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update evaluation_strategy to eval_strategy in all examples and transformers_doc en cases (#570)
* update `evaluation_strategy` to `eval_strategy` in all examples and transformers_doc cases Signed-off-by: YAO Matrix <[email protected]> * update Signed-off-by: YAO Matrix <[email protected]> * fix typo Signed-off-by: YAO Matrix <[email protected]> --------- Signed-off-by: YAO Matrix <[email protected]>
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‎examples/audio_classification.ipynb

+1-1
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@@ -1370,7 +1370,7 @@
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"\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-ks\",\n",
1373-
" evaluation_strategy = \"epoch\",\n",
1373+
" eval_strategy = \"epoch\",\n",
13741374
" save_strategy = \"epoch\",\n",
13751375
" learning_rate=3e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/idefics/finetune_image_captioning.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -104,7 +104,7 @@ def ds_transforms(example_batch):
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gradient_accumulation_steps=2,
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dataloader_pin_memory=False,
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save_total_limit=3,
107-
evaluation_strategy="steps",
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eval_strategy="steps",
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save_strategy="steps",
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save_steps=1000, # don't save until ready...
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eval_steps=40,

‎examples/idefics/finetune_image_captioning_peft.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -765,7 +765,7 @@
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" gradient_accumulation_steps=8,\n",
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" dataloader_pin_memory=False,\n",
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" save_total_limit=3,\n",
768-
" evaluation_strategy=\"steps\",\n",
768+
" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
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" save_steps=40,\n",
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" eval_steps=20,\n",

‎examples/idefics/idefics_zero3_finetuning/idefics_zero3_finetuning.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -113,7 +113,7 @@ def ds_transforms(example_batch):
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# gradient_checkpointing=True, # Uncomment if OOM
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dataloader_pin_memory=False,
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save_total_limit=3,
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evaluation_strategy="steps",
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eval_strategy="steps",
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save_strategy="steps",
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save_steps=40,
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eval_steps=20,

‎examples/image_classification.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -1221,7 +1221,7 @@
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-eurosat\",\n",
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" remove_unused_columns=False,\n",
1224-
" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/image_classification_albumentations.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -1305,7 +1305,7 @@
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-eurosat-albumentations\",\n",
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" remove_unused_columns=False,\n",
1308-
" evaluation_strategy = \"epoch\",\n",
1308+
" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/image_classification_kornia.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -1432,7 +1432,7 @@
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-eurosat-kornia\",\n",
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" remove_unused_columns=False,\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/language_modeling.ipynb

+2-2
Original file line numberDiff line numberDiff line change
@@ -709,7 +709,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"training_args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-wikitext2\",\n",
712-
" evaluation_strategy = \"epoch\",\n",
712+
" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" weight_decay=0.01,\n",
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" push_to_hub=True,\n",
@@ -1064,7 +1064,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"training_args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-wikitext2\",\n",
1067-
" evaluation_strategy = \"epoch\",\n",
1067+
" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" weight_decay=0.01,\n",
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" push_to_hub=True,\n",

‎examples/language_modeling_from_scratch.ipynb

+2-2
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@@ -703,7 +703,7 @@
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"source": [
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"training_args = TrainingArguments(\n",
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" f\"{model_checkpoint}-wikitext2\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" weight_decay=0.01,\n",
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" push_to_hub=True\n",
@@ -1342,7 +1342,7 @@
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"source": [
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"training_args = TrainingArguments(\n",
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" \"test-clm\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" weight_decay=0.01,\n",
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" push_to_hub=True,\n",

‎examples/multi_lingual_speech_recognition.ipynb

+1-1
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@@ -2455,7 +2455,7 @@
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" group_by_length=True,\n",
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" per_device_train_batch_size=batch_size,\n",
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" gradient_accumulation_steps=2,\n",
2458-
" evaluation_strategy=\"steps\",\n",
2458+
" eval_strategy=\"steps\",\n",
24592459
" num_train_epochs=30,\n",
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" gradient_checkpointing=True,\n",
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" fp16=True,\n",

‎examples/multiple_choice.ipynb

+1-1
Original file line numberDiff line numberDiff line change
@@ -960,7 +960,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-swag\",\n",
963-
" evaluation_strategy = \"epoch\",\n",
963+
" eval_strategy = \"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/nucleotide_transformer_dna_sequence_modelling.ipynb

+2-2
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@@ -672,7 +672,7 @@
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"args_promoter = TrainingArguments(\n",
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" f\"{model_name}-finetuned-NucleotideTransformer\",\n",
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" remove_unused_columns=False,\n",
675-
" evaluation_strategy=\"steps\",\n",
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" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
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" learning_rate=1e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
@@ -1348,7 +1348,7 @@
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"args_enhancers = TrainingArguments(\n",
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" f\"{model_name}-finetuned-NucleotideTransformer\",\n",
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" remove_unused_columns=False,\n",
1351-
" evaluation_strategy=\"steps\",\n",
1351+
" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
13531353
" learning_rate=1e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/nucleotide_transformer_dna_sequence_modelling_with_peft.ipynb

+2-2
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@@ -882,7 +882,7 @@
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"args_promoter = TrainingArguments(\n",
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" f\"{model_name}-finetuned-lora-NucleotideTransformer\",\n",
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" remove_unused_columns=False,\n",
885-
" evaluation_strategy=\"steps\",\n",
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" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
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" learning_rate=5e-4,\n",
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" per_device_train_batch_size=batch_size,\n",
@@ -1901,7 +1901,7 @@
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"args_enhancers = TrainingArguments(\n",
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" f\"{model_name}-finetuned-lora-NucleotideTransformer\",\n",
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" remove_unused_columns=False,\n",
1904-
" evaluation_strategy=\"steps\",\n",
1904+
" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
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" learning_rate=5e-4,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/patch_tsmixer.ipynb

+2-2
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@@ -629,7 +629,7 @@
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" learning_rate=0.001,\n",
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" num_train_epochs=100, # For a quick test of this notebook, set it to 1\n",
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" do_eval=True,\n",
632-
" evaluation_strategy=\"epoch\",\n",
632+
" eval_strategy=\"epoch\",\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",
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" dataloader_num_workers=num_workers,\n",
@@ -1056,7 +1056,7 @@
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" learning_rate=0.0001,\n",
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" num_train_epochs=100,\n",
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" do_eval=True,\n",
1059-
" evaluation_strategy=\"epoch\",\n",
1059+
" eval_strategy=\"epoch\",\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",
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" dataloader_num_workers=num_workers,\n",

‎examples/patch_tst.ipynb

+2-2
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@@ -796,7 +796,7 @@
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" # learning_rate=0.001,\n",
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" num_train_epochs=100,\n",
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" do_eval=True,\n",
799-
" evaluation_strategy=\"epoch\",\n",
799+
" eval_strategy=\"epoch\",\n",
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" per_device_train_batch_size=batch_size,\n",
801801
" per_device_eval_batch_size=batch_size,\n",
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" dataloader_num_workers=num_workers,\n",
@@ -1120,7 +1120,7 @@
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" learning_rate=0.0001,\n",
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" num_train_epochs=100,\n",
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" do_eval=True,\n",
1123-
" evaluation_strategy=\"epoch\",\n",
1123+
" eval_strategy=\"epoch\",\n",
11241124
" per_device_train_batch_size=batch_size,\n",
11251125
" per_device_eval_batch_size=batch_size,\n",
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" dataloader_num_workers=num_workers,\n",

‎examples/protein_language_modeling.ipynb

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@@ -1069,7 +1069,7 @@
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"\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-localization\",\n",
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" evaluation_strategy = \"epoch\",\n",
1072+
" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
@@ -2084,7 +2084,7 @@
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"\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-secondary-structure\",\n",
2087-
" evaluation_strategy = \"epoch\",\n",
2087+
" eval_strategy = \"epoch\",\n",
20882088
" save_strategy = \"epoch\",\n",
20892089
" learning_rate=1e-4,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/question_answering.ipynb

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@@ -1189,7 +1189,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-squad\",\n",
1192-
" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/question_answering_ort.ipynb

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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = ORTTrainingArguments(\n",
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" output_dir=f\"{model_name}-finetuned-squad\",\n",
635-
" evaluation_strategy = \"epoch\",\n",
635+
" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",
@@ -963,7 +963,7 @@
963963
" model_name = model_checkpoint.split(\"/\")[-1]\n",
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" args = ORTTrainingArguments(\n",
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" output_dir=f\"{model_name}-finetuned-squad\",\n",
966-
" evaluation_strategy = \"epoch\",\n",
966+
" eval_strategy = \"epoch\",\n",
967967
" learning_rate=2e-5,\n",
968968
" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/semantic_segmentation.ipynb

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@@ -854,7 +854,7 @@
854854
" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",
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" save_total_limit=3,\n",
857-
" evaluation_strategy=\"steps\",\n",
857+
" eval_strategy=\"steps\",\n",
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" save_strategy=\"steps\",\n",
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" save_steps=20,\n",
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" eval_steps=20,\n",

‎examples/speech_recognition.ipynb

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@@ -2005,7 +2005,7 @@
20052005
" output_dir=repo_name,\n",
20062006
" group_by_length=True,\n",
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" per_device_train_batch_size=32,\n",
2008-
" evaluation_strategy=\"steps\",\n",
2008+
" eval_strategy=\"steps\",\n",
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" num_train_epochs=30,\n",
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" fp16=True,\n",
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" gradient_checkpointing=True,\n",

‎examples/summarization.ipynb

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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = Seq2SeqTrainingArguments(\n",
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" f\"{model_name}-finetuned-xsum\",\n",
837-
" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/summarization_ort.ipynb

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@@ -638,7 +638,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = ORTSeq2SeqTrainingArguments(\n",
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" f\"{model_name}-finetuned-xsum\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=learning_rate,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/text_classification.ipynb

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@@ -994,7 +994,7 @@
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"\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-{task}\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/text_classification_ort.ipynb

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"\n",
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"args = ORTTrainingArguments(\n",
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" f\"{model_name}-finetuned-{task}\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/text_classification_quantization_inc.ipynb

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@@ -506,7 +506,7 @@
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" output_dir = save_directory,\n",
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" do_train=True,\n",
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" do_eval=False,\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" save_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎examples/token_classification.ipynb

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@@ -1053,7 +1053,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = TrainingArguments(\n",
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" f\"{model_name}-finetuned-{task}\",\n",
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" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/translation.ipynb

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@@ -803,7 +803,7 @@
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"model_name = model_checkpoint.split(\"/\")[-1]\n",
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"args = Seq2SeqTrainingArguments(\n",
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" f\"{model_name}-finetuned-{source_lang}-to-{target_lang}\",\n",
806-
" evaluation_strategy = \"epoch\",\n",
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" eval_strategy = \"epoch\",\n",
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" learning_rate=2e-5,\n",
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" per_device_train_batch_size=batch_size,\n",
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" per_device_eval_batch_size=batch_size,\n",

‎examples/video_classification.ipynb

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@@ -1086,7 +1086,7 @@
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"args = TrainingArguments(\n",
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" new_model_name,\n",
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" remove_unused_columns=False,\n",
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" evaluation_strategy=\"epoch\",\n",
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" eval_strategy=\"epoch\",\n",
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" save_strategy=\"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=batch_size,\n",

‎transformers_doc/en/asr.ipynb

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@@ -526,7 +526,7 @@
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" gradient_checkpointing=True,\n",
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" fp16=True,\n",
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" group_by_length=True,\n",
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" evaluation_strategy=\"steps\",\n",
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" eval_strategy=\"steps\",\n",
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" per_device_eval_batch_size=8,\n",
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" save_steps=1000,\n",
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" eval_steps=1000,\n",

‎transformers_doc/en/audio_classification.ipynb

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@@ -471,7 +471,7 @@
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=\"my_awesome_mind_model\",\n",
474-
" evaluation_strategy=\"epoch\",\n",
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" eval_strategy=\"epoch\",\n",
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" save_strategy=\"epoch\",\n",
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" learning_rate=3e-5,\n",
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" per_device_train_batch_size=32,\n",

‎transformers_doc/en/document_question_answering.ipynb

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@@ -680,7 +680,7 @@
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" num_train_epochs=20,\n",
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" save_steps=200,\n",
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" logging_steps=50,\n",
683-
" evaluation_strategy=\"steps\",\n",
683+
" eval_strategy=\"steps\",\n",
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" learning_rate=5e-5,\n",
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" save_total_limit=2,\n",
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" remove_unused_columns=False,\n",

‎transformers_doc/en/image_captioning.ipynb

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@@ -315,7 +315,7 @@
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" per_device_eval_batch_size=32,\n",
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" gradient_accumulation_steps=2,\n",
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" save_total_limit=3,\n",
318-
" evaluation_strategy=\"steps\",\n",
318+
" eval_strategy=\"steps\",\n",
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" eval_steps=50,\n",
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" save_strategy=\"steps\",\n",
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" save_steps=50,\n",

‎transformers_doc/en/image_classification.ipynb

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Original file line numberDiff line numberDiff line change
@@ -552,7 +552,7 @@
552552
"training_args = TrainingArguments(\n",
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" output_dir=\"my_awesome_food_model\",\n",
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" remove_unused_columns=False,\n",
555-
" evaluation_strategy=\"epoch\",\n",
555+
" eval_strategy=\"epoch\",\n",
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" save_strategy=\"epoch\",\n",
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" learning_rate=5e-5,\n",
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" per_device_train_batch_size=16,\n",

‎transformers_doc/en/language_modeling.ipynb

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Original file line numberDiff line numberDiff line change
@@ -467,7 +467,7 @@
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=\"my_awesome_eli5_clm-model\",\n",
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" evaluation_strategy=\"epoch\",\n",
470+
" eval_strategy=\"epoch\",\n",
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" learning_rate=2e-5,\n",
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" weight_decay=0.01,\n",
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" push_to_hub=True,\n",

‎transformers_doc/en/masked_language_modeling.ipynb

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Original file line numberDiff line numberDiff line change
@@ -458,7 +458,7 @@
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"source": [
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"training_args = TrainingArguments(\n",
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" output_dir=\"my_awesome_eli5_mlm_model\",\n",
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" evaluation_strategy=\"epoch\",\n",
461+
" eval_strategy=\"epoch\",\n",
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" learning_rate=2e-5,\n",
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" num_train_epochs=3,\n",
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" weight_decay=0.01,\n",

‎transformers_doc/en/multilingual.ipynb

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@@ -378,7 +378,7 @@
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}
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],
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"source": [
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"generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id(\"en_XX\"))\n",
381+
"generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.lang_code_to_id[\"en_XX\"])\n",
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"tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)"
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]
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},

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