通过AutoModelXXX加载模型 - 通过TrainingArguments配置学习率等参数 - 通过trainer.train()开始训练 - 通过trainer.predict计算验证数据集的预测结果 - 通过load_metric来加载指定指标对象,并调用其compute方法计算训练准确率等 - 初始化Trainer时可指定compute_metrics来动态监控每个epoch的数据 #来自油管 https://youtu...
from transformers import Trainer class WeightedCELossTrainer(Trainer): def compute_loss(self, model, inputs, return_outputs=False): labels = inputs.pop("labels") # Get model's predictions outputs = model(**inputs) logits = outputs.get("logits") # Compute custom loss loss_fct = torch.nn...
How does one create a custom hugging face model that is compatible with the HF trainer? I want to create a new hugging face (HF) architecture with some existing tokenizer (any one that is excellent is fine). Let's say decoder to make it concrete (but both is better)...
3.2.1 trainer中设置学习率 3.2.2 get_scheduler具体参数 一、Load dataset 本节参考官方文档:Load数据集存储在各种位置,比如 Hub 、本地计算机的磁盘上、Github 存储库中以及内存中的数据结构(如 Python 词典和 Pandas DataFrames)中。无论您的数据集存储在何处, Datasets 都为您提供了一种加载和使用它进行训练...
trainer (Trainer): The Hugging Face Trainer instance. tokenizer (AutoTokenizer): The tokenizer associated with the model. sample_dataset (Dataset): A subset of the validation dataset for generating predictions. num_samples (int, optional): Number of samples to select from ...
I have overridden the on_step_end method in TrainerCallback from transformers library in order to be able to get the evaluation on the training set, such that I can compare training loss with accuracy after training. The code is as follows: class CustomCallback(TrainerCall...
如前所述,Hugging Face transformers 现支持 PyTorch/XLA 的最新 FSDP 实现,这可以显著加快微调速度。只需在 transformers.Trainer 中添加 FSDP 配置即可启用此功能:from transformers import DataCollatorForLanguageModeling, Trainer, TrainingArguments# Set up the FSDP config. To enable FSDP via SPMD, set xla...
使用Hugging Face 微调 Gemma 模型 我们最近宣布了,来自 Google Deepmind 开放权重的语言模型Gemma现已通过 Hugging Face 面向更广泛的开源社区开放。该模型提供了两个规模的版本:20 亿和 70 亿参数,包括预训练版本和经过指令调优的版本。它在 Hugging Face 平台上提供支持,可在 Vertex Model Garden 和 Google ...
然后用nvidia-smi监视gpu,见:x1c 0d1x
In this post, we walk you through an example of how to build and deploy a custom Hugging Face text summarizer on SageMaker. We use Pegasus [1] for this purpose, the first Transformer-based model specifically pre-trained on an objective tailored for abstractive text summarization. BER...