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🚨🚨🚨Deprecate evaluation_strategy to eval_strategy🚨🚨🚨 (#30190)

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This commit is contained in:
Zach Mueller
2024-04-18 12:49:43 -04:00
committed by GitHub
parent c86d020ead
commit 60d5f8f9f0
116 changed files with 214 additions and 203 deletions
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2
docs/source/ko/model_memory_anatomy.md
View File

@@ -132,7 +132,7 @@ Tue Jan 11 08:58:05 2022
```py
default_args = {
"output_dir": "tmp",
"evaluation_strategy": "steps",
"eval_strategy": "steps",
"num_train_epochs": 1,
"log_level": "error",
"report_to": "none",

2
docs/source/ko/tasks/asr.md
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@@ -274,7 +274,7 @@ MInDS-14 데이터 세트의 샘플링 레이트는 8000kHz이므로([데이터
... gradient_checkpointing=True,
... fp16=True,
... group_by_length=True,
... evaluation_strategy="steps",
... eval_strategy="steps",
... per_device_eval_batch_size=8,
... save_steps=1000,
... eval_steps=1000,

2
docs/source/ko/tasks/audio_classification.md
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@@ -221,7 +221,7 @@ MinDS-14 데이터 세트의 샘플링 속도는 8000khz이므로(이 정보는
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_mind_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... learning_rate=3e-5,
... per_device_train_batch_size=32,

2
docs/source/ko/tasks/document_question_answering.md
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@@ -385,7 +385,7 @@ end_index 18
... num_train_epochs=20,
... save_steps=200,
... logging_steps=50,
... evaluation_strategy="steps",
... eval_strategy="steps",
... learning_rate=5e-5,
... save_total_limit=2,
... remove_unused_columns=False,

2
docs/source/ko/tasks/image_captioning.md
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@@ -201,7 +201,7 @@ training_args = TrainingArguments(
per_device_eval_batch_size=32,
gradient_accumulation_steps=2,
save_total_limit=3,
evaluation_strategy="steps",
eval_strategy="steps",
eval_steps=50,
save_strategy="steps",
save_steps=50,

2
docs/source/ko/tasks/image_classification.md
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@@ -301,7 +301,7 @@ food["test"].set_transform(preprocess_val)
>>> training_args = TrainingArguments(
... output_dir="my_awesome_food_model",
... remove_unused_columns=False,
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=16,

2
docs/source/ko/tasks/language_modeling.md
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@@ -233,7 +233,7 @@ pip install transformers datasets evaluate
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_eli5_clm-model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... learning_rate=2e-5,
... weight_decay=0.01,
... push_to_hub=True,

2
docs/source/ko/tasks/masked_language_modeling.md
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@@ -236,7 +236,7 @@ Hugging Face 계정에 로그인하여 모델을 업로드하고 커뮤니티와
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_eli5_mlm_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... learning_rate=2e-5,
... num_train_epochs=3,
... weight_decay=0.01,

2
docs/source/ko/tasks/multiple_choice.md
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@@ -265,7 +265,7 @@ tokenized_swag = swag.map(preprocess_function, batched=True)
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_swag_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... learning_rate=5e-5,

2
docs/source/ko/tasks/question_answering.md
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@@ -215,7 +215,7 @@ pip install transformers datasets evaluate
```py
>>> training_args = TrainingArguments(
... output_dir="my_awesome_qa_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,

2
docs/source/ko/tasks/semantic_segmentation.md
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@@ -317,7 +317,7 @@ pip install -q datasets transformers evaluate
... per_device_train_batch_size=2,
... per_device_eval_batch_size=2,
... save_total_limit=3,
... evaluation_strategy="steps",
... eval_strategy="steps",
... save_strategy="steps",
... save_steps=20,
... eval_steps=20,

2
docs/source/ko/tasks/sequence_classification.md
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@@ -185,7 +185,7 @@ tokenized_imdb = imdb.map(preprocess_function, batched=True)
... per_device_eval_batch_size=16,
... num_train_epochs=2,
... weight_decay=0.01,
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,

2
docs/source/ko/tasks/summarization.md
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@@ -211,7 +211,7 @@ Hugging Face 계정에 로그인하면 모델을 업로드하고 커뮤니티에
```py
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="my_awesome_billsum_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,

2
docs/source/ko/tasks/token_classification.md
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@@ -288,7 +288,7 @@ Hugging Face 계정에 로그인하여 모델을 업로드하고 커뮤니티에
... per_device_eval_batch_size=16,
... num_train_epochs=2,
... weight_decay=0.01,
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... load_best_model_at_end=True,
... push_to_hub=True,

2
docs/source/ko/tasks/translation.md
View File

@@ -209,7 +209,7 @@ pip install transformers datasets evaluate sacrebleu
```py
>>> training_args = Seq2SeqTrainingArguments(
... output_dir="my_awesome_opus_books_model",
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... learning_rate=2e-5,
... per_device_train_batch_size=16,
... per_device_eval_batch_size=16,

2
docs/source/ko/tasks/video_classification.md
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@@ -358,7 +358,7 @@ You should probably TRAIN this model on a down-stream task to be able to use it
>>> args = TrainingArguments(
... new_model_name,
... remove_unused_columns=False,
... evaluation_strategy="epoch",
... eval_strategy="epoch",
... save_strategy="epoch",
... learning_rate=5e-5,
... per_device_train_batch_size=batch_size,

4
docs/source/ko/training.md
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@@ -129,12 +129,12 @@ rendered properly in your Markdown viewer.
... return metric.compute(predictions=predictions, references=labels)
```
미세 튜닝 중에 평가 지표를 모니터링하려면 훈련 인수에 `evaluation_strategy` 파라미터를 지정하여 각 에폭이 끝날 때 평가 지표를 확인할 수 있습니다:
미세 튜닝 중에 평가 지표를 모니터링하려면 훈련 인수에 `eval_strategy` 파라미터를 지정하여 각 에폭이 끝날 때 평가 지표를 확인할 수 있습니다:
```py
>>> from transformers import TrainingArguments, Trainer
>>> training_args = TrainingArguments(output_dir="test_trainer", evaluation_strategy="epoch")
>>> training_args = TrainingArguments(output_dir="test_trainer", eval_strategy="epoch")
```
### 훈련 하기[[trainer]]