系列篇章????
AI大模型探索之路-训练篇1：大语言模型微调基础认知
AI大模型探索之路-训练篇2：大语言模型预训练基础认知
AI大模型探索之路-训练篇3：大语言模型全景解读
AI大模型探索之路-训练篇4：大语言模型训练数据集概览
AI大模型探索之路-训练篇5：大语言模型预训练数据准备-词元化
AI大模型探索之路-训练篇6：大语言模型预训练数据准备-预处理
AI大模型探索之路-训练篇7：大语言模型Transformer库之HuggingFace介绍
AI大模型探索之路-训练篇8：大语言模型Transformer库-预训练流程编码体验
目录
- 系列篇章????
- 一、Pipeline的简介
- 二、Pipeline支持的任务类型
- 三、Pipeline的使用
- 1、Pipeline的创建与使用方式
- 2、预先加载模型，再创建Pipeline
- 3、CPU和GPU推理对比
- 4、确定Pipeline参数的使用
- 四、Pipeline背后的工作
- 1、导入依赖，创建模型
- 2、分词处理、生成模型输入
- 3、模型推理
- 4、获取原始分数、再归一化处理
- 5、查看概率最高的类别索引
- 6、查看标签映射
- 7、根据我们得到的索引类别，从标签映射中取出结果
- 8、Pipeline使用完整代码样例如下
- 五、使用Pipeline优化
- 总结
一、Pipeline的简介
在人工智能和机器学习领域，Pipeline是一种设计模式，它将多个处理步骤串联起来，形成一个有序的、自动化的工作流程。这一概念在自然语言处理（NLP）尤其重要，因为NLP任务通常涉及多个阶段，如文本清洗、特征提取、模型训练或预测等。Pipeline的设计旨在减少重复代码、提高代码的可维护性，并优化整个处理流程的效率。
官网API地址：https://huggingface.co/docs/transformers/main_classes/pipelines
二、Pipeline支持的任务类型
Pipeline能够支持多种机器学习任务，包括但不限于：
1）文本分类：将文本分配到预定义的类别。
2）情感分析：判断文本表达的情绪倾向。
3）机器翻译：将一种语言的文本转换为另一种语言。
4）问答系统：针对问题提供准确答案。
5）文本摘要：生成文本的简短摘要。
6）语言模型生成：基于语言模型生成新的文本内容。
在Python中，可以通过以下方式查询transformers库支持的所有任务类型：
from transformers.pipelines import SUPPORTED_TASKS
print(SUPPORTED_TASKS.items())
dict_items([('audio-classification', {'impl': <class 'transformers.pipelines.audio_classification.AudioClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForAudioClassification'>,), 'default': {'model': {'pt': ('superb/wav2vec2-base-superb-ks', '372e048')}}, 'type': 'audio'}), ('automatic-speech-recognition', {'impl': <class 'transformers.pipelines.automatic_speech_recognition.AutomaticSpeechRecognitionPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForCTC'>, <class 'transformers.models.auto.modeling_auto.AutoModelForSpeechSeq2Seq'>), 'default': {'model': {'pt': ('facebook/wav2vec2-base-960h', '55bb623')}}, 'type': 'multimodal'}), ('text-to-audio', {'impl': <class 'transformers.pipelines.text_to_audio.TextToAudioPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForTextToWaveform'>, <class 'transformers.models.auto.modeling_auto.AutoModelForTextToSpectrogram'>), 'default': {'model': {'pt': ('suno/bark-small', '645cfba')}}, 'type': 'text'}), ('feature-extraction', {'impl': <class 'transformers.pipelines.feature_extraction.FeatureExtractionPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModel'>,), 'default': {'model': {'pt': ('distilbert-base-cased', '935ac13'), 'tf': ('distilbert-base-cased', '935ac13')}}, 'type': 'multimodal'}), ('text-classification', {'impl': <class 'transformers.pipelines.text_classification.TextClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>,), 'default': {'model': {'pt': ('distilbert-base-uncased-finetuned-sst-2-english', 'af0f99b'), 'tf': ('distilbert-base-uncased-finetuned-sst-2-english', 'af0f99b')}}, 'type': 'text'}), ('token-classification', {'impl': <class 'transformers.pipelines.token_classification.TokenClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForTokenClassification'>,), 'default': {'model': {'pt': ('dbmdz/bert-large-cased-finetuned-conll03-english', 'f2482bf'), 'tf': ('dbmdz/bert-large-cased-finetuned-conll03-english', 'f2482bf')}}, 'type': 'text'}), ('question-answering', {'impl': <class 'transformers.pipelines.question_answering.QuestionAnsweringPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForQuestionAnswering'>,), 'default': {'model': {'pt': ('distilbert-base-cased-distilled-squad', '626af31'), 'tf': ('distilbert-base-cased-distilled-squad', '626af31')}}, 'type': 'text'}), ('table-question-answering', {'impl': <class 'transformers.pipelines.table_question_answering.TableQuestionAnsweringPipeline'>, 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForTableQuestionAnswering'>,), 'tf': (), 'default': {'model': {'pt': ('google/tapas-base-finetuned-wtq', '69ceee2'), 'tf': ('google/tapas-base-finetuned-wtq', '69ceee2')}}, 'type': 'text'}), ('visual-question-answering', {'impl': <class 'transformers.pipelines.visual_question_answering.VisualQuestionAnsweringPipeline'>, 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForVisualQuestionAnswering'>,), 'tf': (), 'default': {'model': {'pt': ('dandelin/vilt-b32-finetuned-vqa', '4355f59')}}, 'type': 'multimodal'}), ('document-question-answering', {'impl': <class 'transformers.pipelines.document_question_answering.DocumentQuestionAnsweringPipeline'>, 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForDocumentQuestionAnswering'>,), 'tf': (), 'default': {'model': {'pt': ('impira/layoutlm-document-qa', '52e01b3')}}, 'type': 'multimodal'}), ('fill-mask', {'impl': <class 