() return avg_loss def train_epoch(self, datasets, epoch): self.model.train() for batch_id, data in enumerate(datasets()): loss = self.train_step(data) # 每训练了1000批次的数据,打印下当前Loss的情况 if batch_id % 500 == 0: print("epoch_id: {}, batch_id: {}, loss is: {}...
per_device_eval_batch_size:指定每个GPU核心/CPU在评估时使用的批处理大小。 gradient_accumulation_steps:在执行反向传播和更新参数之前,累积梯度的步数。使用梯度累积时,一次步数对应一次反向传播。 eval_accumulation_steps:在将预测结果移动到CPU之前,累积的预测步数。如果未设置,则整个预测结果将在GPU/TPU上累积后再...
Step4: 执行fine-tune并评估模型 In [10] trainer.train(train_dataset, epochs=10, batch_size=32, eval_dataset=dev_dataset, save_interval=5) # 配置训练参数,启动训练,并指定验证集 [2021-01-18 19:38:09,224] [ TRAIN] - Epoch=1/10, Step=10/50 loss=0.9344 f1_score=0.0703 lr=0.000050 s...
[2021-01-14 18:07:09,178] [ TRAIN] - Epoch=1/3, Step=250/300 loss=0.2602 acc=0.9125 lr=0.000050 step/sec=10.55 | ETA 00:01:26 [2021-01-14 18:07:10,127] [ TRAIN] - Epoch=1/3, Step=260/300 loss=0.1979 acc=0.9281 lr=0.000050 step/sec=10.54 | ETA 00:01:26 [2021-01-1...
eval_batch_step: [0, 2000] cal_metric_during_train: true pretrained_model: checkpoints: save_inference_dir: use_visualdl: false infer_img: doc/imgs_words/ch/word_1.jpg character_dict_path: ppocr/utils/ppocr_keys_v1.txt max_text_length: &max_text_length 70 ...
eval_batch_step: [0,1200] #模型评估间隔 # if pretrained_model is saved in static mode, load_static_weights must set to True load_static_weights: True #是否将预训练模型保存在静态图形模式 cal_metric_during_train: False #是否设置中值评估 ...
to(self.device) predict_data_loader = [(input_ids, token_type_ids, mask)] batch_probs = [] self.model.eval() with torch.no_grad(): for x in predict_data_loader: batch_prob = self.model(x) batch_probs.append(batch_prob.cpu().numpy()) batch_probs = np.concatenate(batch_pr...
eval() loss_all = 0 eval_steps = 0 formatted_outputs = [] current_idx = 0 for batch in tqdm(data_loader, total=len(data_loader)): eval_steps += 1 input_ids, seq_len, tok_to_orig_start_index, tok_to_orig_end_index, labels = batch logits = model(input_ids=input_ids) mask ...
因此选择一个合适的 batch_size 是很重要的一步;log_interval:每隔 10 step 打印一次训练日志;eval_interval:每隔 50 step 在验证集上进行一次性能评估;checkpoint_dir:将训练的参数和数据保存到 cv_Fine-tune_turtorial_demo 目录中;strategy:使用 DefaultFine-tuneStrategy 策略进行 Fine-tune;更多运行配置,请查看...
我这里最后一次训练保存的文件夹是step_1200,因此填入step_1200,要依据自己的情况填入。然后一句命令就够了: 代码语言:javascript 代码运行次数:0 复制 Cloud Studio代码运行 $ sh run.sh eval 可以看到我的模型准确率大概有98%,还是挺不错的。 5.预测 ...