def preprocess_logits_for_metrics(logits, labels): if isinstance(logits, tuple): # Depending on the model and config, logits may contain extra tensors, # like past_key_values, but logits always come first logits = logits[0] return logits.argmax(dim=-1) # Initialize our Trainer trainer =...
preprocess_logits_for_metrics为模型评估阶段前对logits的预处理 TrainingArguments为训练参数类,其网址为:huggingface.co/docs/tra,传入参数非常多(transformers版本4.32.1中有98个参数!),我们在这里只介绍几个常见的: output_dir: stroverwrite_output_dir: bool = False evaluation_strategy: typing.Union[transformers...
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 API # let's only keep detections with score > 0.9 target_sizes =torch....
preprocess_logits_for_metrics=preprocess_logits_for_metrics, eval_dataset=dataset_eval, data_collator=default_data_collator ) trainer.train() trainer.model.save_pretrained(output_dir) del model del trainer peft_config = PeftConfig.from_pretrained(output_dir) model = AutoModelForCausalLM.from_pretrai...
huggingface-cli 隶属于 huggingface_hub 库,不仅可以下载模型、数据,还可以可以登录huggingface、上传模型、数据等huggingface-cli 属于官方工具,其长期支持肯定是最好的。优先推荐!安装依赖 1 pip install -U huggingface_hub 注意:huggingface_hub 依赖于 Python>=3.8,此外需要安装 0.17.0 及以上的版本,推荐0.19.0+...
││ 152 │ │ preprocess_logits_for_metrics=preprocess_logits_for_metrics, ││ 153 │ ) ││ ❱ 154 │ trainer.train() ││ 155 │ trainer.save_model(output_dir) ││ 156 ││ ││ /data/transformers/src/transformers/trainer.py:1631 in train ││ ││ 1628 │ │ inner_training...
[fix bug] logits's shape different from label's shape in preprocess_logits_for_metrics by@wiserxinin#31447 Fix RT-DETR cache for generate_anchors by@qubvelin#31671 Fix RT-DETR weights initialization by@qubvelin#31724 pytest_num_workers=4for some CircleCI jobs by@ydshiehin#31764 ...
我觉得最简单还是你基于函数直接写一个训练的loop,比如像huggingface的accelerator那样,只维持尽量少的功能...
正如@Enes Altınışık在评论中指出的那样,使用CPU重新运行代码(从而重新启动Kaggle示例)起...
我认为最好先调试train函数,实际上可以通过从数据集采样并整理输出来调试collate函数: