Trainer huggingface.

• Trainer huggingface 写在前面. The model can be: Passed directly as a PreTrainedModel instance Nov 20, 2022 · A discussion thread about the differences and uses of Trainer and Accelerate, two libraries for distributed training with PyTorch. Hyperparameter search. Nov 20, 2022 · I assume accelerate was added later and has more features like: """ Accelerate is a library that enables the same PyTorch code to be run across any distributed configuration by adding just four lines of code! Trainer¶ We also provide a simple but feature-complete training and evaluation interface through Trainer() and TFTrainer(). hub_private_repo (bool, optional, defaults to False) — If True, the Hub repo will be set to private. Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function. Accelerate is a library designed to simplify distributed training on any type of setup with PyTorch by uniting the most common frameworks (Fully Sharded Data Parallel (FSDP) and DeepSpeed) for it into a single interface. 当从 Trainer 生成的checkpoint恢复训练时，程序会尽一切努力将 python、numpy 和 pytorch 的 RNG（随机数生成器）状态恢复为保存检查点时的状态，这样可以使“停止和恢复”式训练尽可能接近“非停止式”训练。 (如果在多节点环境，添加 --log_on_each_node 0). TrainerCallback subclasses, including: WandbCallback for logging training metrics to W&B if wandb is installed; TensorBoardCallback for logging training metrics to TensorBoard if tensorboard is accessible; CodeCarbonCallback for tracking carbon emissions during training if DPO Trainer. The Trainer supports full model training, fine-tuning, and even model creation through a model_init function. The Hugging Face Trainer is a powerful high-level API provided by the transformers (如果在多节点环境，添加 --log_on_each_node 0). 🤗 Transformers 提供了一个专为训练 🤗 Transformers 模型而优化的 Trainer 类，使您无需手动编写自己的训练循环步骤而更轻松地开始训练模型。Trainer API 支持各种训练选项和功能，如日志记录、梯度累积和混合精度。 Use model after training Once you have trained a model using either the SFTTrainer, PPOTrainer, or DPOTrainer, you will have a fine-tuned model that can be used for text generation. - trl/trl/trainer/grpo_trainer. Trainer takes care of the training loop and allows you to fine-tune a model in a single line of code. When training I want to pass class_weights so the update for rare classes is highen than for large classes. At this point, you may need to restart your notebook or execute the following code to free some memory: [ ] Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Use SFTTrainer: If you have GRPO Trainer. init(project='your_project_name') somewhere before you start using the logger. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2. Aug 9, 2024 · The Trainer class abstracts away much of the complexity involved in training machine learning models, making it easier for practitioners to focus on developing and experimenting with models rather than managing the intricate details of the training process. Trainer( model: Union = None, args: TrainingArguments = None, data_collator: Optional = None, train_dataset: Union = None, eval_dataset: Union = None, tokenizer: Optional = None, model_init: Optional = None, compute May 10, 2023 · When training a model with Huggingface Trainer object, e. Scalability strategy. The Hugging Face Trainer is a powerful high-level API provided by the transformers May 10, 2023 · If the above is not the canonical way to continue training a model, how to continue training with HuggingFace Trainer? Edited With transformers version, 4. We evaluate the fine-tuned model on the test Trainer¶. 什么是huggingface Trainer？ huggingface Trainer是huggingface库中的一个组件，它提供了一个高级的训练接口，可以简化训练过程的编写和管理。Trainer提供了许多配置选项，可以轻松地进行超参数调整、模型保存和加载、学习率调整等操作。 