做自己视频教程的网站,改变网站的域名,张家界住房和城乡建设局网站,遵义网站建设制作摘要#xff1a; 通过识别BERT对话情绪状态的实例#xff0c;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mindspore2.2.14#xff0c;如需更换mindspore版本#xff0c;可更改下…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mindspore2.2.14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 mindspore2.2.14 # 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 mindnlp0.3.1 !pip install mindnlp 输出 Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Collecting mindnlpDownloading https://pypi.tuna.tsinghua.edu.cn/packages/72/37/ef313c23fd587c3d1f46b0741c98235aecdfd93b4d6d446376f3db6a552c/mindnlp-0.3.1-py3-none-any.whl (5.7 MB)━━━━━━━━━━━━━━━━ 5.7/5.7 MB 14.2 MB/s eta 0:00:0000:0100:01 Requirement already satisfied: mindspore in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp) (2.2.14) Requirement already satisfied: tqdm in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp) (4.66.4) Requirement already satisfied: requests in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindnlp) (2.32.3) Collecting datasets (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/60/2d/963b266bb8f88492d5ab4232d74292af8beb5b6fdae97902df9e284d4c32/datasets-2.20.0-py3-none-any.whl (547 kB)━━━━━━━━━━━━━━━━ 547.8/547.8 kB 21.2 MB/s eta 0:00:00 Collecting evaluate (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c2/d6/ff9baefc8fc679dcd9eb21b29da3ef10c81aa36be630a7ae78e4611588e1/evaluate-0.4.2-py3-none-any.whl (84 kB)━━━━━━━━━━━━━━━━ 84.1/84.1 kB 24.8 MB/s eta 0:00:00 Collecting tokenizers (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ba/26/139bd2371228a0e203da7b3e3eddcb02f45b2b7edd91df00e342e4b55e13/tokenizers-0.19.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (3.6 MB)━━━━━━━━━━━━━━━━ 3.6/3.6 MB 14.7 MB/s eta 0:00:00a 0:00:01 Collecting safetensors (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/02/28e6280ed0f1bde89eed644b80f2ece4e5ae212dc9ee70d7f56fadc93602/safetensors-0.4.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.2 MB)━━━━━━━━━━━━━━━━ 1.2/1.2 MB 17.8 MB/s eta 0:00:00a 0:00:01 Collecting sentencepiece (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a3/69/e96ef68261fa5b82379fdedb325ceaf1d353c6e839ec346d8244e0da5f2f/sentencepiece-0.2.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (1.3 MB)━━━━━━━━━━━━━━━━ 1.3/1.3 MB 14.4 MB/s eta 0:00:00a 0:00:01 Collecting regex (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/70/70/fea4865c89a841432497d1abbfd53878513b55c6543245fabe31cf8df0b8/regex-2024.5.15-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (774 kB)━━━━━━━━━━━━━━━━ 774.7/774.7 kB 15.3 MB/s eta 0:00:00a 0:00:01 Collecting addict (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/6a/00/b08f23b7d7e1e14ce01419a467b583edbb93c6cdb8654e54a9cc579cd61f/addict-2.4.0-py3-none-any.whl (3.8 kB) Collecting ml-dtypes (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/50/96/13d7c3cc82d5ef597279216cf56ff461f8b57e7096a3ef10246a83ca80c0/ml_dtypes-0.4.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (2.2 MB)━━━━━━━━━━━━━━━━ 2.2/2.2 MB 11.9 MB/s eta 0:00:00a 0:00:01 Collecting pyctcdecode (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/a5/8a/93e2118411ae5e861d4f4ce65578c62e85d0f1d9cb389bd63bd57130604e/pyctcdecode-0.5.0-py2.py3-none-any.whl (39 kB) Collecting jieba (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c6/cb/18eeb235f833b726522d7ebed54f2278ce28ba9438e3135ab0278d9792a2/jieba-0.42.1.tar.gz (19.2 MB)━━━━━━━━━━━━━━━━ 19.2/19.2 MB 16.5 MB/s eta 0:00:0000:0100:01Preparing metadata (setup.py) ... done Collecting pytest7.2.0 (from mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/67/68/a5eb36c3a8540594b6035e6cdae40c1ef1b6a2bfacbecc3d1a544583c078/pytest-7.2.0-py3-none-any.whl (316 kB)━━━━━━━━━━━━━━━━ 316.8/316.8 kB 16.7 MB/s eta 0:00:00 Requirement already satisfied: attrs19.2.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (23.2.0) Requirement already satisfied: iniconfig in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (2.0.0) Requirement already satisfied: packaging in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (23.2) Requirement already satisfied: pluggy2.0,0.12 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (1.5.0) Requirement already satisfied: exceptiongroup1.0.0rc8 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (1.2.0) Requirement already satisfied: tomli1.0.