做网站送白酒帝国网站系统做专题

当前位置： 首页 > news >正文

做网站送白酒,帝国网站系统做专题,网页制作代码html添加音乐,连接wordpress​​​​​​​ 目录 一、引言
二、pipeline库 2.1 概述 2.2 使用task实例化pipeline对象 2.2.1 基于task实例化“自动语音识别” 2.2.2 task列表 2.2.3 task默认模型 2.3 使用model实例化pipeline对象 2.3.1 基于model实例化“自动语音识别” 2.3.2 查看model与task… ​​​​​​​ 目录 一、引言  二、pipeline库 2.1 概述 2.2 使用task实例化pipeline对象 2.2.1 基于task实例化“自动语音识别” 2.2.2 task列表 2.2.3 task默认模型 2.3 使用model实例化pipeline对象 2.3.1 基于model实例化“自动语音识别” 2.3.2 查看model与task的对应关系 三、总结 一、引言  pipeline管道是huggingface transformers库中一种极简方式使用大模型推理的抽象将所有大模型分为语音Audio、计算机视觉Computer vision、自然语言处理NLP、多模态Multimodal等4大类28小类任务tasks。共计覆盖32万个模型 本文对pipeline进行整体介绍之后本专栏以每个task为主题分别介绍各种task使用方法。 二、pipeline库 2.1 概述 管道是一种使用模型进行推理的简单而好用的方法。这些管道是从库中抽象出大部分复杂代码的对象提供了专用于多项任务的简单 API包括命名实体识别、掩码语言建模、情感分析、特征提取和问答。在使用上主要有2种方法 使用task实例化pipeline对象使用model实例化pipeline对象 2.2 使用task实例化pipeline对象 2.2.1 基于task实例化“自动语音识别” 自动语音识别的task为automatic-speech-recognition import os os.environ[HF_ENDPOINT] https://hf-mirror.com os.environ[CUDA_VISIBLE_DEVICES] 2from transformers import pipelinespeech_file ./output_video_enhanced.mp3 pipe pipeline(taskautomatic-speech-recognition) result pipe(speech_file) print(result) 2.2.2 task列表 task共计28类按首字母排序列表如下直接替换2.2.1代码中的pipeline的task即可应用 audio-classification将返回一个AudioClassificationPipeline。automatic-speech-recognition将返回一个AutomaticSpeechRecognitionPipeline。depth-estimation将返回一个DepthEstimationPipeline。document-question-answering将返回一个DocumentQuestionAnsweringPipeline。feature-extraction将返回一个FeatureExtractionPipeline。fill-mask将返回一个FillMaskPipeline。image-classification将返回一个ImageClassificationPipeline。image-feature-extraction将返回一个ImageFeatureExtractionPipeline。image-segmentation将返回一个ImageSegmentationPipeline。image-to-image将返回一个ImageToImagePipeline。image-to-text将返回一个ImageToTextPipeline。mask-generation将返回一个MaskGenerationPipeline。object-detection将返回一个ObjectDetectionPipeline。question-answering将返回一个QuestionAnsweringPipeline。summarization将返回一个SummarizationPipeline。table-question-answering将返回一个TableQuestionAnsweringPipeline。text2text-generation将返回一个Text2TextGenerationPipeline。text-classification(sentiment-analysis可用别名)将返回一个 TextClassificationPipeline。text-generation将返回一个TextGenerationPipeline。text-to-audiotext-to-speech可用别名将返回一个TextToAudioPipeline。token-classification(ner可用别名)将返回一个TokenClassificationPipeline。translation将返回一个TranslationPipeline。translation_xx_to_yy将返回一个TranslationPipeline。video-classification将返回一个VideoClassificationPipeline。visual-question-answering将返回一个VisualQuestionAnsweringPipeline。zero-shot-classification将返回一个ZeroShotClassificationPipeline。zero-shot-image-classification将返回一个ZeroShotImageClassificationPipeline。zero-shot-audio-classification将返回一个ZeroShotAudioClassificationPipeline。zero-shot-object-detection将返回一个ZeroShotObjectDetectionPipeline。 