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319 lines
14 KiB
ReStructuredText
Training and fine-tuning
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=======================================================================================================================
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Model classes in 🤗 Transformers are designed to be compatible with native
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PyTorch and TensorFlow 2 and can be used seemlessly with either. In this
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quickstart, we will show how to fine-tune (or train from scratch) a model
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using the standard training tools available in either framework. We will also
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show how to use our included :func:`~transformers.Trainer` class which
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handles much of the complexity of training for you.
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This guide assume that you are already familiar with loading and use our
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models for inference; otherwise, see the :doc:`task summary <task_summary>`. We also assume
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that you are familiar with training deep neural networks in either PyTorch or
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TF2, and focus specifically on the nuances and tools for training models in
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🤗 Transformers.
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Sections:
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- :ref:`pytorch`
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- :ref:`tensorflow`
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- :ref:`trainer`
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- :ref:`additional-resources`
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.. _pytorch:
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Fine-tuning in native PyTorch
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Model classes in 🤗 Transformers that don't begin with ``TF`` are
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`PyTorch Modules <https://pytorch.org/docs/master/generated/torch.nn.Module.html>`_,
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meaning that you can use them just as you would any model in PyTorch for
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both inference and optimization.
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Let's consider the common task of fine-tuning a masked language model like
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BERT on a sequence classification dataset. When we instantiate a model with
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:func:`~transformers.PreTrainedModel.from_pretrained`, the model
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configuration and pre-trained weights
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of the specified model are used to initialize the model. The
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library also includes a number of task-specific final layers or 'heads' whose
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weights are instantiated randomly when not present in the specified
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pre-trained model. For example, instantiating a model with
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``BertForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=2)``
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will create a BERT model instance with encoder weights copied from the
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``bert-base-uncased`` model and a randomly initialized sequence
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classification head on top of the encoder with an output size of 2. Models
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are initialized in ``eval`` mode by default. We can call ``model.train()`` to
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put it in train mode.
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.. code-block:: python
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from transformers import BertForSequenceClassification
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model = BertForSequenceClassification.from_pretrained('bert-base-uncased', return_dict=True)
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model.train()
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This is useful because it allows us to make use of the pre-trained BERT
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encoder and easily train it on whatever sequence classification dataset we
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choose. We can use any PyTorch optimizer, but our library also provides the
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:func:`~transformers.AdamW` optimizer which implements gradient bias
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correction as well as weight decay.
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.. code-block:: python
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from transformers import AdamW
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optimizer = AdamW(model.parameters(), lr=1e-5)
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The optimizer allows us to apply different hyperpameters for specific
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parameter groups. For example, we can apply weight decay to all parameters
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other than bias and layer normalization terms:
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.. code-block:: python
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no_decay = ['bias', 'LayerNorm.weight']
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optimizer_grouped_parameters = [
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{'params': [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)], 'weight_decay': 0.01},
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{'params': [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], 'weight_decay': 0.0}
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]
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optimizer = AdamW(optimizer_grouped_parameters, lr=1e-5)
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Now we can set up a simple dummy training batch using
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:func:`~transformers.PreTrainedTokenizer.__call__`. This returns a
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:func:`~transformers.BatchEncoding` instance which
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prepares everything we might need to pass to the model.
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.. code-block:: python
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from transformers import BertTokenizer
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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text_batch = ["I love Pixar.", "I don't care for Pixar."]
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encoding = tokenizer(text_batch, return_tensors='pt', padding=True, truncation=True)
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input_ids = encoding['input_ids']
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attention_mask = encoding['attention_mask']
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When we call a classification model with the ``labels`` argument, the first
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returned element is the Cross Entropy loss between the predictions and the
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passed labels. Having already set up our optimizer, we can then do a
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backwards pass and update the weights:
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.. code-block:: python
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labels = torch.tensor([1,0]).unsqueeze(0)
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outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
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loss = outputs.loss
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loss.backward()
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optimizer.step()
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Alternatively, you can just get the logits and calculate the loss yourself.
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The following is equivalent to the previous example:
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.. code-block:: python
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from torch.nn import functional as F
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labels = torch.tensor([1,0])
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outputs = model(input_ids, attention_mask=attention_mask)
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loss = F.cross_entropy(outputs.logits, labels)
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loss.backward()
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optimizer.step()
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Of course, you can train on GPU by calling ``to('cuda')`` on the model and
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inputs as usual.
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We also provide a few learning rate scheduling tools. With the following, we
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can set up a scheduler which warms up for ``num_warmup_steps`` and then
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linearly decays to 0 by the end of training.
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.. code-block:: python
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from transformers import get_linear_schedule_with_warmup
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scheduler = get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_train_steps)
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Then all we have to do is call ``scheduler.step()`` after ``optimizer.step()``.
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.. code-block:: python
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loss.backward()
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optimizer.step()
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scheduler.step()
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We highly recommend using :func:`~transformers.Trainer`, discussed below,
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which conveniently handles the moving parts of training 🤗 Transformers models
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with features like mixed precision and easy tensorboard logging.
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Freezing the encoder
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-----------------------------------------------------------------------------------------------------------------------
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In some cases, you might be interested in keeping the weights of the
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pre-trained encoder frozen and optimizing only the weights of the head
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layers. To do so, simply set the ``requires_grad`` attribute to ``False`` on
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the encoder parameters, which can be accessed with the ``base_model``
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submodule on any task-specific model in the library:
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.. code-block:: python
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for param in model.base_model.parameters():
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param.requires_grad = False
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.. _tensorflow:
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Fine-tuning in native TensorFlow 2
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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Models can also be trained natively in TensorFlow 2. Just as with PyTorch,
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TensorFlow models can be instantiated with
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:func:`~transformers.PreTrainedModel.from_pretrained` to load the weights of
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the encoder from a pretrained model.
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.. code-block:: python
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from transformers import TFBertForSequenceClassification
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model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
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Let's use ``tensorflow_datasets`` to load in the `MRPC dataset
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<https://www.tensorflow.org/datasets/catalog/glue#gluemrpc>`_ from GLUE. We
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can then use our built-in
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:func:`~transformers.data.processors.glue.glue_convert_examples_to_features`
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to tokenize MRPC and convert it to a TensorFlow ``Dataset`` object. Note that
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tokenizers are framework-agnostic, so there is no need to prepend ``TF`` to
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the pretrained tokenizer name.
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.. code-block:: python
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from transformers import BertTokenizer, glue_convert_examples_to_features
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import tensorflow as tf
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import tensorflow_datasets as tfds
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tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
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data = tfds.load('glue/mrpc')
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train_dataset = glue_convert_examples_to_features(data['train'], tokenizer, max_length=128, task='mrpc')
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train_dataset = train_dataset.shuffle(100).batch(32).repeat(2)
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The model can then be compiled and trained as any Keras model:
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.. code-block:: python
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optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
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loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
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model.compile(optimizer=optimizer, loss=loss)
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model.fit(train_dataset, epochs=2, steps_per_epoch=115)
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With the tight interoperability between TensorFlow and PyTorch models, you
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can even save the model and then reload it as a PyTorch model (or vice-versa):
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.. code-block:: python
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from transformers import BertForSequenceClassification
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model.save_pretrained('./my_mrpc_model/')
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pytorch_model = BertForSequenceClassification.from_pretrained('./my_mrpc_model/', from_tf=True)
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.. _trainer:
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Trainer
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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We also provide a simple but feature-complete training and evaluation
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interface through :func:`~transformers.Trainer` and
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:func:`~transformers.TFTrainer`. You can train, fine-tune,
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and evaluate any 🤗 Transformers model with a wide range of training options and
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with built-in features like logging, gradient accumulation, and mixed
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precision.
