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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
specific language governing permissions and limitations under the License.
Training and fine-tuning
Fine-tuning a pretrained model
=======================================================================================================================
Model classes in 🤗 Transformers are designed to be compatible with native PyTorch and TensorFlow 2 and can be used
seamlessly 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.
In this tutorial, we will show you how to fine-tune a pretrained model from the Transformers library. In TensorFlow,
models can be directly trained using Keras and the :obj:`fit` method. In PyTorch, there is no generic training loop so
the 🤗 Transformers library provides an API with the class :class:`~transformers.Trainer` to let you fine-tune or train
a model from scratch easily. Then we will show you how to alternatively write the whole training loop in PyTorch.
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.
Before we can fine-tune a model, we need a dataset. In this tutorial, we will show you how to fine-tune BERT on the
`IMDB dataset <https://www.imdb.com/interfaces/>`__: the task is to classify whether movie reviews are positive or
negative. For examples of other tasks, refer to the :ref:`additional-resources` section!
Sections:
.. _data-processing:
- :ref:`pytorch`
- :ref:`tensorflow`
- :ref:`trainer`
- :ref:`additional-resources`
.. _pytorch:
Fine-tuning in native PyTorch
Preparing the datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.
We will use the `🤗 Datasets <https:/github.com/huggingface/datasets/>`__ library to download and preprocess the IMDB
datasets. We will go over this part pretty quickly. Since the focus of this tutorial is on training, you should refer
to the 🤗 Datasets `documentation <https://huggingface.co/docs/datasets/>`__ or the :doc:`preprocessing` tutorial for
more information.
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_labels=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.
First, we can use the :obj:`load_dataset` function to download and cache the dataset:
.. code-block:: python
from transformers import BertForSequenceClassification
model = BertForSequenceClassification.from_pretrained('bert-base-uncased')
model.train()
from datasets import load_dataset
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.
raw_datasets = load_dataset("imdb")
This works like the :obj:`from_pretrained` method we saw for the models and tokenizers (except the cache directory is
`~/.cache/huggingface/dataset` by default).
The :obj:`raw_datasets` object is a dictionary with three keys: :obj:`"train"`, :obj:`"test"` and :obj:`"unsupervised"`
(which correspond to the three splits of that dataset). We will use the :obj:`"train"` split for training and the
:obj:`"test"` split for validation.
To preprocess our data, we will need a tokenizer:
.. code-block:: python
from transformers import AdamW
optimizer = AdamW(model.parameters(), lr=1e-5)
from transformers import AutoTokenizer
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:
tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
As we saw in :doc:`preprocessing`, we can prepare the text inputs for the model with the following command (this is an
example, not a command you can execute):
.. 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)
inputs = tokenizer(sentences, padding="max_length", truncation=True)
Now we can set up a simple dummy training batch using :func:`~transformers.PreTrainedTokenizer.__call__`. This returns
a :func:`~transformers.BatchEncoding` instance which prepares everything we might need to pass to the model.
This will make all the samples have the maximum length the model can accept (here 512), either by padding or truncating
them.
However, we can instead apply these preprocessing steps to all the splits of our dataset at once by using the
:obj:`map` method:
.. 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']
def tokenize_function(examples):
return tokenizer(examples["text"], padding="max_length", truncation=True)
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:
tokenized_datasets = raw_datasets.map(tokenize_function, batched=True)
You can learn more about the map method or the other ways to preprocess the data in the 🤗 Datasets `documentation
<https://huggingface.co/docs/datasets/>`__.
Next we will generate a small subset of the training and validation set, to enable faster training:
.. code-block:: python
labels = torch.tensor([1,0]).unsqueeze(0)
outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
loss = outputs.loss
loss.backward()
optimizer.step()
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
full_train_dataset = tokenized_datasets["train"]
full_eval_dataset = tokenized_datasets["test"]
Alternatively, you can just get the logits and calculate the loss yourself. The following is equivalent to the previous
example:
In all the examples below, we will always use :obj:`small_train_dataset` and :obj:`small_eval_dataset`. Just replace
them by their `full` equivalent to train or evaluate on the full dataset.
.. code-block:: python
.. _trainer:
from torch.nn import functional as F
labels = torch.tensor([1,0])
outputs = model(input_ids, attention_mask=attention_mask)
loss = F.cross_entropy(outputs.logits, labels)
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
Fine-tuning in PyTorch with the Trainer API
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.
Since PyTorch does not provide a training loop, the 🤗 Transformers library provides a :class:`~transformers.Trainer`
API that is optimized for 🤗 Transformers models, with a wide range of training options and with built-in features like
logging, gradient accumulation, and mixed precision.
First, let's define our model:
.. code-block:: python
from transformers import TFBertForSequenceClassification
model = TFBertForSequenceClassification.from_pretrained('bert-base-uncased')
from transformers import AutoModelForSequenceClassification
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.
