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Training & fine-tuning quickstart (#5034)

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* add initial fine-tuning guide

* split code blocks to smaller segments

* fix up trianer section of fine-tune doc

* a few last typos

* Update usage -> task summary link

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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2020-06-25 15:11:11 -06:00
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docs/source/index.rst
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task_summary
model_summary
training
preprocessing
serialization
model_sharing

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