text 模型 【人工智能】Transformers之Pipeline(六):图像分类(image-classification) 人工智能classificationimagepipeline模型 pipeline(管道)是huggingface transformers库中一种极简方式使用大模型推理的抽象,将所有大模型分为音频(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...
pipe = pipeline("text-classification") 指定任务类型,再指定模型,创建基于指定模型的Pipeline pipe = pipeline("text-classification", model="uer/roberta-base-finetuned-dianping-chinese") 预先加载模型,再创建Pipeline # 这种方式,必须同时指定model和tokenizermodel = AutoModelForSequenceClassification.from_pretrai...
"text-classification"("sentiment-analysis"可用别名):将返回一个TextClassificationPipeline。 "text-generation":将返回一个TextGenerationPipeline:。 "text-to-audio"("text-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。 "tra...
"""pipeline是Hugging Face旗下的另一个端到端模型库,它的作用是使得开发者能够”一键式“调用模型来完成baseline任务,这个过程中pipeline会直接调用成熟的经典模型,在无需任何训练微调操作下帮助开发者得到预期的输出结果。pipeline支持的task如下"feature-extraction": will return a FeatureExtractionPipeline."text-cla...
Text Classification pipelines are a sequence of tasks needed to be performed to classify documents into a set of predefined categories. The pre-processing phase (before training) of these pipelines involve different ways of transforming and manipulating the documents for the next (learning) phase. ...
"text-classification"("sentiment-analysis"可用别名):将返回一个TextClassificationPipeline。 "text-generation":将返回一个TextGenerationPipeline:。 "text-to-audio"("text-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。
pipe = pipeline("text-classification", model=model, tokenizer=tokenizer) 3.选择CPU/GPU 在代码中加入 ”device“选项,当使用GPU推理时,device=0 or device=1 or device=2(数字为选择第几张显卡);当使用CPU推理时,device=cpu. pipe = pipeline("text-classification", model="uer/roberta-base-finetuned-...
pipe = pipeline("text-classification") pipe("very good!") # [{'label': 'POSITIVE', 'score': 0.9998525381088257}] 1. 2. 3. 4. 5. 2.指定任务类型,再指定模型,创建基于指定模型的Pipeline from transformers import pipeline # https:///models ...
text fill-mask text question-answering text summarization text table-question-answering text text-classification text text-generation text text-to-audio text text2text-generation text token-classification text translation text zero-shot-classification ...
model = AutoModelForSequenceClassification.from_pretrained( model_dir, torch_dtype="auto", device_map="auto", local_files_only=True, ) # 创建一个情感分析Pipeline nlp = pipeline("text-classification", tokenizer=tokenizer, model=model) # 对一段文本进行情感分析 result = nlp("我觉得使用Transformer...