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pytest7.2.0-mindnlp) (2.0.1) Requirement already satisfied: filelock in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets-mindnlp) (3.15.3) Requirement already satisfied: numpy1.17 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets-mindnlp) (1.26.4) Collecting pyarrow15.0.0 (from datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/87/60/cc0645eb4ef73f88847e40a7f9d238bae6b7409d6c1f6a5d200d8ade1f09/pyarrow-16.1.0-cp39-cp39-manylinux_2_28_aarch64.whl (38.1 MB)━━━━━━━━━━━━━━━━ 38.1/38.1 MB 14.2 MB/s eta 0:00:0000:0100:01 Collecting pyarrow-hotfix (from datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/e4/f4/9ec2222f5f5f8ea04f66f184caafd991a39c8782e31f5b0266f101cb68ca/pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB) Requirement already satisfied: dill0.3.9,0.3.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets-mindnlp) (0.3.8) Requirement already satisfied: pandas in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from datasets-mindnlp) (2.2.2) Collecting xxhash (from datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/7c/b9/93f860969093d5d1c4fa60c75ca351b212560de68f33dc0da04c89b7dc1b/xxhash-3.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (220 kB)━━━━━━━━━━━━━━━━ 220.6/220.6 kB 15.6 MB/s eta 0:00:00 Collecting multiprocess (from datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/da/d9/f7f9379981e39b8c2511c9e0326d212accacb82f12fbfdc1aa2ce2a7b2b6/multiprocess-0.70.16-py39-none-any.whl (133 kB)━━━━━━━━━━━━━━━━ 133.4/133.4 kB 15.8 MB/s eta 0:00:00 Collecting fsspec2024.5.0,2023.1.0 (from fsspec[http]2024.5.0,2023.1.0-datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ba/a3/16e9fe32187e9c8bc7f9b7bcd9728529faa725231a0c96f2f98714ff2fc5/fsspec-2024.5.0-py3-none-any.whl (316 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astunparse1.6.3 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from mindspore-mindnlp) (1.6.3) Collecting pygtrie3.0,2.1 (from pyctcdecode-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ec/cd/bd196b2cf014afb1009de8b0f05ecd54011d881944e62763f3c1b1e8ef37/pygtrie-2.5.0-py3-none-any.whl (25 kB) Collecting hypothesis7,6.14 (from pyctcdecode-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/ae/ea/526a7a629fcf6c78a1a6d37f988ca7e02e5b5785ec4de8a194deb40529f4/hypothesis-6.104.2-py3-none-any.whl (462 kB)━━━━━━━━━━━━━━━━ 462.4/462.4 kB 14.4 MB/s eta 0:00:00 Requirement already satisfied: six in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from asttokens2.0.4-mindspore-mindnlp) (1.16.0) Requirement already satisfied: wheel1.0,0.23.0 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from astunparse1.6.3-mindspore-mindnlp) (0.43.0) Collecting aiosignal1.1.2 (from aiohttp-datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/76/ac/a7305707cb852b7e16ff80eaf5692309bde30e2b1100a1fcacdc8f731d97/aiosignal-1.3.1-py3-none-any.whl (7.6 kB) Collecting frozenlist1.1.1 (from aiohttp-datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/57/15/172af60c7e150a1d88ecc832f2590721166ae41eab582172fe1e9844eab4/frozenlist-1.4.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (239 kB)━━━━━━━━━━━━━━━━ 239.4/239.4 kB 17.1 MB/s eta 0:00:00 Collecting multidict7.0,4.5 (from aiohttp-datasets-mindnlp)Downloading https://pypi.tuna.tsinghua.edu.cn/packages/d0/10/2ff646c471e84af25fe8111985ffb8ec85a3f6e1ade8643bfcfcc0f4d2b1/multidict-6.0.5-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (125 kB)━━━━━━━━━━━━━━━━ 125.9/125.9 kB 31.0 MB/s eta 0:00:00 Collecting yarl2.0,1.0 (from aiohttp-datasets-mindnlp)Downloading 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/home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas-datasets-mindnlp) (2.9.0.post0) Requirement already satisfied: pytz2020.1 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas-datasets-mindnlp) (2024.1) Requirement already satisfied: tzdata2022.7 in /home/nginx/miniconda/envs/jupyter/lib/python3.9/site-packages (from pandas-datasets-mindnlp) (2024.1) Building wheels for collected packages: jiebaBuilding wheel for jieba (setup.py) ... doneCreated wheel for jieba: filenamejieba-0.42.1-py3-none-any.whl size19314459 sha256352f23b7dc8b4bade2f918165e055bc707601544400a4918136ba69f220ce9f6Stored in directory: /home/nginx/.cache/pip/wheels/1a/76/68/b6d79c4db704bb18d54f6a73ab551185f4711f9730c0c15d97 