摘要：
通过识别BERT对话情绪状态的实例，展现在昇思MindSpore AI框架中大语言模型的原理和实际使用方法、步骤。
一、环境配置
%%capture captured_output
# 实验环境已经预装了mindspore==2.2.14，如需更换mindspore版本，可更改下面mindspore的版本号
!pip uninstall mindspore -y
!pip install -i https://pypi.mirrors.ustc.edu.cn/simple mindspore==2.2.14
# 该案例在 mindnlp 0.3.1 版本完成适配，如果发现案例跑不通，可以指定mindnlp版本，执行`!pip install mindnlp==0.3.1`
!pip install mindnlp
输出：
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting mindnlp
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Collecting tokenizers (from mindnlp)
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Collecting jieba (from mindnlp)
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Collecting pytest==7.2.0 (from mindnlp)
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Collecting xxhash (from datasets->mindnlp)
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Collecting pygtrie<3.0,>=2.1 (from pyctcdecode->mindnlp)
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Created wheel for jieba: filename=jieba-0.42.1-py3-none-any.whl size=19314459 sha256=352f23b7dc8b4bade2f918165e055bc707601544400a4918136ba69f220ce9f6
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Installing collected packages: sortedcontainers, sentencepiece, pygtrie, jieba, addict, xxhash, safetensors, regex, pytest, pyarrow-hotfix, pyarrow, multiprocess, multidict, ml-dtypes, hypothesis, fsspec, frozenlist, async-timeout, yarl, pyctcdecode, aiosignal, tokenizers, aiohttp, datasets, evaluate, mindnlp
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[notice] A new release of pip is available: 24.1 -> 24.1.1
[notice] To update, run: python -m pip install --upgrade pip
显示mindspore模块的基本信息
!pip show mindspore
输出：
Name: mindspore
Version: 2.2.14
Summary: MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Home-page: https://www.mindspore.cn
Author: The MindSpore Authors
Author-email: contact@mindspore.cn
License: Apache 2.0
Location: /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages
Requires: asttokens, astunparse, numpy, packaging, pillow, protobuf, psutil, scipy
Required-by: mindnlp
二、模型简介
BERT是一种新型语言模型
全称：Bidirectional Encoder Representations from Transformers
中文：双向表达的编码变换
Google发布于2018年
用于自然语言处理场景类似的预训练语言模型有：
问答
命名实体识别
自然语言推理
文本分类等
BERT模型涉及
Transformer的Encoder
双向结构
BERT模型的主要创新点
pre-train方法
用Masked Language Model捕捉词语
用Next Sentence Prediction捕捉句子
用Masked Language Model方法训练BERT对话时
随机把语料库中15%的单词做Mask操作。
Mask操作的三种情况：
80%的单词直接用[Mask]替换
10%的单词直接替换成另一个新的单词
10%的单词保持不变。
问答Question Answering (QA)
自然语言推断Natural Language Inference (NLI)
Next Sentence Prediction预训练任务
目的：
让模型理解两个句子之间的联系。
训练内容：
输入是句子A和B
B有一半的几率是A的下一句
预测B是不是A的下一句
训练结果：
Embedding table
12层Transformer权重（BERT-BASE）
或24层Transformer权重（BERT-LARGE）。
微调Fine-tuning下游任务：
文本分类
相似度判断
阅读理解等。
对话情绪识别Emotion Detection（简称EmoTect）
对话文本
判断文本情绪类别
积极
消极
中性
计算置信度。
示例：
导入mindspore dataset nn context mindnlp等模块
import os
import mindspore
from mindspore.dataset import text, GeneratorDataset, transforms
from mindspore import nn, context
from mindnlp._legacy.engine import Trainer, Evaluator
from mindnlp._legacy.engine.callbacks import CheckpointCallback, BestModelCallback
from mindnlp._legacy.metrics import Accuracy
输出：
Building prefix dict from the default dictionary ...
