Open Markkk111 opened 1 year ago
Hello! I see that you have the same problem as me, did you solve it? If solved, how to solve it? Looking forward to your answer, very anxious! @Markkk111
Hi @Markkk111 @25018528927 , I am also facing this similar issue, anyone of you able to solve this?
You're welcome! I'm glad to assist you with this question!
tokenizers
before the fork if possibletokenizers
before the fork if possibletokenizers
before the fork if possibletokenizers
before the fork if possibleTraining completed. Do not forget to share your model on huggingface.co/models =)
{'train_runtime': 1749.401, 'train_samples_per_second': 142.815, 'train_steps_per_second': 14.281, 'train_loss': 0.023130248267032992, 'epoch': 6.0} [INFO|trainer.py:2709] 2023-04-19 17:43:28,693 >> Saving model checkpoint to classifier_models/e2e-tgt-tree_e=6_b=10_m=bert-base-uncased_wikitext-103-raw-v1_101_wp_None [INFO|configuration_utils.py:453] 2023-04-19 17:43:28,694 >> Configuration saved in classifier_models/e2e-tgt-tree_e=6_b=10_m=bert-base-uncased_wikitext-103-raw-v1_101_wp_None/config.json [INFO|modeling_utils.py:1704] 2023-04-19 17:43:29,841 >> Model weights saved in classifier_models/e2e-tgt-tree_e=6_b=10_m=bert-base-uncased_wikitext-103-raw-v1_101_wp_None/pytorch_model.bin train metrics epoch = 6.0 train_loss = 0.0231 train_runtime = 0:29:09.40 train_samples = 41640 train_samples_per_second = 142.815 train_steps_per_second = 14.281 04/19/2023 17:43:29 - INFO - main - Evaluate [INFO|trainer.py:710] 2023-04-19 17:43:29,848 >> The following columns in the evaluation set don't have a corresponding argument in
Classifier_Tree.forward
and have been ignored: chart_lst. If chart_lst are not expected byClassifier_Tree.forward
, you can safely ignore this message. [INFO|trainer.py:2964] 2023-04-19 17:43:29,850 >> Running Evaluation [INFO|trainer.py:2966] 2023-04-19 17:43:29,850 >> Num examples = 421 [INFO|trainer.py:2969] 2023-04-19 17:43:29,851 >> Batch size = 10 huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks... To disable this warning, you can either:tokenizers
before the fork if possibletokenizers
before the fork if possibletokenizers
before the fork if possibletokenizers
before the fork if possibleThe relevant code that caused the error is as follows:
Training
if training_args.do_train: checkpoint = None if training_args.resume_from_checkpoint is not None: checkpoint = training_args.resume_from_checkpoint elif last_checkpoint is not None: checkpoint = last_checkpoint train_result = trainer.train(resume_from_checkpoint=checkpoint) trainer.save_model() # Saves the tokenizer too for easy upload
Evaluation
if training_args.do_eval: logger.info(" Evaluate ")
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"} if data_args.dataset_name is not None: kwargs["dataset_tags"] = data_args.dataset_name if data_args.dataset_config_name is not None: kwargs["dataset_args"] = data_args.dataset_config_name kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}" else: kwargs["dataset"] = data_args.dataset_name
if training_args.push_to_hub: trainer.push_to_hub(kwargs) else: trainer.create_model_card(kwargs)