WebMay 9, 2024 · I'm using the huggingface Trainer with BertForSequenceClassification.from_pretrained("bert-base-uncased") model. Simplified, … WebMar 31, 2024 · The Huggingface’s trainer function example. The first part of the code is defining a function to measure the model’s accuracy (compute_metric) using the sklearn library. Then we have the training arguments, which control the whole training process.
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WebFeb 21, 2024 · When I add a custom compute_metrics function to the Trainer, I get the warning “Not all data has been set. Are you sure you passed all values?” at each evaluation step. This warning is defined in the finalize function of the class trainer_pt_utils.DistributedTensorGatherer: if self._offsets [0] != self.process_length: Webhuggingface中的库： Transformers; Datasets; Tokenizers; Accelerate; 1. Transformer模型 本章总结 - Transformer的函数pipeline()，处理各种nlp任务，在hub中搜索和使用模型 - transformer模型的分类，包括encoder 、decoder、encoder-decoder model pipeline() Transformers库提供了创建和使用共享模型的功能。 the home robot教材分析
使用 LoRA 和 Hugging Face 高效训练大语言模型 - HuggingFace
WebComing from tensorflow I am a bit confused as to how to properly define the compute_metrics () in Trainer. For instance, I see in the notebooks various possibilities. … WebFeb 26, 2024 · Compute metrics on the test set. Last, let’s use the best trained model to make predictions on the test set and compute its accuracy. Predictions can be produced using the predict method of the ... WebApr 13, 2024 · import numpy as np import evaluate metric = evaluate.load("accuracy") def compute_metrics(eval_pred): logits, labels = eval_pred predictions = np.argmax(logits, … the home revivalists