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.. code-block:: python
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## PYTORCH CODE
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from transformers import BertForSequenceClassification, Trainer, TrainingArguments
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model = BertForSequenceClassification.from_pretrained("bert-large-uncased")
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training_args = TrainingArguments(
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output_dir='./results', # output directory
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num_train_epochs=3, # total # of training epochs
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per_device_train_batch_size=16, # batch size per device during training
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per_device_eval_batch_size=64, # batch size for evaluation
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warmup_steps=500, # number of warmup steps for learning rate scheduler
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weight_decay=0.01, # strength of weight decay
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logging_dir='./logs', # directory for storing logs
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)
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trainer = Trainer(
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model=model, # the instantiated 🤗 Transformers model to be trained
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args=training_args, # training arguments, defined above
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train_dataset=train_dataset, # training dataset
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eval_dataset=test_dataset # evaluation dataset
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)
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## TENSORFLOW CODE
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from transformers import TFBertForSequenceClassification, TFTrainer, TFTrainingArguments
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model = TFBertForSequenceClassification.from_pretrained("bert-large-uncased")
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training_args = TFTrainingArguments(
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output_dir='./results', # output directory
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num_train_epochs=3, # total # of training epochs
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per_device_train_batch_size=16, # batch size per device during training
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per_device_eval_batch_size=64, # batch size for evaluation
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warmup_steps=500, # number of warmup steps for learning rate scheduler
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weight_decay=0.01, # strength of weight decay
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logging_dir='./logs', # directory for storing logs
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)
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trainer = TFTrainer(
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model=model, # the instantiated 🤗 Transformers model to be trained
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args=training_args, # training arguments, defined above
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train_dataset=tfds_train_dataset, # tensorflow_datasets training dataset
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eval_dataset=tfds_test_dataset # tensorflow_datasets evaluation dataset
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)
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Now simply call ``trainer.train()`` to train and ``trainer.evaluate()`` to
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evaluate. You can use your own module as well, but the first
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argument returned from ``forward`` must be the loss which you wish to
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optimize.
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:func:`~transformers.Trainer` uses a built-in default function to collate
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batches and prepare them to be fed into the model. If needed, you can also
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use the ``data_collator`` argument to pass your own collator function which
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takes in the data in the format provided by your dataset and returns a
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batch ready to be fed into the model. Note that
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:func:`~transformers.TFTrainer` expects the passed datasets to be dataset
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objects from ``tensorflow_datasets``.
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To calculate additional metrics in addition to the loss, you can also define
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your own ``compute_metrics`` function and pass it to the trainer.
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.. code-block:: python
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from sklearn.metrics import accuracy_score, precision_recall_fscore_support
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def compute_metrics(pred):
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labels = pred.label_ids
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preds = pred.predictions.argmax(-1)
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precision, recall, f1, _ = precision_recall_fscore_support(labels, preds, average='binary')
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acc = accuracy_score(labels, preds)
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return {
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'accuracy': acc,
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'f1': f1,
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'precision': precision,
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'recall': recall
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}
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Finally, you can view the results, including any calculated metrics, by
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launching tensorboard in your specified ``logging_dir`` directory.
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.. _additional-resources:
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Additional resources
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^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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- `A lightweight colab demo <https://colab.research.google.com/drive/1-JIJlao4dI-Ilww_NnTc0rxtp-ymgDgM?usp=sharing>`_
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which uses ``Trainer`` for IMDb sentiment classification.
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- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_
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including scripts for training and fine-tuning on GLUE, SQuAD, and several other tasks.
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- `How to train a language model <https://colab.research.google.com/github/huggingface/blog/blob/master/notebooks/01_how_to_train.ipynb>`_,
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a detailed colab notebook which uses ``Trainer`` to train a masked language model from scratch on Esperanto.
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- `🤗 Transformers Notebooks <notebooks.html>`_ which contain dozens of example notebooks from the community for
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training and using 🤗 Transformers on a variety of tasks.
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