ruff pytest_ruff.RuffError: flair/trainers/trainer.py:348:56: W291 [*] Trailing whitespace | 346 | monitor_train_sample: Set this to evaluate on a sample of the train data at the end of each epoch. 347 | If you set an int, it will sample this many sentences to evaluate on. If you set a float, it will sample 348 | a percentage of data points from train. | ^^^^^^^^^^^^^^^^ W291 349 | max_grad_norm (Optional[float]): If not None, gradients are clipped to this value before an optimizer.step is 350 | called. | = help: Remove trailing whitespace flair/trainers/trainer.py:350:24: W291 [*] Trailing whitespace | 348 | a percentage of data points from train. 349 | max_grad_norm (Optional[float]): If not None, gradients are clipped to this value before an optimizer.step is 350 | called. | ^^^^ W291 351 | use_final_model_for_eval (bool): If True, the final model is used for the final evaluation. If False, the 352 | model from the best epoch as determined by main_evaluation_metric is used for the final evaluation. | = help: Remove trailing whitespace flair/trainers/trainer.py:599:52: W291 [*] Trailing whitespace | 598 | # do the optimizer step 599 | scaler.unscale_(self.optimizer) | ^^^^^^^^^^^^^^^^^^^^ W291 600 | if max_grad_norm is not None: 601 | torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm) | = help: Remove trailing whitespace

Black format check --- /home/runner/work/flair/flair/flair/trainers/trainer.py 2023-08-08 14:46:53.706293 +0000 +++ /home/runner/work/flair/flair/flair/trainers/trainer.py 2023-08-08 14:52:06.796250 +0000 @@ -343,13 +343,13 @@ train_with_test (bool): If True, the data from test split is added to the training data main_evaluation_metric: The metric to optimize (often micro-average or macro-average F1-score, or accuracy) monitor_test (bool): If True, test data is evaluated at end of each epoch monitor_train_sample: Set this to evaluate on a sample of the train data at the end of each epoch. If you set an int, it will sample this many sentences to evaluate on. If you set a float, it will sample - a percentage of data points from train. + a percentage of data points from train. max_grad_norm (Optional[float]): If not None, gradients are clipped to this value before an optimizer.step is - called. + called. use_final_model_for_eval (bool): If True, the final model is used for the final evaluation. If False, the model from the best epoch as determined by main_evaluation_metric is used for the final evaluation. gold_label_dictionary_for_eval: Set to force evaluation to use a particular label dictionary exclude_labels: Optionally define a list of labels to exclude from the evaluation sampler: You can pass a data sampler here for special sampling of data. @@ -594,11 +594,11 @@ store_embeddings(batch_step, embeddings_storage_mode, dynamic_embeddings) self.dispatch("before_training_optimizer_step", **batch_kw) # do the optimizer step - scaler.unscale_(self.optimizer) + scaler.unscale_(self.optimizer) if max_grad_norm is not None: torch.nn.utils.clip_grad_norm_(self.model.parameters(), max_grad_norm) scale_before = scaler.get_scale() scaler.step(self.optimizer) scaler.update()