CPO Trainer. Contrastive Preference Optimization (CPO) as introduced in the paper Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation by Haoran Xu, Amr Sharaf, Yunmo Chen, Weiting Tan, Lingfeng Shen, Benjamin Van Durme, Kenton Murray, and Young Jin Kim. but it didn’t worked for me. If using a transformers model, it will be a PreTrainedModel subclass. significantly speed up training - finish training that would take a year in hours; We will first discuss in depth various 1D parallelism techniques and their pros and cons and then look at how they can be combined into 2D and 3D parallelism to enable an even faster training and to support even bigger models. However, you can access useful properties about the training environment through various environment variables (see here for a complete list), such as: SM_MODEL_DIR: A string representing the path to which the training job writes the model artifacts Efficient Training on a Single GPU This guide focuses on training large models efficiently on a single GPU. We will go over everything it supports in Chapter 10. We’ve also used fp16=True to enable mixed-precision training, which gives us another boost in speed. 标题这个 Trainer 还是有歧义的，因为PyTorch的 Lightning 有一个Trainer， HuggingFace 的 Transformers 也有一个Trainer，还有一些github上自己封装的或者基于这两个继续封装的Trainer，知乎上好像还有一个问题讨论了两者哪个比较好。 Callbacks. So I had the idea to instantiate a Trainer with my model and use the trainer. We’re on a journey to advance and democratize artificial intelligence through open source and open science. Mar 25, 2021 · I experimented with Huggingface’s Trainer API and was surprised by how easy it was. The Trainer class supports distributed training, mixed precision, data collation, processing, optimizers, callbacks and more. The code is organized around huggingface transformers Trainer. The reward collator will automatically pass it through and the loss will be computed accordingly. 8. co) 最近在用HF的transformer库自己做训练，所以用着了transformers. Here we tweaked a few of the default options, including logging_steps to ensure we track the training loss with each epoch. 🤗 Transformers库提供了一个优化过的Trainer类，用于训练🤗 Transformers模型，相比于手动编写自己的训练循环，这更容易开始训练。Trainer提供了超参数搜索的API。本文档展示了如何在示例中启用它。 超参数搜索后端 For more details about distributed training, refer to the Accelerate documentation. The abstract from the paper is the following: May 10, 2023 · If your use-case is about adjusting a somewhat-trained model then it can be solved just the same way as fine-tuning. K. Now it’s time to put everything, we have done thus far Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5) to 0. And the user can enjoy the great logging utility and easy distributed training on multiple GPUs provided by Trainer. huggingfaceのTrainerクラスはhuggingfaceで提供されるモデルの事前学習のときに使うものだと思ってて、下流タスクを学習させるとき（Fine Tuning）は普通に学習のコードを実装してたんですが、下流タスクを学習させるときもTrainerクラスは使えて、めちゃくちゃ便利でした。 Accelerate. See examples, links, and tips from the community. This concludes the introduction to fine-tuning using the Trainer API. This model implements the forward pass and computes loss when training. Discover how the Trainer class simplifies training and fine-tuning transformer models, and explore examples for creating custom training loops and dynamically instantiating new models. 1: 2296: June 25, 2024 transformers 라이브러리의 메인은 바로 모델 훈련을 위한 Trainer 함수라 할 수 있는데, 모델 훈련을 위한 정말 많은 기능들을 Dec 19, 2022 · After training, trainer. The following examples build on each other, i. Oct 31, 2023 · Choosing between Trainer and SFTTrainer: Use Trainer: If you have a large dataset and need extensive customization for your training loop or complex training workflows. At TRL we support PPO (Proximal Policy Optimisation) with an implementation that largely follows the structure introduced in the paper “Fine-Tuning Language Models from Human Preferences” by D. I noticed that when I call the train(), I can get a table contains the evaluation loss and training loss, how can I get the data in this table and use them to plot figures? (without wandb) Training customization. Before we start, here are some prerequisites to understand this article: AutoTrain Advanced: faster and easier training and deployments of state-of-the-art machine learning models. Transformers provides thousands of pretrained models to perform tasks on texts such as classification, information extraction, question answering, summarization, translation, text generation, etc in 100+ languages. How to plot loss when using HugginFace's Trainer? 