{‘text’: ‘吴 恩 达 在 北 京 大 学 。’, ‘text_raw’: [‘吴’, ‘恩’, ‘达’, ‘在’, ‘北’, ‘京’, ‘大’, ‘学’, ‘。’……这个出不来，其他都有。
巨危啊，我的出得来，你看看那个接口调用对不对。