然后我们不得不放弃使用generate函数,因为这会将参数应用于 batch 中所有的序列,而实际上每个序列的参数可能各不相同。值得庆幸的是,我们可以重用较底层的 API ,如LogitsProcessor,以节省大量工作。因此,我们重构了一个generate函数,它接受一个参数列表并将列表中的参数分别应用于 batch 中的各个序列。 最终用户体验主...
AutoProcessor 多模态输入,像LayoutLMv2需要token和图像的输入。 from transformers import AutoProcessor processor = AutoProcessor.from_pretrained("microsoft/layoutlmv2-base-uncased") 预处理 token from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-cased") encoded_input =...
logits = model(**inputs).logits # model predicts one of the 1000 ImageNet classes predicted_label = logits.argmax(-1).item() print(model.config.id2label[predicted_label]) # Egyptian cat 即使对于一个困难的任务如目标检测,用户体验也不会改变很多: fromtransformersimportAutoImageProcessor, AutoMod...
processor = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50") model = DetrForObjectDetection.from_pretrained("facebook/detr-resnet-50") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO ...
我复制了如下代码: from datasets import load_dataset import numpy as np from datasets import load_metric metric = load_metric("accuracy") def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, axis=-1) return metric.compute(predi 浏览38提问于2021-05-2...
inputs = processor(images=inputs, return_tensors="pt") inputs['pixel_values'] = inputs['pixel_values'].to(device) labels = labels.to(device) outputs = model(**inputs) logits = outputs.logits predicted= logits.argmax(-1) total += labels.size(0) ...
AutoProcessor: 用于数据处理 AutoModel: 用于加载模型 它们的使用方式均为:AutoClass.from_pretrain("模型名称"),然后就可以用了。例如: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("bert-base-uncased") tokenizer("I'm learning deep learning.") ...
logits_processor=None, return_past_key_values=False, **kwargs): if history is None: history = [] if logits_processor is None: logits_processor = LogitsProcessorList() logits_processor.append(InvalidScoreLogitsProcessor()) eos_token_id = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_...
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] if model_args.from_scratch: logger.info("Training new model from scratch") ...
Docs for applying SynthID watermarking: https://huggingface.co/docs/transformers/internal/generation_utils#transformers.SynthIDTextWatermarkLogitsProcessor Docs for detecting SynthID watermarking: https://huggingface.co/docs/transformers/internal/generation_utils#transformers.SynthIDTextWatermarkDetector Add Synth...