Trainer huggingface 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. 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. 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. Dec 25, 2021 · How to resume training from a checkpoint using huggingface trainer. Manning, Chelsea Finn. Trainer is also powered by Accelerate, a library for handling large models for distributed training. 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. episode: episode: The current global step or episode count in the training process. 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. The abstract from the paper is the following: Org profile for Glif Loradex Trainer on Hugging Face, the AI community building the future. 训练器. 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. DPO Trainer. Will default to the token in the cache folder obtained with huggingface-cli login. 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. AutoTrain Advanced is a no-code solution that allows you to train machine learning models in just a few clicks. 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. 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. Our training script is very similar to a training script you might run outside of SageMaker. It’s used in most of the example scripts. State-of-the-art Natural Language Processing for PyTorch and TensorFlow 2. Train transformer language models with reinforcement learning. At the end of each epoch, the Trainer will evaluate the accuracy and save the training checkpoint. 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. init(project='your_project_name') somewhere before you start using the logger. - trl/trl/trainer/grpo_trainer. Plug a model, preprocessor, dataset, and training arguments into Trainer and let it handle the rest to start training faster. May 28, 2024 · The Sentence Transformers trainer supports various transformers. 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. Learn how to use the Trainer class to train, evaluate or use models with 🤗 Transformers library. co) 最近在用HF的transformer库自己做训练，所以用着了transformers. import os os. 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). As in the Llama 2 paper, you can add a margin to the loss by adding a margin column to the dataset. 使用Trainer API进行超参数搜索. Read Huggingface Transformers Trainer as a general PyTorch trainer for more detail. predict() method on my data. 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. 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. Use Transformers to fine-tune models on your data, build inference applications, and for generative AI use cases across multiple modalities. 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. Dec 23, 2024 · Gradient checkpointing typically saves memory during the training of large models. Use SFTTrainer: If you have GRPO Trainer. 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. /", evaluation 1、目的. 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. /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. So I had the idea to instantiate a Trainer with my model and use the trainer. Apr 29, 2024 · 原文连接: Trainer (huggingface. The code is organized around huggingface transformers Trainer. Mar 25, 2021 · I experimented with Huggingface’s Trainer API and was surprised by how easy it was. Wu, Daya Guo. distributed_backend. 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. 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¶. 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. e. 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. 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. Ziegler et al. We’ve also used fp16=True to enable mixed-precision training, which gives us another boost in speed. How is this possible in HF with PyTorch? Thanks Philip You signed in with another tab or window. Trainer¶. If training works as intended, this metric should keep going up. See examples, links, and tips from the community. Trainer The Trainer class provides an API for feature-complete training in PyTorch for most standard use cases. 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. . The Trainer supports full model training, fine-tuning, and even model creation through a model_init function. Adding a margin to the loss. Li, Y. 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. Sep 9, 2020 · I have an unbalanced dataset. 1 , trying @maciej-skorski answer with Seq2SeqTrainer , Read Huggingface Transformers Trainer as a general PyTorch trainer for more detail. We evaluate the fine-tuned model on the test Jan 9, 2025 · An introduction to training/finetuning language Hugging Face models with PyTorch. Important. Thus, it is modularized, clean, and easy to modify. Use the Model Memory Calculator to calculate how much memory a model . Trainer，这里记录下用法. 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. 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. The abstract from the paper is the following: Trainer¶. Before instantiating your Trainer, create a TrainingArguments to access all the points of customization during training. Jul 31, 2024 · Reference：【HuggingFace Transformers-入门篇】基础组件之Trainer，Trainer-Huggingface官方说明文档. Call train() to finetune your model. Reload to refresh your session. 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. 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. 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. 29. 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. 8. PyTorch HuggingFace Trainer 训练数据的日志记录. How to plot loss when using HugginFace's Trainer? 11. 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. You switched accounts on another tab or window. And the user can enjoy the great logging utility and easy distributed training on multiple GPUs provided by Trainer. 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. environ["CUDA_DEVICE Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. 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. 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. TRL is designed with modularity in mind so that users to be able to efficiently customize the training loop for their needs. 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). 5: 50: May 8, 2025 Resume_from_checkpoint. 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. I’ve read the Trainer and TrainingArguments documents, and I’ve tried the CUDA_VISIBLE_DEVICES thing already. To inject custom behavior you can subclass them and override the following methods: get_train_dataloader — Creates the training DataLoader. 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. The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. 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. 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. The Hugging Face Trainer uses PyTorch under the hood, but makes it very easy and intuative to train a transformer model. 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. 