Mac安装Anaconda

文章目录1.anaconda install2.model1.anaconda installhttps://anaconda.com/download/successhttps://www.anaconda.com/docs/getting-started/anaconda/install#macos-linux-installation# 打开终端进入下载目录假设安装包下载到 Downloads 目录 cd ~/Downloads # 给安装脚本添加执行权限 chmod x Anaconda3-xxxx.xx-MacOSX-x86_64.sh # 请将 Anaconda3-xxxx.xx-MacOSX-x86_64.sh 替换为实际下载的文件名 # 执行安装脚本 ./Anaconda3-xxxx.xx-MacOSX-x86_64.sh # 请将 Anaconda3-xxxx.xx-MacOSX-x86_64.sh 替换为实际下载的文件名 # 安装过程中会提示你阅读许可协议按空格键翻页阅读完后输入 yes 同意协议 # 之后会提示你选择安装路径默认即可按回车键确认 # 最后会询问是否初始化 Anaconda输入 yes 完成初始化 conda --version2.modelhttps://www.modelscope.cn/homehttps://github.com/modelscope/modelscope/blob/master/README_zh.md#!/usr/bin/python # -*- coding: UTF-8 -*- conda create -n modelscope python3.8 conda activate modelscope (modelscope) ➜ modelscope python modelscope_demo.py 2025-07-18 17:16:49,118 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0 Downloading Model from https://www.modelscope.cn to directory: /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom 2025-07-18 17:16:50,390 - modelscope - WARNING - Model revision not specified, use revision: v1.0.0 2025-07-18 17:16:50,590 - modelscope - INFO - initiate model from /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom 2025-07-18 17:16:50,590 - modelscope - INFO - initiate model from location /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom. 2025-07-18 17:16:50,592 - modelscope - INFO - initialize model from /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom 2025-07-18 17:16:52,224 - modelscope - INFO - head has no _keys_to_ignore_on_load_missing 2025-07-18 17:16:52,632 - modelscope - INFO - All model checkpoint weights were used when initializing ModelForTokenClassificationWithCRF. 2025-07-18 17:16:52,632 - modelscope - INFO - All the weights of ModelForTokenClassificationWithCRF were initialized from the model checkpoint If your task is similar to the task the model of the checkpoint was trained on, you can already use ModelForTokenClassificationWithCRF for predictions without further training. 2025-07-18 17:16:52,635 - modelscope - WARNING - No preprocessor field found in cfg. 2025-07-18 17:16:52,635 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2025-07-18 17:16:52,635 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {model_dir: /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom}. trying to build by task and model information. 2025-07-18 17:16:52,641 - modelscope - INFO - cuda is not available, using cpu instead. 2025-07-18 17:16:52,642 - modelscope - WARNING - No preprocessor field found in cfg. 2025-07-18 17:16:52,642 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2025-07-18 17:16:52,642 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {model_dir: /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom, sequence_length: 512}. trying to build by task and model information. 2025-07-18 17:16:52,650 - modelscope - WARNING - No preprocessor field found in cfg. 2025-07-18 17:16:52,650 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file. 2025-07-18 17:16:52,650 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {model_dir: /Users/wgg/.cache/modelscope/hub/models/damo/nlp_raner_named-entity-recognition_chinese-base-ecom, sequence_length: 512}. trying to build by task and model information. * Serving Flask app modelscope_demo * Debug mode: off WARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead. * Running on all addresses (0.0.0.0) * Running on http://127.0.0.1:9080 * Running on http://10.168.88.14:9080 Press CTRLC to quit /Users/wgg/anaconda3/envs/modelscope/lib/python3.8/site-packages/transformers/modeling_utils.py:1161: FutureWarning: The device argument is deprecated and will be removed in v5 of Transformers. warnings.warn( --------ner------- {output: [{type: HP, start: 0, end: 2, prob: 0.6007019, span: 苹果}, {type: HC, start: 10, end: 16, prob: 0.16767623, span: iPhone}, {type: XH, start: 17, end: 19, prob: 0.6483761, span: 15}]} --------ner------- curl -X POST http://0.0.0.0:9080/ner \ -H Content-Type: application/json \#!/usr/bin/env pythonimportwarnings warnings.filterwarnings(ignore,categoryUserWarning,modulemodelscope.utils.plugins,messagepkg_resources is deprecated as an API)# pip install modelscope flask numpy pandas datasets pyarrow addict -i https://pypi.tuna.tsinghua.edu.cn/simplefromflaskimportFlask,request,jsonifyfrommodelscope.pipelinesimportpipelinefrommodelscope.utils.constantimportTasksfrommodelscope.modelsimportModelfrommodelscope.preprocessorsimportTextRankingTransformersPreprocessorfrommodelscope.preprocessorsimportTokenClassificationTransformersPreprocessorimportjsonimportnumpyasnpimportpandasaspd# 1.命名实体识别任务defsimple()-None:# 创建命名实体识别任务的管道ner_pipelinepipeline(Tasks.named_entity_recognition,modeldamo/nlp_raner_named-entity-recognition_chinese-base-ecom)# 输入文本text苹果公司发布了新款 iPhone 15。# 执行命名实体识别resultner_pipeline(text)print(--------simple-------)print(result)# 2. 文本分词任务deftokenize()-None:# 加载预训练模型model_iddamo/nlp_structbert_word-segmentation_chinese-base-ecommercemodelModel.from_pretrained(model_id)# 创建预处理器preprocessorTokenClassificationTransformersPreprocessor(model.model_dir)# 创建分词任务的管道seg_pipelinepipeline(taskTasks.word_segmentation,modelmodel,preprocessorpreprocessor)# 输入文本text苹果公司发布了新款 iPhone 15。# 执行分词任务resultseg_pipeline(text)print(--------tokenize-------)print(result)appFlask(__name__)# 创建命名实体识别任务的管道ner_pipeline_httppipeline(Tasks.named_entity_recognition,modeldamo/nlp_raner_named-entity-recognition_chinese-base-ecom)app.route(/ner,methods[POST])defner():datarequest.get_json()textdata.get(text,)resultner_pipeline_http(text)print(result)returnjsonify(result)if__name____main__:#simple()#tokenize()#app.run(debugTrue)app.run(host0.0.0.0,port9080)