对于这种情况,NoRepeatNGramLogitsProcessor会将以[123,234,456]为结尾的sequence,其next token在预测时的概率设置为负无穷-float("inf") for i, banned_tokens in enumerate(banned_batch_tokens): scores_processed[i, banned_tokens] = -float("inf") NoRepeatNGramLogitsProcessor处理实际过程 1.输入长度为N...
sampling_rate = processor.feature_extractor.sampling_rate common_voice = common_voice.cast_column("audio", Audio(sampling_rate=sampling_rate)) 开始微调训练 从whisper-small模型,开始微调训练: from transformers import WhisperForConditionalGeneration ...
The HuggingFaceProcessor in the Amazon SageMaker Python SDK provides you with the ability to run processing jobs with Hugging Face scripts. When you use the HuggingFaceProcessor, you can leverage an Amazon-built Docker container with a managed Hugging Face environment so that you don't need to ...
我们这里从 Hub 中使用一个在 COCO 全景数据集上训练的一个模型来实例化一个 Mask2Former 以及对应的 processor。需要注意的是,在不同数据集上训练出来的 checkpoints 已经公开,数量不下30 个。 fromtransformersimportAutoImageProcessor, Mask2FormerForUniversalSegmentation processor = AutoImageProcessor.from_pretrai...
inputs = processor(text="Don't count the days, make the days count.", return_tensors="pt") SpeechT5 TTS 模型不限于为单个说话者创建语音。相反,它使用所谓的Speaker Embeddings来捕捉特定说话者的语音特征。我们将从 Hub 上的数据集中加载这样一个 Speaker Embeddings。
processor.set_tokenizer(tokenizer) # 添加配置 # print("config=", config) processor.set_config(config) if data_args.task_type == 'autocls': model_class = build_cls_model(config) else: model_class = MODEL_CLASSES[data_args.task_type] ...
vit 训练CIFAR10数据集,冻结所有层,只保留全连接层 from transformers import ViTImageProcessor, ViTForImageClassification from PIL import Image import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader ...
save/folder/",+ use_habana=True,+ use_lazy_mode=True,+ gaudi_config_name="Habana/bert-base-uncased",... ) # Initialize the trainer- trainer = Trainer(+ trainer = GaudiTrainer(model=model, args=training_args, train_dataset=train_dataset, ... ) # Use Habana Gaudi processor for ...
="path/to/save/folder/",+use_habana=True,+use_lazy_mode=True,+gaudi_config_name="Habana/bert-base-uncased",... ) # Initialize the trainer-trainer = Trainer(+trainer = GaudiTrainer(model=model, args=training_args, train_dataset=train_dataset, ... ) # Use Habana Gaudi processor for ...
简介:本部分首先介绍如何使用pipeline()进行快速推理,然后介绍AutoClass:用AutoModel加载预训练模型、用tokenizer将文本转化为模型的数字输入、用AutoConfig来改变模型超参、用AutoFeatureExtractor加载预训练的feature extractor、用AutoProcessor加载预训练的processor。本文将仅关注PyTorch语言,但对TensorFlow语言的适配在本部分...