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Adopt framework-specific blocks for content (#16342)

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* ✨ refactor code samples with framework-specific blocks

* ✨ update training.mdx

* 🖍 apply feedback
This commit is contained in:
Steven Liu
2022-03-22 14:14:58 -07:00
committed by GitHub
parent 62cbd8423b
commit 7732148124
13 changed files with 169 additions and 133 deletions
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6
docs/source/tasks/asr.mdx
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@@ -155,8 +155,10 @@ Create a batch of examples and dynamically pad them with `DataCollatorForCTCWith
>>> data_collator = DataCollatorCTCWithPadding(processor=processor, padding=True)
```
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load Wav2Vec2 with [`AutoModelForCTC`]. For `ctc_loss_reduction`, it is often better to use the average instead of the default summation:
```py
@@ -206,6 +208,8 @@ At this point, only three steps remain:
>>> trainer.train()
```
</pt>
</frameworkcontent>
<Tip>

6
docs/source/tasks/audio_classification.mdx
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@@ -91,8 +91,10 @@ Use 🤗 Datasets [`map`](https://huggingface.co/docs/datasets/package_reference
>>> encoded_ks = ks.map(preprocess_function, remove_columns=["audio", "file"], batched=True)
```
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load Wav2Vec2 with [`AutoModelForAudioClassification`]. Specify the number of labels, and pass the model the mapping between label number and label class:
```py
@@ -135,6 +137,8 @@ At this point, only three steps remain:
>>> trainer.train()
```
</pt>
</frameworkcontent>
<Tip>

6
docs/source/tasks/image_classification.mdx
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@@ -109,8 +109,10 @@ Use [`DefaultDataCollator`] to create a batch of examples. Unlike other data col
>>> data_collator = DefaultDataCollator()
```
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load ViT with [`AutoModelForImageClassification`]. Specify the number of labels, and pass the model the mapping between label number and label class:
```py
@@ -162,6 +164,8 @@ At this point, only three steps remain:
>>> trainer.train()
```
</pt>
</frameworkcontent>
<Tip>

54
docs/source/tasks/language_modeling.mdx
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@@ -200,8 +200,10 @@ For masked language modeling, use the same [`DataCollatorForLanguageModeling`] e
Causal language modeling is frequently used for text generation. This section shows you how to fine-tune [DistilGPT2](https://huggingface.co/distilgpt2) to generate new text.
### Fine-tune with Trainer
### Train
<frameworkcontent>
<pt>
Load DistilGPT2 with [`AutoModelForCausalLM`]:
```py
@@ -240,18 +242,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
### Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = lm_dataset["train"].to_tf_dataset(
@@ -271,6 +264,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate, and some training hyperparameters:
```py
@@ -300,13 +299,17 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
## Masked language modeling
Masked language modeling is also known as a fill-mask task because it predicts a masked token in a sequence. Models for masked language modeling require a good contextual understanding of an entire sequence instead of only the left context. This section shows you how to fine-tune [DistilRoBERTa](https://huggingface.co/distilroberta-base) to predict a masked word.
### Fine-tune with Trainer
### Train
<frameworkcontent>
<pt>
Load DistilRoBERTa with [`AutoModelForMaskedlM`]:
```py
@@ -346,18 +349,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
### Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = lm_dataset["train"].to_tf_dataset(
@@ -377,6 +371,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate, and some training hyperparameters:
```py
@@ -406,6 +406,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>

29
docs/source/tasks/multiple_choice.mdx
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@@ -176,8 +176,10 @@ tokenized_swag = swag.map(preprocess_function, batched=True)
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load BERT with [`AutoModelForMultipleChoice`]:
```py
@@ -220,18 +222,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs in `columns`, targets in `label_cols`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs in `columns`, targets in `label_cols`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> data_collator = DataCollatorForMultipleChoice(tokenizer=tokenizer)
@@ -252,6 +245,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -284,4 +283,6 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=2)
```
```
</tf>
</frameworkcontent>

27
docs/source/tasks/question_answering.mdx
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@@ -151,8 +151,10 @@ Use [`DefaultDataCollator`] to create a batch of examples. Unlike other data col
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load DistilBERT with [`AutoModelForQuestionAnswering`]:
```py
@@ -195,18 +197,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and the start and end positions of an answer in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and the start and end positions of an answer in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = tokenized_squad["train"].to_tf_dataset(
@@ -226,6 +219,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -262,6 +261,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>

27
docs/source/tasks/sequence_classification.mdx
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@@ -91,8 +91,10 @@ Use [`DataCollatorWithPadding`] to create a batch of examples. It will also *dyn
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load DistilBERT with [`AutoModelForSequenceClassification`] along with the number of expected labels:
```py
@@ -140,18 +142,9 @@ At this point, only three steps remain:
[`Trainer`] will apply dynamic padding by default when you pass `tokenizer` to it. In this case, you don't need to specify a data collator explicitly.
</Tip>
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = tokenized_imdb["train"].to_tf_dataset(
@@ -169,6 +162,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -203,6 +202,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>

27
docs/source/tasks/summarization.mdx
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@@ -110,8 +110,10 @@ Use [`DataCollatorForSeq2Seq`] to create a batch of examples. It will also *dyna
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load T5 with [`AutoModelForSeq2SeqLM`]:
```py
@@ -156,18 +158,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = tokenized_billsum["train"].to_tf_dataset(
@@ -185,6 +178,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -212,6 +211,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>

27
docs/source/tasks/token_classification.mdx
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@@ -151,8 +151,10 @@ Use [`DataCollatorForTokenClassification`] to create a batch of examples. It wil
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load DistilBERT with [`AutoModelForTokenClassification`] along with the number of expected labels:
```py
@@ -195,18 +197,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = tokenized_wnut["train"].to_tf_dataset(
@@ -224,6 +217,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -261,6 +260,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_validation_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>

27
docs/source/tasks/translation.mdx
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@@ -112,8 +112,10 @@ Use [`DataCollatorForSeq2Seq`] to create a batch of examples. It will also *dyna
</tf>
</frameworkcontent>
## Fine-tune with Trainer
## Train
<frameworkcontent>
<pt>
Load T5 with [`AutoModelForSeq2SeqLM`]:
```py
@@ -158,18 +160,9 @@ At this point, only three steps remain:
>>> trainer.train()
```
## Fine-tune with TensorFlow
To fine-tune a model in TensorFlow is just as easy, with only a few differences.
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](../training#finetune-with-keras)!
</Tip>
Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
</pt>
<tf>
To fine-tune a model in TensorFlow, start by converting your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.to_tf_dataset). Specify inputs and labels in `columns`, whether to shuffle the dataset order, batch size, and the data collator:
```py
>>> tf_train_set = tokenized_books["train"].to_tf_dataset(
@@ -187,6 +180,12 @@ Convert your datasets to the `tf.data.Dataset` format with [`to_tf_dataset`](htt
... )
```
<Tip>
If you aren't familiar with fine-tuning a model with Keras, take a look at the basic tutorial [here](training#finetune-with-keras)!
</Tip>
Set up an optimizer function, learning rate schedule, and some training hyperparameters:
```py
@@ -214,6 +213,8 @@ Call [`fit`](https://keras.io/api/models/model_training_apis/#fit-method) to fin
```py
>>> model.fit(x=tf_train_set, validation_data=tf_test_set, epochs=3)
```
</tf>
</frameworkcontent>
<Tip>