Dumping model to file cache /tmp/jieba.cache
Loading model cost 1.037 seconds.
Prefix dict has been built successfully.
三、准备数据集
- 数据集说明
实验数据集采用百度飞桨的机器人聊天数据
已标注
分词预处理
数据两列，制表符（'\t'）分隔：
情绪分类
0消极
1中性
2积极
中文文本
空格分词
utf8编码
数据示例：
label--text_a
0--谁骂人了？我从来不骂人，我骂的都不是人，你是人吗 ？
1--我有事等会儿就回来和你聊
2--我见到你很高兴谢谢你帮我
2.下载数据集
# download dataset
!wget https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz -O emotion_detection.tar.gz
!tar xvf emotion_detection.tar.gz
输出：
--2024-07-01 13:38:50-- https://baidu-nlp.bj.bcebos.com/emotion_detection-dataset-1.0.0.tar.gz
Resolving baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)... 119.249.103.5, 113.200.2.111, 2409:8c04:1001:1203:0:ff:b0bb:4f27
Connecting to baidu-nlp.bj.bcebos.com (baidu-nlp.bj.bcebos.com)|119.249.103.5|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 1710581 (1.6M) [application/x-gzip]
Saving to: 'emotion_detection.tar.gz'
emotion_detection.t 100%[===================>] 1.63M 8.04MB/s in 0.2s
2024-07-01 13:38:50 (8.04 MB/s) - 'emotion_detection.tar.gz' saved [1710581/1710581]
data/
data/test.tsv
data/infer.tsv
data/dev.tsv
data/train.tsv
data/vocab.txt
3.定义数据集类
# prepare dataset
class SentimentDataset:
"""Sentiment Dataset"""
def __init__(self, path):
self.path = path
self._labels, self._text_a = [], []
self._load()
def _load(self):
with open(self.path, "r", encoding="utf-8") as f:
dataset = f.read()
lines = dataset.split("\n")
for line in lines[1:-1]:
label, text_a = line.split("\t")
self._labels.append(int(label))
self._text_a.append(text_a)
def __getitem__(self, index):
return self._labels[index], self._text_a[index]
def __len__(self):
return len(self._labels)
四、 数据加载和数据预处理
数据加载和预处理函数
process_dataset()
import numpy as np
def process_dataset(source, tokenizer, max_seq_len=64, batch_size=32, shuffle=True):
is_ascend = mindspore.get_context('device_target') == 'Ascend'
column_names = ["label", "text_a"]
dataset = GeneratorDataset(source, column_names=column_names, shuffle=shuffle)
# transforms
type_cast_op = transforms.TypeCast(mindspore.int32)
def tokenize_and_pad(text):
if is_ascend:
tokenized = tokenizer(text, padding='max_length',
truncation=True, max_length=max_seq_len)
else:
tokenized = tokenizer(text)
return tokenized['input_ids'], tokenized['attention_mask']
# map dataset
dataset = dataset.map(operations=tokenize_and_pad, input_columns="text_a",
output_columns=['input_ids', 'attention_mask'])
dataset = dataset.map(operations=[type_cast_op], input_columns="label",
output_columns='labels')
# batch dataset
if is_ascend:
dataset = dataset.batch(batch_size)
else:
dataset = dataset.padded_batch(batch_size,
pad_info={'input_ids': (None, tokenizer.pad_token_id),
'attention_mask': (None, 0)})
return dataset
数据预处理部分采用静态Shape处理：
昇腾NPU环境下暂不支持动态Shape
from mindnlp.transformers import BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-chinese')
输出：
100%━━━━━━━━━━━━━━━━━━━━━ 49.0/49.0 [00:00<00:00, 3.05kB/s]
━107k/0.00 [00:05<00:00, 36.3kB/s]
━263k/0.00 [00:15<00:00, 10.2kB/s]
━━━━━━━━━━━━━━━━━━━━━ 624/? [00:00<00:00, 56.0kB/s]
tokenizer.pad_token_id
输出：
0
取训练数据集的列名：
dataset_train = process_dataset(SentimentDataset("data/train.tsv"), tokenizer)
dataset_val = process_dataset(SentimentDataset("data/dev.tsv" ), tokenizer)
dataset_test = process_dataset(SentimentDataset("data/test.tsv" ), tokenizer, shuffle=False)
dataset_train.get_col_names()
输出：
['input_ids', 'attention_mask', 'labels']
遍历显示训练数据集
print(next(dataset_train.create_tuple_iterator()))
输出：
[Tensor(shape=[32, 64], dtype=Int64, value=
[[ 101, 2769, 4638 ... 0, 0, 0],
[ 101, 2769, 3221 ... 0, 0, 0],
[ 101, 758, 1282 ... 0, 0, 0],
...