11. Jul 31, 2024 · Reference：【HuggingFace Transformers-入门篇】基础组件之Trainer，Trainer-Huggingface官方说明文档. Train transformer language models with reinforcement learning. 29. Important attributes: model — Always points to the core model. 一. Wu, Daya Guo. WARNING，仅报告错误和警告。 使用 log_level_replica() 更改日志记录级别和日志详细程度。 要为每个节点配置日志级别，请使用 log_on_each_node() 确定是在每个节点上使用特定日志级别还是仅在主节点上使用。 (如果在多节点环境，添加 --log_on_each_node 0). My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. 当从 Trainer 生成的checkpoint恢复训练时，程序会尽一切努力将 python、numpy 和 pytorch 的 RNG（随机数生成器）状态恢复为保存检查点时的状态，这样可以使“停止和恢复”式训练尽可能接近“非停止式”训练。 Trainer. Will default to the token in the cache folder obtained with huggingface-cli login. 5: 50: May 8, 2025 Resume_from_checkpoint. Trainer supports several hyperparameter search backends - Optuna, SigOpt, Weights & Biases, Ray Tune - through hyperparameter_search() to optimize an objective or even multiple objectives. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch. For example, you may want to remove a column or cast it as a different type. You just need to call wandb. Aug 20, 2023 · We create a Trainer instance with the model, training arguments, and customized evaluation metrics. 这里主要是记录一下 huggingface 的 trainer 用来做 torch 的训练，验证，测试，比手写方便不少。. Trainer. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. In this section, we’ll walk through the process of loading the fine-tuned model and generating text. Dec 25, 2021 · How to resume training from a checkpoint using huggingface trainer. 1 , trying @maciej-skorski answer with Seq2SeqTrainer , Read Huggingface Transformers Trainer as a general PyTorch trainer for more detail. Call train() to finetune your model. 在本文中，我们将介绍如何使用PyTorch和HuggingFace Trainer库来记录训练数据的日志。HuggingFace Trainer库是一个用于进行深度学习模型训练的高级库，它提供了一系列方便的功能，包括模型训练、评估和日志记录等。 Trainer is an optimized training loop for Transformers models, making it easy to start training right away without manually writing your own training code. TRL supports the GRPO Trainer for training language models, as described in the paper DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models by Zhihong Shao, Peiyi Wang, Qihao Zhu, Runxin Xu, Junxiao Song, Mingchuan Zhang, Y. Models. Reload to refresh your session. Trainer，这里记录下用法. How is this possible in HF with PyTorch? Thanks Philip You signed in with another tab or window. Often times you may want to modify the structure and content of your dataset before you use it to train a model. Li, Y. Below are some The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. May 18, 2021 · Hi @hiramcho, check out the docs on the logger to solve that issue. Pytorch 使用Huggingface Trainer和分布式数据并行 在本文中，我们将介绍如何使用Pytorch的Huggingface Trainer和分布式数据并行来训练模型。 Huggingface Trainer是一个用于训练和评估自然语言处理（NLP）模型的高级API，可以简化训练过程并提供便捷的功能。 Trainer. At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint. e. import os os. The abstract from the paper is the following: Org profile for Glif Loradex Trainer on Hugging Face, the AI community building the future. Important. Manning, Chelsea Finn. predict() immediately after trainer. Nov 20, 2022 · 【 Huggingface Transformers入門⑦】文章分類モデルを作成する（2） 〜Trainerクラスとファインチューニング〜 このシリーズ では、自然言語処理において主流であるTransformerを中心に、環境構築から学習の方法までまとめます。 Trainer¶. For a comprehensive guide on scaling large language models, check out the Ultrascale Playbook, which provides detailed strategies and best practices for training at scale. Now I would like to run my trained model to get labels for a large test dataset (around 20,000 texts). My question is how do I use the model I created to predict the labels on my test dataset? Do I just call trainer. The Trainer contains the basic training loop which supports the above features. Trainer¶. eval_strategy Nov 10, 2020 · Hi, I made this post to see if anyone knows how can I save in the logs the results of my training and validation loss. Generalized Knowledge Distillation (GKD) was proposed in On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes by Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, Piotr Stanczyk, Sabela Ramos, Matthieu Geist, and Olivier Bachem. Plug a model, preprocessor, dataset, and training arguments into Trainer and let it handle the rest to start training faster. Instead, I found here that they add arguments to their python file with nproc_per_node , but that seems too specific to their script and not clear how to use in general. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks. As there are very few examples online on how to use Huggingface’s Trainer API, I hope to contribute a simple example of how Trainer could be used to fine-tune your pretrained model. Online DPO was proposed in Direct Language Model Alignment from Online AI Feedback by Shangmin Guo, Biao Zhang, Tianlin Liu, Tianqi Liu, Misha Khalman, Felipe Llinares, Alexandre Rame, Thomas Mesnard, Yao Zhao, Bilal Piot, Johan Ferret, and Mathieu Blondel. torch的最大优点就是灵活度极高，导致不同人开发出来的代码范式千差万别，缺点就是自己纯手写太麻烦了，复用性也不好。 Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. Generalized Knowledge Distillation Trainer. 在分布式环境中，Trainer 副本设置为 logging. Feb 4, 2023 · This article provides a guide to the Hugging Face Trainer class, covering its components, customization options, and practical use cases. 🤗 Datasets provides the necessary tools to do this, but since each dataset is so different, the processing approach will vary individually. Jan 15, 2025 · 本文介绍了如何使用HuggingFace中的Trainer对BERT模型微调。可以看到，使用Trainer进行模型微调，代码较为简洁，且支持功能丰富，是理想的模型训练方式。 Transformers is a library of pretrained text, computer vision, audio, video, and multimodal models for inference and training. We are able to use the Trainer API as it is; however, we are also able to tweak how we use the Trainer in order to develop custom training loops. Trainer¶ We also provide a simple but feature-complete training and evaluation interface through Trainer() and TFTrainer(). Adding a margin to the loss. To this end, you pass the current model state along with a new parameter config to the Trainer object in PyTorch API. (如果在多节点环境，添加 --log_on_each_node 0). py at main · huggingface/trl Mar 22, 2023 · The Huggingface docs on training with multiple GPUs are not really clear to me and don't have an example of using the Trainer. Callbacks are objects that can customize the behavior of the training loop in the PyTorch Trainer (this feature is not yet implemented in TensorFlow) that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early stopping). Learn how to use the Trainer class to train, evaluate or use models with 🤗 Transformers library. Oct 16, 2023 · Create Trainer. Aug 20, 2020 · Hi I’m trying to fine-tune model with Trainer in transformers, Well, I want to use a specific number of GPU in my server. . 使用Trainer API进行超参数搜索. This makes training with LoRA much faster, memory-efficient, and produces smaller model weights (a few hundred MBs), which are easier to store and share. Thus, it is modularized, clean, and easy to modify. To properly define it, we need to know the number of training steps we will take, which is the number of epochs we want to run multiplied by the number of training batches (which is the length of our training dataloader). TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Overview. I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. Jul 28, 2023 · There’s a few *Trainer objects available from transformers, trl and setfit. PyTorch HuggingFace Trainer 训练数据的日志记录. Use the Model Memory Calculator to calculate how much memory a model . hub_always_push (bool, optional, defaults to False) — Unless this is True, the Trainer will skip pushing a checkpoint when the previous push is not finished. LoRA can also be combined with other training techniques like DreamBooth to speedup training. 随机性. If training works as intended, this metric should keep going up. The abstract from the paper is the following: Trainer¶. Dec 23, 2024 · Gradient checkpointing typically saves memory during the training of large models. If training is to be performed on multiple GPUs (and/or multiple nodes), it indicates that the distributed training method in use is DDP. Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. The Trainer will work out of the box on multiple GPUs or TPUs and provides lots of options, like mixed-precision training (use fp16 = True in your training arguments). /", evaluation 1、目的. May 28, 2024 · The Sentence Transformers trainer supports various transformers. May 3, 2022 · I am using the huggingface transformers. Debugging TIP: objective/rlhf_reward: this is the ultimate objective of the RLHF training. /results', # output directory* * num_train_epochs=3, # total number of training epochs* * per_device_train_batch_size=16, # batch size per device during training* * per_device_eval_batch_size=16 GRPO Trainer. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities. Odds Ratio Preference Optimization (ORPO) was introduced in ORPO: Monolithic Preference Optimization without Reference Model by Jiwoo Hong, Noah Lee, and James Thorne. By default, the Trainer will remove any columns that are not part of the model’s forward() method. Supervised Fine-tuning Trainer. You can train, fine-tune, and evaluate any 🤗 Transformers model with a wide range of training options and with built-in features like logging, gradient accumulation, and mixed precision. 当从 Trainer 生成的checkpoint恢复训练时，程序会尽一切努力将 python、numpy 和 pytorch 的 RNG（随机数生成器）状态恢复为保存检查点时的状态，这样可以使“停止和恢复”式训练尽可能接近“非停止式”训练。 Trainer is an optimized training loop for Transformers models, making it easy to start training right away without manually writing your own training code. The Hugging Face Trainer uses PyTorch under the hood, but makes it very easy and intuative to train a transformer model. 🤗Transformers. Aug 16, 2021 · Wandb website for Huggingface Trainer shows plots and logs only for the first model. These approaches are still valid if you have access to a machine with multiple GPUs but you will also have access to additional methods outlined in the multi-GPU section. I’m using this code: *training_args = TrainingArguments(* * output_dir='. 概述 本教程假定你已经对于 PyToch 训练一个简单模型有一定的基础理解。本教程将展示使用 3 种封装层级不同的方法调用 DDP (DistributedDataParallel) 进程，在多个 GPU 上训练同一个模型： Apr 10, 2023 · はじめに. You switched accounts on another tab or window. As in the Llama 2 paper, you can add a margin to the loss by adding a margin column to the dataset. This works fine, but I was wondering if it makes sense (and it’s efficient, advisable, & so on) to use a Adding a margin to the loss. Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5) to 0. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. [paper, code]. To inject custom behavior you can subclass them and override the following methods: get_train_dataloader — Creates the training DataLoader. Jul 7, 2021 · Does anyone have an end-to-end example of how to do multi-gpu, multi-node distributed training using the trainer? I can’t seem to find one anywhere. Trainer内部封装了完整的训练以及评估逻辑，搭配TrainingArguments可以对训练过程中的各项参数进行配置。Trainer的参数非常多，Trainer-Huggingface官方说明文档提供了详细的参数说明。 May 22, 2022 · Huggingface が提供している様々なコード例のうち、no_trainer が末尾に付いていない訓練コードでは、大方、引数を TrainingArguments にまとめ上げ、それを Trainer クラスに渡すという実装方法になっている。 GRPO Trainer. environ["WANDB_DISABLED"] = "true" batch_size = 2 # set training arguments - these params are not really tuned, feel free to change training_args = Seq2SeqTrainingArguments( output_dir=". Other than the standard answer of “it depends on the task and which library you want to use”, what is the best practice or general guidelines when choosing which *Trainer object to use to train/tune our models? Together with the *Trainer object, sometimes we see suggestions to use *TrainingArguments or the ORPO Trainer. 现在开源的训练大模型的框架，包括 FactChat 、 LLama-Factory 等经典的训练框架，它们内部训练模型的流程类似，都采用了trainer来训练，trainer是 HuggingFace 的高阶训练框架，它封装了模型训练的loss计算、metrics计算等内容，所以，使用trainer只需要设置训练参数，就可以训练模型，但是，loss是 episode: episode: The current episode count in the training process. 0. