NLP之Huggingface抱抱脸实体命名识别NER案例

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Huggingface github：https://github.com/huggingface/
NER任务打标签神奇：https://github.com/doccano/doccano

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文章目录

• NLP之Huggingface抱抱脸实体命名识别NER案例
• 前言
• 一、训练数据集
• 二、读取数据代码
• 三、导入Bert Tokenizer
• 四、标签对齐
• 五、构建数据集
• 六、导入Bert 预训练模型进行微调
• 七、自定义评估标准
• 八、开始训练
• 九、Demo运用
• 总结

前言

Huggingface抱抱脸框架是当下非常流行的自然语言处理的框架，可以做各种自然语言处理任务，本文主要是运用Huggingface解决ner任务。ner任务也是很多其他任务的子任务。以前运用双向lstm加crf较多，本文直接采用bert。

; 一、训练数据集

2 B-year
0 I-year
1 I-year
9 I-year
年 I-year
成 O
人 O
高 B-exam
考 I-exam
招 O
生 O
统 O
一 O
考 B-exam
试 I-exam
时 O
间 O
表 O

数据为上面所示，其中B-year 代表年份开头，I-year代表年份结尾，B-exam代表考试开头，I-exam代表考试结尾，O代表其他。
ps标签工具有很多，本人使用的是开源的doccano。地址已经共享在文章开头。

二、读取数据代码

代码如下（示例）：

#这里自己写了一个函数用来读取数据，data_dir改成自己的路径
data_dir = ''
def read_data(file_path):
    file_path = Path(file_path)

    raw_text = file_path.read_text(encoding='UTF-8').strip()
    raw_docs = re.split(r'\n\t?\n', raw_text)
    # raw_docs = file_path.read_text().strip()
    token_docs = []
    tag_docs = []
    for doc in raw_docs:
        tokens = []
        tags = []
        for line in doc.split('\n'):
            token, tag = line.split(' ')
            tokens.append(token)
            tags.append(tag)
        token_docs.append(tokens)
        tag_docs.append(tags)
    return token_docs, tag_docs

train_texts, train_tags = read_data(data_dir + '/train.txt')
test_texts, test_tags = read_data(data_dir + '/val.txt')
val_texts, val_tags = read_data(data_dir + '/test.txt')

#unique_tags 代表有多少种标签，tag2id表示每种标签对应的id，id2tag表示每种id对应的标签。后面需要。
unique_tags = set(tag for doc in train_tags for tag in doc)
tag2id = {tag: id for id, tag in enumerate(unique_tags)}
id2tag = {id: tag for tag, id in tag2id.items()}

三、导入Bert Tokenizer

代码如下（示例）：

from transformers import AutoTokenizer, BertTokenizerFast
#is_split_into_words表示已经分词好了
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')
train_encodings = tokenizer(train_texts, is_split_into_words=True,return_offsets_mapping=True, padding=True, truncation=True,max_length=512)
val_encodings = tokenizer(val_texts, is_split_into_words=True,return_offsets_mapping=True, padding=True, truncation=True,max_length=512)

四、标签对齐

由于需要加上cls和padding，所以需要对标签做对应的处理，格外生成的用-100代替。代码如下

def encode_tags(tags, encodings):
    labels = [[tag2id[tag] for tag in doc] for doc in tags]
    #print(labels)
    encoded_labels = []
    for doc_labels, doc_offset in zip(labels, encodings.offset_mapping):
        # 创建全由-100组成的矩阵
        doc_enc_labels = np.ones(len(doc_offset),dtype=int) * -100
        arr_offset = np.array(doc_offset)
        # set labels whose first offset position is 0 and the second is not 0
        if len(doc_labels) >= 510:#防止异常
            doc_labels = doc_labels[:510]
        doc_enc_labels[(arr_offset[:,0] == 0) & (arr_offset[:,1] != 0)] = doc_labels
        encoded_labels.append(doc_enc_labels.tolist())

    return encoded_labels

train_labels = encode_tags(train_tags, train_encodings)
val_labels = encode_tags(val_tags, val_encodings)

五、构建数据集

class NerDataset(torch.utils.data.Dataset):
    def __init__(self, encodings, labels):
        self.encodings = encodings
        self.labels = labels

    def __getitem__(self, idx):
        item = {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
        item['labels'] = torch.tensor(self.labels[idx])
        return item

    def __len__(self):
        return len(self.labels)

train_encodings.pop("offset_mapping") # 训练不需要这个
val_encodings.pop("offset_mapping")
train_dataset = NerDataset(train_encodings, train_labels)
val_dataset = NerDataset(val_encodings, val_labels)

