根据任务类型直接创建Pipeline 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 = AutoModelForSequ...
Transformers库使用pipeline去完成各种NLP任务,如Question-answering、Text classification、Image classification等,之后我们会对每一个pipeline进行详细介绍。 在这里,先以一个例子了解Pipeline的用法。 from transformers import pipeline classifier = pipeline("sentiment-analysis") classifier( ["I've been waiting for a ...
pipeline支持的task包括: "feature-extraction": will return a FeatureExtractionPipeline. "text-classification": will return a TextClassificationPipeline. "sentiment-analysis": (alias of "text-classification") will return a TextClassificationPipeline. "token-classification": will return a TokenClassification...
from transformers import TextClassificationPipeline class MarioThePlumber(TextClassificationPipeline): def postprocess(self, model_outputs): best_class = model_outputs["logits"] return best_class pipe = MarioThePlumber(model=model, tokenizer=tokenizer) pipe(text, batch_size=2, truncation="only_first"...
pipeline支持的task包括: "feature-extraction": will return a FeatureExtractionPipeline. "text-classification": will return a TextClassificationPipeline. "sentiment-analysis": (alias of "text-classification") will return a TextClassificationPipeline. ...
model_name ="nlptown/bert-base-multilingual-uncased-sentiment"model = AutoModelForSequenceClassification.from_pretrained(model_name) tokenizer = AutoTokenizer.from_pretrained(model_name) classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer) ...
创建Pipeline步骤简单，只需几个关键步骤。你需确定使用的目标模型和任务类型，然后使用HuggingFace提供的模型库创建模型实例。接着，根据任务需求选择合适的Pipeline类，比如对于文本分类任务，可以选择TextClassificationPipeline。验证Pipeline创建是否成功，可以通过调用其方法并提供测试文本。例如，使用文本“very ...
"fill-mask": will return a FillMaskPipeline:. "image-classification": will return a ImageClassificationPipeline. "image-segmentation": will return a ImageSegmentationPipeline. "image-to-image": will return a ImageToImagePipeline. "image-to-text": will return a ImageToTextPipeline. "mask-generat...
tokenizer.pad_token = tokenizer.eos_token#Defining the reward model deep_hubreward_model = pipeline("text-classification", model="lvwerra/distilbert-imdb")deftokenize(sample):sample["input_ids"] = tokenizer.encode(sample["query"])returnsample ...
reward_model = pipeline("text-classification",model="lvwerra/distilbert-imdb") def tokenize(sample): sample["input_ids"] = tokenizer.encode(sample["query"]) return sample dataset = dataset.map(tokenize,batched=False) ppo_trainer = PPOTrainer(model=model,config=config,train_dataset=train_datase...