'transformers.pipelines.fill_mask.FillMaskPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForMaskedLM'>,), 'default': {'model': {'pt': ('distilroberta-base', 'ec58a5b'), 'tf': ('distilroberta-base', 'ec58a5b')}}, 'type': 'text'}), ('summarization', {'impl': <class 'transformers.pipelines.text2text_generation.SummarizationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSeq2SeqLM'>,), 'default': {'model': {'pt': ('sshleifer/distilbart-cnn-12-6', 'a4f8f3e'), 'tf': ('t5-small', 'd769bba')}}, 'type': 'text'}), ('translation', {'impl': <class 'transformers.pipelines.text2text_generation.TranslationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSeq2SeqLM'>,), 'default': {('en', 'fr'): {'model': {'pt': ('t5-base', '686f1db'), 'tf': ('t5-base', '686f1db')}}, ('en', 'de'): {'model': {'pt': ('t5-base', '686f1db'), 'tf': ('t5-base', '686f1db')}}, ('en', 'ro'): {'model': {'pt': ('t5-base', '686f1db'), 'tf': ('t5-base', '686f1db')}}}, 'type': 'text'}), ('text2text-generation', {'impl': <class 'transformers.pipelines.text2text_generation.Text2TextGenerationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSeq2SeqLM'>,), 'default': {'model': {'pt': ('t5-base', '686f1db'), 'tf': ('t5-base', '686f1db')}}, 'type': 'text'}), ('text-generation', {'impl': <class 'transformers.pipelines.text_generation.TextGenerationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForCausalLM'>,), 'default': {'model': {'pt': ('gpt2', '6c0e608'), 'tf': ('gpt2', '6c0e608')}}, 'type': 'text'}), ('zero-shot-classification', {'impl': <class 'transformers.pipelines.zero_shot_classification.ZeroShotClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSequenceClassification'>,), 'default': {'model': {'pt': ('facebook/bart-large-mnli', 'c626438'), 'tf': ('roberta-large-mnli', '130fb28')}, 'config': {'pt': ('facebook/bart-large-mnli', 'c626438'), 'tf': ('roberta-large-mnli', '130fb28')}}, 'type': 'text'}), ('zero-shot-image-classification', {'impl': <class 'transformers.pipelines.zero_shot_image_classification.ZeroShotImageClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForZeroShotImageClassification'>,), 'default': {'model': {'pt': ('openai/clip-vit-base-patch32', 'f4881ba'), 'tf': ('openai/clip-vit-base-patch32', 'f4881ba')}}, 'type': 'multimodal'}), ('zero-shot-audio-classification', {'impl': <class 'transformers.pipelines.zero_shot_audio_classification.ZeroShotAudioClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModel'>,), 'default': {'model': {'pt': ('laion/clap-htsat-fused', '973b6e5')}}, 'type': 'multimodal'}), ('conversational', {'impl': <class 'transformers.pipelines.conversational.ConversationalPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForSeq2SeqLM'>, <class 'transformers.models.auto.modeling_auto.AutoModelForCausalLM'>), 'default': {'model': {'pt': ('microsoft/DialoGPT-medium', '8bada3b'), 'tf': ('microsoft/DialoGPT-medium', '8bada3b')}}, 'type': 'text'}), ('image-classification', {'impl': <class 'transformers.pipelines.image_classification.ImageClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForImageClassification'>,), 'default': {'model': {'pt': ('google/vit-base-patch16-224', '5dca96d'), 'tf': ('google/vit-base-patch16-224', '5dca96d')}}, 'type': 'image'}), ('image-segmentation', {'impl': <class 'transformers.pipelines.image_segmentation.ImageSegmentationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForImageSegmentation'>, <class 'transformers.models.auto.modeling_auto.AutoModelForSemanticSegmentation'>), 'default': {'model': {'pt': ('facebook/detr-resnet-50-panoptic', 'fc15262')}}, 'type': 'multimodal'}), ('image-to-text', {'impl': <class 'transformers.pipelines.image_to_text.ImageToTextPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForVision2Seq'>,), 'default': {'model': {'pt': ('ydshieh/vit-gpt2-coco-en', '65636df'), 'tf': ('ydshieh/vit-gpt2-coco-en', '65636df')}}, 'type': 'multimodal'}), ('object-detection', {'impl': <class 'transformers.pipelines.object_detection.ObjectDetectionPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForObjectDetection'>,), 'default': {'model': {'pt': ('facebook/detr-resnet-50', '2729413')}}, 'type': 'multimodal'}), ('zero-shot-object-detection', {'impl': <class 'transformers.pipelines.zero_shot_object_detection.ZeroShotObjectDetectionPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForZeroShotObjectDetection'>,), 'default': {'model': {'pt': ('google/owlvit-base-patch32', '17740e1')}}, 'type': 'multimodal'}), ('depth-estimation', {'impl': <class 'transformers.pipelines.depth_estimation.DepthEstimationPipeline'>, 'tf': (