【HuggingFace Transformer库学习笔记】基础组件学习：Pipeline_何在huggingface查pipeline使用教程

一、Transformer基础知识

pip install transformers datasets evaluate peft accelerate gradio optimum sentencepiece
pip install jupyterlab scikit-learn pandas matplotlib tensorboard nltk rouge
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在host文件里添加途中信息，可以避免运行代码下载模型时候报错。

Transformers测试

#导入gradio
import gradio as gr
#导入transformersi相关包
from transformers import *
#通过Interface)加载pipeline并启动文本分类服务
gr.Interface.from_pipeline(pipeline("text-classification", model="uer/roberta-base-finetuned-dianping-chinese")).launch()
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1、基础组件——pipeline

导入包

from transformers.pipelines import SUPPORTED_TASKS
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查看pipeline支持的任务类型

# 查看SUPPORTED_TASK所有可支持的任务
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': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForDepthEstimation'>,), 'default': {'model': {'pt': ('Intel/dpt-large', 'e93beec')}}, 'type': 'image'}), ('video-classification', {'impl': <class 'transformers.pipelines.video_classification.VideoClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForVideoClassification'>,), 'default': {'model': {'pt': ('MCG-NJU/videomae-base-finetuned-kinetics', '4800870')}}, 'type': 'video'}), ('mask-generation', {'impl': <class 'transformers.pipelines.mask_generation.MaskGenerationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForMaskGeneration'>,), 'default': {'model': {'pt': ('facebook/sam-vit-huge', '997b15')}}, 'type': 'multimodal'}), ('image-to-image', {'impl': <class 'transformers.pipelines.image_to_image.ImageToImagePipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForImageToImage'>,), 'default': {'model': {'pt': ('caidas/swin2SR-classical-sr-x2-64', '4aaedcb')}}, 'type': 'image'})])

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查看pipeline都支持哪些任务和实现

for k, v in SUPPORTED_TASKS.items():
    print(k, v)     # k：任务名称，v：任务的实现。tf:tensorflow模型，pt:pytorch模型

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': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForDepthEstimation'>,), 'default': {'model': {'pt': ('Intel/dpt-large', 'e93beec')}}, 'type': 'image'}
video-classification {'impl': <class 'transformers.pipelines.video_classification.VideoClassificationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForVideoClassification'>,), 'default': {'model': {'pt': ('MCG-NJU/videomae-base-finetuned-kinetics', '4800870')}}, 'type': 'video'}
mask-generation {'impl': <class 'transformers.pipelines.mask_generation.MaskGenerationPipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForMaskGeneration'>,), 'default': {'model': {'pt': ('facebook/sam-vit-huge', '997b15')}}, 'type': 'multimodal'}
image-to-image {'impl': <class 'transformers.pipelines.image_to_image.ImageToImagePipeline'>, 'tf': (), 'pt': (<class 'transformers.models.auto.modeling_auto.AutoModelForImageToImage'>,), 'default': {'model': {'pt': ('caidas/swin2SR-classical-sr-x2-64', '4aaedcb')}}, 'type': 'image'}    
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导入包

from transformers import pipeline
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根据任务类型直接创建pipeline，默认都是英文模型
加载模型

pipe = pipeline("text-classification", model="./model/distilbert-base-uncased-finetuned-sst-2-english")
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测试分类效果

pipe(["very good!", "vary bad!", "not bad", "just so so", "oh, damn!"])

[{'label': 'POSITIVE', 'score': 0.9998525381088257},
 {'label': 'NEGATIVE', 'score': 0.9991207718849182},
 {'label': 'POSITIVE', 'score': 0.9995881915092468},
 {'label': 'POSITIVE', 'score': 0.9887603521347046},
 {'label': 'NEGATIVE', 'score': 0.5632225871086121}]
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推理测试

from transformers import *

# 这种方式，必须同时指定model和tokenizer
model = AutoModelForSequenceClassification.from_pretrained("uer/roberta-base-finetuned-dianping-chinese")
tokenizer = AutoTokenizer.from_pretrained("uer/roberta-base-finetuned-dianping-chinese")
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer, device_map="auto")		# GPU自动分配

Model config DistilBertConfig {
  "_name_or_path": "model/roberta-base-finetuned-dianping-chinese",
  "activation": "gelu",
  "architectures": [
    "DistilBertForSequenceClassification"
  ],
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "finetuning_task": "sst-2",
  "hidden_dim": 3072,
  "id2label": {
    "0": "NEGATIVE",
    "1": "POSITIVE"
  },
  "initializer_range": 0.02,
  "label2id": {
    "NEGATIVE": 0,
    "POSITIVE": 1
  },
  "max_position_embeddings": 512,
  "model_type": "distilbert",
  "n_heads": 12,
  "n_layers": 6,
...
  "transformers_version": "4.35.2",
  "vocab_size": 30522
}
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测试效果

pipe(["我觉得不太行！", "一般般", "还凑合吧", "太强了！"])

[{'label': 'NEGATIVE', 'score': 0.5539911389350891},
 {'label': 'POSITIVE', 'score': 0.5317790508270264},
 {'label': 'NEGATIVE', 'score': 0.5028885006904602},
 {'label': 'POSITIVE', 'score': 0.8547790050506592}]
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速度测试

import torch
import time
times = []
for i in range(100):
    torch.cuda.synchronize()
    start = time.time()
    pipe("我觉得不太行！")
    torch.cuda.synchronize()
    end = time.time()
    times.append(end - start)
print(sum(times) / 100)

0.01336388111114502
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当想用知道怎么使用某个库时候，可以先实例化这个库，然后再查看对应信息去查找。
例如

qa_pipe = pipeline("question-answering", model="model/robert-base-chinese-extractive-qa")
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输入qa_pipe查看pipline

qa_pipe

<transformers.pipelines.question_answering.QuestionAnsweringPipeline at 0x7f40c65a75e0>
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再在代码界面上输入QuestionAnsweringPipeline，按住Ctrl进去查看示例，查看__call___方法

QuestionAnsweringPipeline
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测试

# question是问题，context是让模型根据context内容抽取可以回答问题的答案
qa_pipe(question="中国的首都是哪里？", context="北京是中国的政治和文化中心，上海是中国的经济中心")

{'score': 0.00011347973486408591, 'start': 0, 'end': 2, 'answer': '北京'}
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设置输出答案字长度

qa_pipe(question="中国的首都是哪里？", context="中国的首都是北京", max_answer_len=1)

{'score': 0.0022874099668115377, 'start': 6, 'end': 7, 'answer': '北'}

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解析pipline背后的实现过程

先初始化tokenizer和model

from transformers import *
import torch

tokenizer = AutoTokenizer.from_pretrained("model/roberta-base-finetuned-dianping-chinese")
model = AutoModelForSequenceClassification.from_pretrained("model/roberta-base-finetuned-dianping-chinese")

Model config DistilBertConfig {
  "_name_or_path": "model/roberta-base-finetuned-dianping-chinese",
  "activation": "gelu",
  "architectures": [
    "DistilBertForSequenceClassification"
  ],
  "attention_dropout": 0.1,
  "dim": 768,
  "dropout": 0.1,
  "finetuning_task": "sst-2",
  "hidden_dim": 3072,
  "id2label": {
    "0": "NEGATIVE",
    "1": "POSITIVE"
  },
  "initializer_range": 0.02,
  "label2id": {
    "NEGATIVE": 0,
    "POSITIVE": 1
  },
  "max_position_embeddings": 512,
  "model_type": "distilbert",
  "n_heads": 12,
  "n_layers": 6,
...
All model checkpoint weights were used when initializing DistilBertForSequenceClassification.
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输入文本并进行token化

input_text = "我觉得不太行！"
inputs = tokenizer(input_text, return_tensors="pt")
inputs

{'input_ids': tensor([[ 101, 1855,  100,  100, 1744, 1812, 1945, 1986,  102]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1]])}
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将inputs输入model

res = model(**inputs)
res

SequenceClassifierOutput(loss=None, logits=tensor([[2.1696e-01, 1.5108e-04]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)
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模型训练后，对最终全连接层的输出（logits）的最后一个维度进行归一化

logits = res.logits
logits = torch.softmax(logits, dim=-1)      # 对最后一个维度进行归一化
logits

tensor([[0.5540, 0.4460]], grad_fn=<SoftmaxBackward0>)
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根据最后一层的输出结果，找到概率最大的类别作为最终输出

pred = torch.argmax(logits).item()      # 通过取概率最大值对应类的下表，取对应的类别
pred

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查看一下0索引对应的类别

model.config.id2label       # model config里的id2label有的对应的类别信息

{0: 'NEGATIVE', 1: 'POSITIVE'}
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输出最终结果

result = model.config.id2label.get(pred)
result
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