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[docs] fix invalid class name (#11438)

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* fix invalid class name

* proper ref

* proper ref
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Stas Bekman 2021-04-26 08:37:32 -07:00 committed by GitHub
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commit a753cafdc0
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2 changed files with 4 additions and 4 deletions
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4
docs/source/migration.md
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@ -169,8 +169,8 @@ Regarding the `TFTrainer` class:
- The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`. - The `TFTrainer` method `_setup_wandb` is deprecated in favor of `setup_wandb`.
- The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`. - The `TFTrainer` method `_run_model` is deprecated in favor of `run_model`.
Regarding the `TrainerArgument` class: Regarding the `TrainingArguments` class:
- The `TrainerArgument` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`. - The `TrainingArguments` argument `evaluate_during_training` is deprecated in favor of `evaluation_strategy`.
Regarding the Transfo-XL model: Regarding the Transfo-XL model:
- The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`. - The Transfo-XL configuration attribute `tie_weight` becomes `tie_words_embeddings`.

4
src/transformers/integrations.py
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@ -856,8 +856,8 @@ class MLflowCallback(TrainerCallback):
Whether to use MLflow .log_artifact() facility to log artifacts. Whether to use MLflow .log_artifact() facility to log artifacts.
This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `1`, will copy This only makes sense if logging to a remote server, e.g. s3 or GCS. If set to `True` or `1`, will copy
whatever is in TrainerArgument's output_dir to the local or remote artifact storage. Using it without a whatever is in :class:`~transformers.TrainingArguments`'s ``output_dir`` to the local or remote
remote storage will just copy the files to your artifact location. artifact storage. Using it without a remote storage will just copy the files to your artifact location.
""" """
log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper() log_artifacts = os.getenv("HF_MLFLOW_LOG_ARTIFACTS", "FALSE").upper()
if log_artifacts in {"TRUE", "1"}: if log_artifacts in {"TRUE", "1"}: