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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/ja/tasks/asr.md
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@@ -270,7 +270,7 @@ MInDS-14 データセットのサンプリング レートは 8000kHz です (
... 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/ja/tasks/audio_classification.md
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@@ -221,7 +221,7 @@ MInDS-14 データセットのサンプリング レートは 8000khz です (
```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/ja/tasks/document_question_answering.md
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@@ -403,7 +403,7 @@ 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/ja/tasks/image_captioning.md
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@@ -194,7 +194,7 @@ 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/ja/tasks/image_classification.md
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@@ -308,7 +308,7 @@ 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/ja/tasks/knowledge_distillation_for_image_classification.md
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@@ -112,7 +112,7 @@ training_args = TrainingArguments(
fp16=True,
logging_dir=f"{repo_name}/logs",
logging_strategy="epoch",
evaluation_strategy="epoch",
eval_strategy="epoch",
save_strategy="epoch",
load_best_model_at_end=True,
metric_for_best_model="accuracy",

2
docs/source/ja/tasks/language_modeling.md
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@@ -246,7 +246,7 @@ Apply the `group_texts` function over the entire dataset:
```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/ja/tasks/masked_language_modeling.md
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@@ -231,7 +231,7 @@ pip install transformers datasets evaluate
```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/ja/tasks/multiple_choice.md
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@@ -266,7 +266,7 @@ 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/ja/tasks/question_answering.md
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@@ -220,7 +220,7 @@ 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/ja/tasks/semantic_segmentation.md
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@@ -323,7 +323,7 @@ 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/ja/tasks/sequence_classification.md
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@@ -324,7 +324,7 @@ 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/ja/tasks/summarization.md
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@@ -204,7 +204,7 @@ pip install transformers datasets evaluate rouge_score
```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/ja/tasks/text-to-speech.md
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@@ -477,7 +477,7 @@ SpeechT5 では、モデルのデコーダ部分への入力が 2 分の 1 に
... max_steps=4000,
... gradient_checkpointing=True,
... fp16=True,
... evaluation_strategy="steps",
... eval_strategy="steps",
... per_device_eval_batch_size=2,
... save_steps=1000,
... eval_steps=1000,

2
docs/source/ja/tasks/token_classification.md
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@@ -288,7 +288,7 @@ pip install transformers datasets evaluate seqeval
... 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/ja/tasks/translation.md
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@@ -208,7 +208,7 @@ 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/ja/tasks/video_classification.md
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@@ -360,7 +360,7 @@ 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,