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. You signed in with another tab or window. DPO Trainer. from Neural Plasticity - Bert2Bert on WMT14 | Kaggle from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments import os os. Supervised fine-tuning (SFT) is the most common step in post-training foundation models, and also one of the most effective. You signed out in another tab or window. 训练器. It’s used in most of the example scripts. Cookbook. We fine-tune the model on the training dataset. amp。 Aug 9, 2024 · The Trainer class abstracts away much of the complexity involved in training machine learning models, making it easier for practitioners to focus on developing and experimenting with models rather than managing the intricate details of the training process. Trainer is a complete training and evaluation loop for Transformers’ PyTorch models. 基本参数 class transformers. evaluate() is called which I think is being done on the validation dataset. 当从 Trainer 生成的checkpoint恢复训练时，程序会尽一切努力将 python、numpy 和 pytorch 的 RNG（随机数生成器）状态恢复为保存检查点时的状态，这样可以使“停止和恢复”式训练尽可能接近“非停止式”训练。 Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. environ["CUDA_DEVICE Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. TRL is designed with modularity in mind so that users to be able to efficiently customize the training loop for their needs. Feb 19, 2025 · If your use case is not straightforward and requires specific things to be done, we can develop custom training loops with the Trainer API in order to accomplish these things. distributed_backend. Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. Ziegler et al. Oct 3, 2021 · training_args = TrainingArguments( output_dir=results_dir, # output directory num_train_epochs=50, # total number of training epochs per_device_train_batch_size=16, # batch size per device during training per_device_eval_batch_size=64, # batch size for evaluation warmup_steps=500, # number of warmup steps for learning rate scheduler weight Online DPO Trainer. Hyperparameter search discovers an optimal set of hyperparameters that produces the best model performance. Apr 17, 2025 · The Trainer requires a PyTorch model, typically a PreTrainedModel from the transformers library. evaluate() like so? trainer = Trainer(model, args, train_dataset=encoded_dataset[“train”], It works by inserting a smaller number of new weights into the model and only these are trained. 使用 PyTorch Trainer 进行训练. , all of the scripts below should be copied and pasted into one Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. Pick and choose from a wide range of training features in TrainingArguments such as gradient accumulation, mixed precision, and options for reporting and logging training metrics. Trainer is also powered by Accelerate, a library for handling large models for distributed training. Read Huggingface Transformers Trainer as a general PyTorch trainer for more detail. Mar 7, 2021 · Additionally, if the training is aborted and I’m restarting from a checkpoint - does the checkpoint have information about the shuffling order for this given epoch and which datapoints still haven’t gone through this epoch already? Yes training will resume with the same shuffle, at the same point you were at the time of the save. episode: episode: The current global step or episode count in the training process. We evaluate the fine-tuned model on the test Jan 9, 2025 · An introduction to training/finetuning language Hugging Face models with PyTorch. Sep 9, 2020 · I have an unbalanced dataset. Apr 29, 2024 · 原文连接: Trainer (huggingface. Trainer 类为 PyTorch 中的全功能训练提供了一个 API，它支持在多个 GPU/TPU 上的分布式训练，NVIDIA GPU、AMD GPU 的混合精度训练，以及 PyTorch 的 torch. Our training script is very similar to a training script you might run outside of SageMaker. predict() method on my data. g. Aug 20, 2021 · Hello everyone, I successfully fine-tuned a model for text classification. Jul 5, 2021 · Trainerは便利だが，中で何がどう動いているか分からないと怖くて使えないので，メモ。 公式ドキュメントでの紹介はここ。 基本的な使い方from transformers import Trai… Trainer¶ The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. lfmw ujeowms kcpjh qcwvsbg qfg uop mavshir oehv nuerb gphtk