Update translation.mdx (#18169)
* Update translation.mdx * update translation.mdx by running make style
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@ -93,10 +93,32 @@ Use 🤗 Datasets [`~datasets.Dataset.map`] function to apply the preprocessing
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>>> tokenized_books = books.map(preprocess_function, batched=True)
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```
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<frameworkcontent>
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<pt>
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Load T5 with [`AutoModelForSeq2SeqLM`]:
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```py
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>>> from transformers import AutoModelForSeq2SeqLM
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
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```
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</pt>
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<tf>
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Load T5 with [`TFAutoModelForSeq2SeqLM`]:
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```py
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>>> from transformers import TFAutoModelForSeq2SeqLM
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>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
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```
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</tf>
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</frameworkcontent>
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Use [`DataCollatorForSeq2Seq`] to create a batch of examples. It will also *dynamically pad* your text and labels to the length of the longest element in its batch, so they are a uniform length. While it is possible to pad your text in the `tokenizer` function by setting `padding=True`, dynamic padding is more efficient.
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<frameworkcontent>
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<pt>
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```py
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>>> from transformers import DataCollatorForSeq2Seq
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@ -104,6 +126,7 @@ Use [`DataCollatorForSeq2Seq`] to create a batch of examples. It will also *dyna
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```
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</pt>
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<tf>
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```py
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>>> from transformers import DataCollatorForSeq2Seq
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@ -116,13 +139,6 @@ Use [`DataCollatorForSeq2Seq`] to create a batch of examples. It will also *dyna
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<frameworkcontent>
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<pt>
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Load T5 with [`AutoModelForSeq2SeqLM`]:
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```py
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>>> from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer
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>>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
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```
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<Tip>
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@ -137,6 +153,8 @@ At this point, only three steps remain:
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3. Call [`~Trainer.train`] to fine-tune your model.
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```py
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>>> from transformers import Seq2SeqTrainingArguments, Seq2SeqTrainer
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>>> training_args = Seq2SeqTrainingArguments(
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... output_dir="./results",
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... evaluation_strategy="epoch",
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@ -194,14 +212,6 @@ Set up an optimizer function, learning rate schedule, and some training hyperpar
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>>> optimizer = AdamWeightDecay(learning_rate=2e-5, weight_decay_rate=0.01)
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```
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Load T5 with [`TFAutoModelForSeq2SeqLM`]:
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```py
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>>> from transformers import TFAutoModelForSeq2SeqLM
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>>> model = TFAutoModelForSeq2SeqLM.from_pretrained("t5-small")
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```
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Configure the model for training with [`compile`](https://keras.io/api/models/model_training_apis/#compile-method):
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```py
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@ -222,4 +232,4 @@ For a more in-depth example of how to fine-tune a model for translation, take a
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[PyTorch notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation.ipynb)
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or [TensorFlow notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/translation-tf.ipynb).
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</Tip>
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</Tip>
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