[ 101, 1217, 678 ... 0, 0, 0],
[ 101, 872, 679 ... 0, 0, 0],
[ 101, 872, 3766 ... 0, 0, 0]]),
Tensor(shape=[32, 64], dtype=Int64, value=
[[1, 1, 1 ... 0, 0, 0],
[1, 1, 1 ... 0, 0, 0],
[1, 1, 1 ... 0, 0, 0],
...
[1, 1, 1 ... 0, 0, 0],
[1, 1, 1 ... 0, 0, 0],
[1, 1, 1 ... 0, 0, 0]]),
Tensor(shape=[32], dtype=Int32, value=
[1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 1])]
五、 模型构建
BERT 模型
BertForSequenceClassification模块构建
加载预训练权重
设置情感三分类
自动混合精度
实例化优化器
实例化评价指标
设置模型训练的权重保存策略
构建训练器
模型开始训练
from mindnlp.transformers import BertForSequenceClassification, BertModel
from mindnlp._legacy.amp import auto_mixed_precision
# set bert config and define parameters for training
model = BertForSequenceClassification.from_pretrained('bert-base-chinese', num_labels=3)
model = auto_mixed_precision(model, 'O1')
optimizer = nn.Adam(model.trainable_params(), learning_rate=2e-5)
(), learning_rate=2e-5)
输出：
100%━━━━━━━━━━━━━━━━━━ 392M/392M [00:53<00:00, 6.82MB/s]
The following parameters in checkpoint files are not loaded:
['cls.predictions.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.dense.weight', 'cls.seq_relationship.bias', 'cls.seq_relationship.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.LayerNorm.weight']
The following parameters in models are missing parameter:
['classifier.weight', 'classifier.bias']
metric = Accuracy()
# define callbacks to save checkpoints
ckpoint_cb = CheckpointCallback(save_path='checkpoint', ckpt_name='bert_emotect', epochs=1, keep_checkpoint_max=2)
best_model_cb = BestModelCallback(save_path='checkpoint', ckpt_name='bert_emotect_best', auto_load=True)
# 构建训练器
trainer = Trainer(network=model, train_dataset=dataset_train,
eval_dataset=dataset_val, metrics=metric,
epochs=5, optimizer=optimizer, callbacks=[ckpoint_cb, best_model_cb])
%%time
# start training
trainer.run(tgt_columns="labels")
输出：
The train will start from the checkpoint saved in 'checkpoint'.