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. The Trainer contains the basic training loop which supports the above features. Models. My question is how do I use the model I created to predict the labels on my test dataset? Do I just call trainer. For users who prefer to write their own training loop, you can also fine-tune a 🤗 Transformers model in native PyTorch. Finally, the learning rate scheduler used by default is just a linear decay from the maximum value (5e-5) to 0. 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. My server has two GPUs,(index 0, index 1) and I want to train my model with GPU index 1. If training is to be performed on multiple GPUs (and/or multiple nodes), it indicates that the distributed training method in use is DDP. We will go over everything it supports in Chapter 10. 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. Odds Ratio Preference Optimization (ORPO) was introduced in ORPO: Monolithic Preference Optimization without Reference Model by Jiwoo Hong, Noah Lee, and James Thorne. 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=". The following examples build on each other, i. 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. 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). Oct 16, 2023 · Create Trainer. 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. 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(). Generalized Knowledge Distillation 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. If using a transformers model, it will be a PreTrainedModel subclass. Supervised Fine-tuning Trainer. hub_private_repo (bool, optional, defaults to False) — If True, the Hub repo will be set to private. To this end, you pass the current model state along with a new parameter config to the Trainer object in PyTorch API. You signed in with another tab or window. 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. but it didn’t worked for me. When training I want to pass class_weights so the update for rare classes is highen than for large classes. 使用 PyTorch Trainer 进行训练. 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. We’re on a journey to advance and democratize artificial intelligence through open source and open science. Aug 20, 2023 · We create a Trainer instance with the model, training arguments, and customized evaluation metrics. You signed out in another tab or window. 在分布式环境中，Trainer 副本设置为 logging. Here we tweaked a few of the default options, including logging_steps to ensure we track the training loss with each epoch. 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. from Neural Plasticity - Bert2Bert on WMT14 | Kaggle from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments import os os. 一. 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). 随机性. Trainer supports several hyperparameter search backends - Optuna, SigOpt, Weights & Biases, Ray Tune - through hyperparameter_search() to optimize an objective or even multiple objectives. 🤗Transformers. g. Feb 4, 2023 · This article provides a guide to the Hugging Face Trainer class, covering its components, customization options, and practical use cases. In this section, we’ll walk through the process of loading the fine-tuned model and generating text. 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. 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. Now I would like to run my trained model to get labels for a large test dataset (around 20,000 texts). Important attributes: model — Always points to the core model. Aug 20, 2021 · Hello everyone, I successfully fine-tuned a model for text classification. Overview. 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. 0. The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. This concludes the introduction to fine-tuning using the Trainer API. 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. LoRA can also be combined with other training techniques like DreamBooth to speedup training. Trainer is a complete training and evaluation loop for Transformers’ PyTorch models. Aug 16, 2021 · Wandb website for Huggingface Trainer shows plots and logs only for the first model. 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. 写在前面. You just need to call wandb. Trainer¶ We also provide a simple but feature-complete training and evaluation interface through Trainer() and TFTrainer(). 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。. Jul 28, 2023 · There’s a few *Trainer objects available from transformers, trl and setfit. predict() immediately after trainer. Scalability strategy. By default, the Trainer will remove any columns that are not part of the model’s forward() method. For example, you may want to remove a column or cast it as a different type. , 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. Supervised fine-tuning (SFT) is the most common step in post-training foundation models, and also one of the most effective. 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). Pass the training arguments to Trainer along with the model, dataset, tokenizer, data collator, and compute_metrics function. Trainer takes care of the training loop and allows you to fine-tune a model in a single line of code. 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. Hyperparameter search discovers an optimal set of hyperparameters that produces the best model performance. Before instantiating your Trainer / TFTrainer, create a TrainingArguments / TFTrainingArguments to access all the points of customization during training. We evaluate the fine-tuned model on the test Trainer¶. 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. Cookbook. 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. We fine-tune the model on the training dataset. 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. 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. Apr 17, 2025 · The Trainer requires a PyTorch model, typically a PreTrainedModel from the transformers library. Hyperparameter search. May 18, 2021 · Hi @hiramcho, check out the docs on the logger to solve that issue. Debugging TIP: objective/rlhf_reward: this is the ultimate objective of the RLHF training. 🤗 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. I’m using this code: *training_args = TrainingArguments(* * output_dir='. Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers. The reward collator will automatically pass it through and the loss will be computed accordingly. [paper, code]. Often times you may want to modify the structure and content of your dataset before you use it to train a model. This model implements the forward pass and computes loss when training. The Trainer class supports distributed training, mixed precision, data collation, processing, optimizers, callbacks and more. Below are some The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex and Native AMP for PyTorch. (如果在多节点环境，添加 --log_on_each_node 0). 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. 🤗 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. K. 🤗 Datasets provides the necessary tools to do this, but since each dataset is so different, the processing approach will vary individually. 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. May 3, 2022 · I am using the huggingface transformers. 基本参数 class transformers. evaluate() is called which I think is being done on the validation dataset. jizie xmrdg ejfp crcdi nyy vpk abl sfg tlsrxd graoh