Successfully built jieba 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, mindnlpAttempting uninstall: pytestFound existing installation: pytest 8.0.0Uninstalling pytest-8.0.0:Successfully uninstalled pytest-8.0.0Attempting uninstall: fsspecFound existing installation: fsspec 2024.6.0Uninstalling fsspec-2024.6.0:Successfully uninstalled fsspec-2024.6.0 Successfully installed addict-2.4.0 aiohttp-3.9.5 aiosignal-1.3.1 async-timeout-4.0.3 datasets-2.20.0 evaluate-0.4.2 frozenlist-1.4.1 fsspec-2024.5.0 hypothesis-6.104.2 jieba-0.42.1 mindnlp-0.3.1 ml-dtypes-0.4.0 multidict-6.0.5 multiprocess-0.70.16 pyarrow-16.1.0 pyarrow-hotfix-0.6 pyctcdecode-0.5.0 pygtrie-2.5.0 pytest-7.2.0 regex-2024.5.15 safetensors-0.4.3 sentencepiece-0.2.0 sortedcontainers-2.4.0 tokenizers-0.19.1 xxhash-3.4.1 yarl-1.9.4[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: contactmindspore.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 1. 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 pathself._labels, self._text_a [], []self._load() ​def _load(self):with open(self.path, r, encodingutf-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_len64, batch_size32, shuffleTrue):is_ascend mindspore.get_context(device_target) Ascendcolumn_names [label, text_a]dataset GeneratorDataset(source, column_namescolumn_names, shuffleshuffle)# transformstype_cast_op transforms.TypeCast(mindspore.int32)def tokenize_and_pad(text):if is_ascend:tokenized tokenizer(text, paddingmax_length, truncationTrue, max_lengthmax_seq_len)else:tokenized tokenizer(text)return tokenized[input_ids], tokenized[attention_mask]# map dataset dataset dataset.map(operationstokenize_and_pad, input_columnstext_a, output_columns[input_ids, attention_mask]) dataset dataset.map(operations[type_cast_op], input_columnslabel, output_columnslabels)# batch datasetif 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 [00:0000:00, 3.05kB/s]━107k/0.00 [00:0500:00, 36.3kB/s]━263k/0.00 [00:1500:00, 10.2kB/s]━━━━━━━━━━━━━━━━━━━━━ 624/? [00:0000:00, 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, shuffleFalse) 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], dtypeInt64, 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], dtypeInt64, 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], dtypeInt32, 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_labels3) model auto_mixed_precision(model, O1) ​ optimizer nn.Adam(model.trainable_params(), learning_rate2e-5) (), learning_rate2e-5) 输出 100%━━━━━━━━━━━━━━━━━━ 392M/392M [00:5300:00, 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_pathcheckpoint, ckpt_namebert_emotect, epochs1, keep_checkpoint_max2) best_model_cb BestModelCallback(save_pathcheckpoint, ckpt_namebert_emotect_best, auto_loadTrue) # 构建训练器 trainer Trainer(networkmodel, train_datasetdataset_train,eval_datasetdataset_val, metricsmetric,epochs5, optimizeroptimizer, callbacks[ckpoint_cb, best_model_cb])%%time # start training trainer.run(tgt_columnslabels) 输出 The train will start from the checkpoint saved in checkpoint. Epoch  0: 100%━━━━━━━━━━━━━━ 302/302 [04:0700:00,  2.25s/it, loss0.3460012] Checkpoint: bert_emotect_epoch_0.ckpt has been saved in epoch: 0. Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:0700:00,  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 [02:3800:00,  1.95it/s, loss0.19017023] Checkpoint: bert_emotect_epoch_1.ckpt has been saved in epoch: 1. Evaluate: 100%━━━━━━━━━━━━━━ 34/34 [00:0500:00,  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 [02:4000:00,  1.92it/s, loss0.12662967] 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 [00:0400:00,  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 [02:4000:00,  1.92it/s, loss0.08593981] 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 [00:0400:00,  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 [02:4100:00,  1.92it/s, loss0.05900709] 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 [00:0400:00,  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(networkmodel, eval_datasetdataset_test, metricsmetric) evaluator.run(tgt_columnslabels) 输出 Evaluate: 100%━━━━━━━━━━━━━━ 33/33 [00:0800:00,  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, labelNone):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 finputs: {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