Epoch 0: 100%━━━━━━━━━━━━━━ 302/302 [04:07<00:00, 2.25s/it, loss=0.3460012]
Checkpoint: 'bert_emotect_epoch_0.ckpt' has been saved in epoch: 0.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:07<00:00, 1.07it/s]
Evaluate Score: {'Accuracy': 0.9351851851851852}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 0.---------------
Epoch 1: 100%━━━━━━━━━━━━━━ 302/302 [02:38<00:00, 1.95it/s, loss=0.19017023]
Checkpoint: 'bert_emotect_epoch_1.ckpt' has been saved in epoch: 1.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:05<00:00, 7.48it/s]
Evaluate Score: {'Accuracy': 0.9564814814814815}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 1.---------------
Epoch 2: 100%━━━━━━━━━━━━━━ 302/302 [02:40<00:00, 1.92it/s, loss=0.12662967]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_2.ckpt' has been saved in epoch: 2.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00, 7.59it/s]
Evaluate Score: {'Accuracy': 0.9740740740740741}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 2.---------------
Epoch 3: 100%━━━━━━━━━━━━━━ 302/302 [02:40<00:00, 1.92it/s, loss=0.08593981]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_3.ckpt' has been saved in epoch: 3.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00, 7.51it/s]
Evaluate Score: {'Accuracy': 0.9833333333333333}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 3.---------------
Epoch 4: 100%━━━━━━━━━━━━━━ 302/302 [02:41<00:00, 1.92it/s, loss=0.05900709]
The maximum number of stored checkpoints has been reached.
Checkpoint: 'bert_emotect_epoch_4.ckpt' has been saved in epoch: 4.
Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:04<00:00, 7.39it/s]
Evaluate Score: {'Accuracy': 0.9879629629629629}
---------------Best Model: 'bert_emotect_best.ckpt' has been saved in epoch: 4.---------------
Loading best model from 'checkpoint' with '['Accuracy']': [0.9879629629629629]...
---------------The model is already load the best model from 'bert_emotect_best.ckpt'.---------------
CPU times: user 22min 58s, sys: 13min 25s, total: 36min 24s
Wall time: 15min 30s
六、 模型验证
验证评估
测试数据集
准确率
evaluator = Evaluator(network=model, eval_dataset=dataset_test, metrics=metric)
evaluator.run(tgt_columns="labels")
输出：
Evaluate: 100%━━━━━━━━━━━━━━ 33/33 [00:08<00:00, 1.20s/it]
Evaluate Score: {'Accuracy': 0.8822393822393823}
七、 模型推理
遍历推理数据集，展示结果与标签。
dataset_infer = SentimentDataset("data/infer.tsv")
def predict(text, label=None):
label_map = {0: "消极", 1: "中性", 2: "积极"}
text_tokenized = Tensor([tokenizer(text).input_ids])
logits = model(text_tokenized)
predict_label = logits[0].asnumpy().argmax()
info = f"inputs: '{text}', predict: '{label_map[predict_label]}'"
if label is not None:
info += f" , label: '{label_map[label]}'"
print(info)
from mindspore import Tensor
for label, text in dataset_infer:
predict(text, label)
输出：
inputs: '我 要 客观', predict: '中性' , label: '中性'
inputs: '靠 你 真是 说 废话 吗', predict: '消极' , label: '消极'
inputs: '口嗅 会', predict: '中性' , label: '中性'
inputs: '每次 是 表妹 带 窝 飞 因为 窝路痴', predict: '中性' , label: '中性'
inputs: '别说 废话 我 问 你 个 问题', predict: '消极' , label: '消极'
inputs: '4967 是 新加坡 那 家 银行', predict: '中性' , label: '中性'
inputs: '是 我 喜欢 兔子', predict: '积极' , label: '积极'
inputs: '你 写 过 黄山 奇石 吗', predict: '中性' , label: '中性'
inputs: '一个一个 慢慢来', predict: '中性' , label: '中性'
inputs: '我 玩 过 这个 一点 都 不 好玩', predict: '消极' , label: '消极'
inputs: '网上 开发 女孩 的 QQ', predict: '中性' , label: '中性'
inputs: '背 你 猜 对 了', predict: '中性' , label: '中性'
inputs: '我 讨厌 你 ， 哼哼 哼 。 。', predict: '消极' , label: '消极'
inputs: '我 讨厌 你 ， 哼哼 哼 。 。', predict: '消极' , label: '消极'
八、 自定义推理数据集
predict("家人们咱就是说一整个无语住了 绝绝子叠buff")
输出：
inputs: '家人们咱就是说一整个无语住了 绝绝子叠buff', predict: '中性'