六、导入Bert 预训练模型进行微调

这里会先下载预训练模型
num_labels根据自己的任务进行修改，我这里改成5。

from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
model = AutoModelForTokenClassification.from_pretrained('ckiplab/albert-base-chinese-ner',num_labels=5,
                                                        ignore_mismatched_sizes=True,
                                                        id2label=id2tag,
                                                        label2id=tag2id
                                                        )

七、自定义评估标准

from datasets import load_metric
metric = load_metric("seqeval")

def compute_metrics(p):
    predictions, labels = p
    predictions = np.argmax(predictions, axis=2)

    # 不要管-100那些，剔除掉
    true_predictions = [
        [label_list[p] for (p, l) in zip(prediction, label) if l != -100]
        for prediction, label in zip(predictions, labels)
    ]
    true_labels = [
        [label_list[l] for (p, l) in zip(prediction, label) if l != -100]
        for prediction, label in zip(predictions, labels)
    ]

    results = metric.compute(predictions=true_predictions, references=true_labels)
    return {
        "precision": results["overall_precision"],
        "recall": results["overall_recall"],
        "f1": results["overall_f1"],
        "accuracy": results["overall_accuracy"],
    }

八、开始训练

checkpoint = 'bert-base-chinese'
num_train_epochs = 1000
per_device_train_batch_size=8
per_device_eval_batch_size=8

training_args = TrainingArguments(
    output_dir='./output',          # 输入路径
    num_train_epochs=num_train_epochs,              # 训练epoch数量
    per_device_train_batch_size=per_device_train_batch_size,  # 每个GPU的BATCH
    per_device_eval_batch_size=per_device_eval_batch_size,
    warmup_steps=500,                # warmup次数
    weight_decay=0.01,               # 限制权重的大小
    logging_dir='./logs',
    logging_steps=10,
    save_strategy='steps',
    save_steps=1000,
    save_total_limit=1,
    evaluation_strategy='steps',
    eval_steps=1000
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=val_dataset,
    compute_metrics=compute_metrics
)

trainer.train()
trainer.evaluate()

model.save_pretrained("./checkpoint/model/%s-%sepoch" % (checkpoint, num_train_epochs))

九、Demo运用

这里跑个小的demo，案例是input_str = ‘2009年高考在北京的报名费是2009元’

import torch
import numpy as np

def get_token(input):
    english = 'abcdefghijklmnopqrstuvwxyz'
    output = []
    buffer = ''
    for s in input:
        if s in english or s in english.upper():
            buffer += s
        else:
            if buffer: output.append(buffer)
            buffer = ''
            output.append(s)
    if buffer: output.append(buffer)
    return output

from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer

model = AutoModelForTokenClassification.from_pretrained('./output/checkpoint-2000')

from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('bert-base-chinese')

if __name__ == '__main__':
    input_str = '2009年高考在北京的报名费是2009元'
    input_char = get_token(input_str)
    input_tensor = tokenizer(input_char, is_split_into_words=True, padding=True, truncation=True,
                             return_offsets_mapping=True, max_length=512, return_tensors="pt")
    input_tokens = input_tensor.tokens()
    offsets = input_tensor["offset_mapping"]
    ignore_mask = offsets[0, :, 1] == 0
    # print(input_tensor)
    input_tensor.pop("offset_mapping")  # 不剔除的话会报错
    outputs = model(**input_tensor)
    probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)[0].tolist()
    predictions = outputs.logits.argmax(dim=-1)[0].tolist()
    print(predictions)
    results = []

    tokens = input_tensor.tokens()
    idx = 0
    while idx < len(predictions):
        if ignore_mask[idx]:
            idx += 1
            continue
        pred = predictions[idx]
        label = model.config.id2label[pred]
        if label != "O":
            # 不加B-或者I-
            label = label[2:]
            start = idx
            end = start + 1
            # 获取所有token I-label
            all_scores = []
            all_scores.append(probabilities[start][predictions[start]])
            while (
                    end < len(predictions)
                    and model.config.id2label[predictions[end]] == f"I-{label}"
            ):
                all_scores.append(probabilities[end][predictions[end]])
                end += 1
                idx += 1
            # 得到是他们平均的
            score = np.mean(all_scores).item()
            word = input_tokens[start:end]
            results.append(
                {
                    "entity_group": label,
                    "score": score,
                    "word": word,
                    "start": start,
                    "end": end,
                }
            )
        idx += 1
    for i in range(len(results)):
        print(results[i])

总结

Huggingface提供非常简便的框架，本案例是基于bert的ner任务，以后还会更新gpt等等其他Huggingface案例。感兴趣的可以持续关注。

Original: https://blog.csdn.net/weixin_53280379/article/details/125355146
Author: 陈万君Allen
Title: Huggingface抱抱脸实体命名识别NER案例