2.2.3 task默认模型 针对每一个taskpipeline默认配置了模型可以通过pipeline源代码查看 SUPPORTED_TASKS {audio-classification: {impl: AudioClassificationPipeline,tf: (),pt: (AutoModelForAudioClassification,) if is_torch_available() else (),default: {model: {pt: (superb/wav2vec2-base-superb-ks, 372e048)}},type: audio,},automatic-speech-recognition: {impl: AutomaticSpeechRecognitionPipeline,tf: (),pt: (AutoModelForCTC, AutoModelForSpeechSeq2Seq) if is_torch_available() else (),default: {model: {pt: (facebook/wav2vec2-base-960h, 55bb623)}},type: multimodal,},text-to-audio: {impl: TextToAudioPipeline,tf: (),pt: (AutoModelForTextToWaveform, AutoModelForTextToSpectrogram) if is_torch_available() else (),default: {model: {pt: (suno/bark-small, 645cfba)}},type: text,},feature-extraction: {impl: FeatureExtractionPipeline,tf: (TFAutoModel,) if is_tf_available() else (),pt: (AutoModel,) if is_torch_available() else (),default: {model: {pt: (distilbert/distilbert-base-cased, 935ac13),tf: (distilbert/distilbert-base-cased, 935ac13),}},type: multimodal,},text-classification: {impl: TextClassificationPipeline,tf: (TFAutoModelForSequenceClassification,) if is_tf_available() else (),pt: (AutoModelForSequenceClassification,) if is_torch_available() else (),default: {model: {pt: (distilbert/distilbert-base-uncased-finetuned-sst-2-english, af0f99b),tf: (distilbert/distilbert-base-uncased-finetuned-sst-2-english, af0f99b),},},type: text,},token-classification: {impl: TokenClassificationPipeline,tf: (TFAutoModelForTokenClassification,) if is_tf_available() else (),pt: (AutoModelForTokenClassification,) if is_torch_available() else (),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: QuestionAnsweringPipeline,tf: (TFAutoModelForQuestionAnswering,) if is_tf_available() else (),pt: (AutoModelForQuestionAnswering,) if is_torch_available() else (),default: {model: {pt: (distilbert/distilbert-base-cased-distilled-squad, 626af31),tf: (distilbert/distilbert-base-cased-distilled-squad, 626af31),},},type: text,},table-question-answering: {impl: TableQuestionAnsweringPipeline,pt: (AutoModelForTableQuestionAnswering,) if is_torch_available() else (),tf: (TFAutoModelForTableQuestionAnswering,) if is_tf_available() else (),default: {model: {pt: (google/tapas-base-finetuned-wtq, 69ceee2),tf: (google/tapas-base-finetuned-wtq, 69ceee2),},},type: text,},visual-question-answering: {impl: VisualQuestionAnsweringPipeline,pt: (AutoModelForVisualQuestionAnswering,) if is_torch_available() else (),tf: (),default: {model: {pt: (dandelin/vilt-b32-finetuned-vqa, 4355f59)},},type: multimodal,},document-question-answering: {impl: DocumentQuestionAnsweringPipeline,pt: (AutoModelForDocumentQuestionAnswering,) if is_torch_available() else (),tf: (),default: {model: {pt: (impira/layoutlm-document-qa, 52e01b3)},},type: multimodal,},fill-mask: {impl: FillMaskPipeline,tf: (TFAutoModelForMaskedLM,) if is_tf_available() else (),pt: (AutoModelForMaskedLM,) if is_torch_available() else (),default: {model: {pt: (distilbert/distilroberta-base, ec58a5b),tf: (distilbert/distilroberta-base, ec58a5b),}},type: text,},summarization: {impl: SummarizationPipeline,tf: (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),pt: (AutoModelForSeq2SeqLM,) if is_torch_available() else (),default: {model: {pt: (sshleifer/distilbart-cnn-12-6, a4f8f3e), tf: (google-t5/t5-small, d769bba)}},type: text,},# This task is a special case as its parametrized by SRC, TGT languages.translation: {impl: TranslationPipeline,tf: (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),pt: (AutoModelForSeq2SeqLM,) if is_torch_available() else (),default: {(en, fr): {model: {pt: (google-t5/t5-base, 686f1db), tf: (google-t5/t5-base, 686f1db)}},(en, de): {model: {pt: (google-t5/t5-base, 686f1db), tf: (google-t5/t5-base, 686f1db)}},(en, ro): {model: {pt: (google-t5/t5-base, 686f1db), tf: (google-t5/t5-base, 686f1db)}},},type: text,},text2text-generation: {impl: Text2TextGenerationPipeline,tf: (TFAutoModelForSeq2SeqLM,) if is_tf_available() else (),pt: (AutoModelForSeq2SeqLM,) if is_torch_available() else (),default: {model: {pt: (google-t5/t5-base, 686f1db), tf: (google-t5/t5-base, 686f1db)}},type: text,},text-generation: {impl: TextGenerationPipeline,tf: (TFAutoModelForCausalLM,) if is_tf_available() else (),pt: (AutoModelForCausalLM,) if is_torch_available() else (),default: {model: {pt: (openai-community/gpt2, 6c0e608), tf: (openai-community/gpt2, 6c0e608)}},type: text,},zero-shot-classification: {impl: ZeroShotClassificationPipeline,tf: (TFAutoModelForSequenceClassification,) if is_tf_available() else (),pt: (AutoModelForSequenceClassification,) if is_torch_available() else (),default: {model: {pt: (facebook/bart-large-mnli, c626438),tf: (FacebookAI/roberta-large-mnli, 130fb28),},config: {pt: (facebook/bart-large-mnli, c626438),tf: (FacebookAI/roberta-large-mnli, 130fb28),},},type: text,},zero-shot-image-classification: {impl: ZeroShotImageClassificationPipeline,tf: (TFAutoModelForZeroShotImageClassification,) if is_tf_available() else (),pt: (AutoModelForZeroShotImageClassification,) if is_torch_available() else (),default: {model: {pt: (openai/clip-vit-base-patch32, f4881ba),tf: (openai/clip-vit-base-patch32, f4881ba),}},type: multimodal,},zero-shot-audio-classification: {impl: ZeroShotAudioClassificationPipeline,tf: (),pt: (AutoModel,) if is_torch_available() else (),default: {model: {pt: (laion/clap-htsat-fused, 973b6e5),}},type: multimodal,},image-classification: {impl: ImageClassificationPipeline,tf: (TFAutoModelForImageClassification,) if is_tf_available() else (),pt: (AutoModelForImageClassification,) if is_torch_available() else (),default: {model: {pt: (google/vit-base-patch16-224, 5dca96d),tf: (google/vit-base-patch16-224, 5dca96d),}},type: image,},image-feature-extraction: {impl: ImageFeatureExtractionPipeline,tf: (TFAutoModel,) if is_tf_available() else (),pt: (AutoModel,) if is_torch_available() else (),default: {model: {pt: (google/vit-base-patch16-224, 3f49326),tf: (google/vit-base-patch16-224, 3f49326),}},type: image,},image-segmentation: {impl: ImageSegmentationPipeline,tf: (),pt: (AutoModelForImageSegmentation, AutoModelForSemanticSegmentation) if is_torch_available() else (),default: {model: {pt: (facebook/detr-resnet-50-panoptic, fc15262)}},type: multimodal,},image-to-text: {impl: ImageToTextPipeline,tf: (TFAutoModelForVision2Seq,) if is_tf_available() else (),pt: (AutoModelForVision2Seq,) if is_torch_available() else (),default: {model: {pt: (ydshieh/vit-gpt2-coco-en, 65636df),tf: (ydshieh/vit-gpt2-coco-en, 65636df),}},type: multimodal,},object-detection: {impl: ObjectDetectionPipeline,tf: (),pt: (AutoModelForObjectDetection,) if is_torch_available() else (),default: {model: {pt: (facebook/detr-resnet-50, 2729413)}},type: multimodal,},zero-shot-object-detection: {impl: ZeroShotObjectDetectionPipeline,tf: (),pt: (AutoModelForZeroShotObjectDetection,) if is_torch_available() else (),default: {model: {pt: (google/owlvit-base-patch32, 17740e1)}},type: multimodal,},depth-estimation: {impl: DepthEstimationPipeline,tf: (),pt: (AutoModelForDepthEstimation,) if is_torch_available() else (),default: {model: {pt: (Intel/dpt-large, e93beec)}},type: image,},video-classification: {impl: VideoClassificationPipeline,tf: (),pt: (AutoModelForVideoClassification,) if is_torch_available() else (),default: {model: {pt: (MCG-NJU/videomae-base-finetuned-kinetics, 4800870)}},type: video,},mask-generation: {impl: MaskGenerationPipeline,tf: (),pt: (AutoModelForMaskGeneration,) if is_torch_available() else (),default: {model: {pt: (facebook/sam-vit-huge, 997b15)}},type: multimodal,},image-to-image: {impl: ImageToImagePipeline,tf: (),pt: (AutoModelForImageToImage,) if is_torch_available() else (),default: {model: {pt: (caidas/swin2SR-classical-sr-x2-64, 4aaedcb)}},type: image,}, } 2.3 使用model实例化pipeline对象 2.3.1 基于model实例化“自动语音识别” 如果不想使用task中默认的模型可以指定huggingface中的模型 import os os.environ[HF_ENDPOINT] https://hf-mirror.com os.environ[CUDA_VISIBLE_DEVICES] 2from transformers import pipelinespeech_file ./output_video_enhanced.mp3 #transcriber pipeline(taskautomatic-speech-recognition, modelopenai/whisper-medium) pipe pipeline(modelopenai/whisper-medium) result pipe(speech_file) print(result) 2.3.2 查看model与task的对应关系 可以登录https://huggingface.co/tasks查看 三、总结 本文为transformers之pipeline专栏的第0篇后面会以每个task为一篇共计讲述28个tasks的用法通过28个tasks的pipeline使用学习可以掌握语音、计算机视觉、自然语言处理、多模态乃至强化学习等30w个huggingface上的开源大模型。让你成为大模型领域的专家 期待您的3连关注如何还有时间欢迎阅读我的其他文章 《AI—工程篇》 AI智能体研发之路-工程篇一Docker助力AI智能体开发提效 AI智能体研发之路-工程篇二Dify智能体开发平台一键部署 AI智能体研发之路-工程篇三大模型推理服务框架Ollama一键部署 AI智能体研发之路-工程篇四大模型推理服务框架Xinference一键部署 AI智能体研发之路-工程篇五大模型推理服务框架LocalAI一键部署 《AI—模型篇》 AI智能体研发之路-模型篇一大模型训练框架LLaMA-Factory在国内网络环境下的安装、部署及使用 AI智能体研发之路-模型篇二DeepSeek-V2-Chat 训练与推理实战 AI智能体研发之路-模型篇三中文大模型开、闭源之争 AI智能体研发之路-模型篇四一文入门pytorch开发 AI智能体研发之路-模型篇五pytorch vs tensorflow框架DNN网络结构源码级对比 AI智能体研发之路-模型篇六【机器学习】基于tensorflow实现你的第一个DNN网络 AI智能体研发之路-模型篇七【机器学习】基于YOLOv10实现你的第一个视觉AI大模型 AI智能体研发之路-模型篇八【机器学习】Qwen1.5-14B-Chat大模型训练与推理实战 AI智能体研发之路-模型篇九【机器学习】GLM4-9B-Chat大模型/GLM-4V-9B多模态大模型概述、原理及推理实战 《AI—Transformers应用》 【AI大模型】Transformers大模型库一Tokenizer 【AI大模型】Transformers大模型库二AutoModelForCausalLM 【AI大模型】Transformers大模型库三特殊标记special tokens 【AI大模型】Transformers大模型库四AutoTokenizer 【AI大模型】Transformers大模型库五AutoModel、Model Head及查看模型结构