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
This will issue a warning about some of the pretrained weights not being used and some weights being randomly
initialized. That's because we are throwing away the pretraining head of the BERT model to replace it with a
classification head which is randomly initialized. We will fine-tune this model on our task, transferring the knowledge
of the pretrained model to it (which is why doing this is called transfer learning).
Then, to define our :class:`~transformers.Trainer`, we will need to instantiate a
:class:`~transformers.TrainingArguments`. This class contains all the hyperparameters we can tune for the
:class:`~transformers.Trainer` or the flags to activate the different training options it supports. Let's begin by
using all the defaults, the only thing we then have to provide is a directory in which the checkpoints will be saved:
.. code-block:: python
from transformers import TrainingArguments
training_args = TrainingArguments("test_trainer")
Then we can instantiate a :class:`~transformers.Trainer` like this:
.. code-block:: python
from transformers import Trainer
trainer = Trainer(
model=model, args=training_args, train_dataset=small_train_dataset, eval_dataset=small_eval_dataset
)
To fine-tune our model, we just need to call
.. code-block:: python
trainer.train()
which will start a training that you can follow with a progress bar, which should take a couple of minutes to complete
(as long as you hav access to a GPU). It won't actually tell you anything useful about how well (or badly) your model
is performing however as by default, there is no evaluation during training, and we didn't tell the
:class:`~transformers.Trainer` to compute any metrics. Let's have a look on how to do that now!
To have the :class:`~transformers.Trainer` compute and report metrics, we need to give it a :obj:`compute_metrics`
function that takes predictions and labels (grouped in a namedtuple called :class:`~transformers.EvalPrediction`) and
return a dictionary with string items (the metric names) and float values (the metric values).
The 🤗 Datasets library provides an easy way to get the common metrics used in NLP with the :obj:`load_metric` function.
here we simply use accuracy. Then we define the :obj:`compute_metrics` function that just convert logits to predictions
(remember that all 🤗 Transformers models return the logits) and feed them to :obj:`compute` method of this metric.
.. code-block:: python
import numpy as np
from datasets import load_metric
metric = load_metric("accuracy")
def compute_metrics(eval_pred):
logits, labels = eval_pred
predictions = np.argmax(logits, axis=-1)
return metric.compute(predictions=predictions, references=labels)
The compute function needs to receive a tuple (with logits and labels) and has to return a dictionary with string keys
(the name of the metric) and float values. It will be called at the end of each evaluation phase on the whole arrays of
predictions/labels.
To check if this works on practice, let's create a new :class:`~transformers.Trainer` with our fine-tuned model:
.. code-block:: python
trainer = Trainer(
model=model,
args=training_args,
train_dataset=small_train_dataset,
eval_dataset=small_eval_dataset,
compute_metrics=compute_metrics,
)
trainer.evaluate()
which showed an accuracy of 87.5% in our case.
If you want to fine-tune your model and regularly report the evaluation metrics (for instance at the end of each
epoch), here is how you should define your training arguments:
.. code-block:: python
from transformers import TrainingArguments
training_args = TrainingArguments("test_trainer", evaluation_strategy="epoch")
See the documentation of :class:`~transformers.TrainingArguments` for more options.
.. _keras:
Fine-tuning with Keras
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Models can also be trained natively in TensorFlow using the Keras API. First, let's define our model:
.. code-block:: python
from transformers import BertTokenizer, glue_convert_examples_to_features
import tensorflow as tf
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)
from transformers import TFAutoModelForSequenceClassification
The model can then be compiled and trained as any Keras model:
model = TFAutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
Then we will need to convert our datasets from before in standard :obj:`tf.data.Dataset`. Since we have fixed shapes,
it can easily be done like this. First we remove the `"text"` column from our datasets and set them in TensorFlow
format:
.. 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)
tf_train_dataset = small_train_dataset.remove_columns(["text"]).with_format("tensorflow")
tf_eval_dataset = small_eval_dataset.remove_columns(["text"]).with_format("tensorflow")
Then we convert everything in big tensors and use the :obj:`tf.data.Dataset.from_tensor_slices` method:
.. code-block:: python
train_features = {x: tf_train_dataset[x].to_tensor() for x in tokenizer.model_input_names}
train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, tf_train_dataset["label"]))
train_tf_dataset = train_tf_dataset.shuffle(len(tf_train_dataset)).batch(8)
eval_features = {x: tf_eval_dataset[x].to_tensor() for x in tokenizer.model_input_names}
eval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, tf_eval_dataset["label"]))
eval_tf_dataset = eval_tf_dataset.batch(8)
With this done, the model can then be compiled and trained as any Keras model:
.. code-block:: python
model.compile(
optimizer=tf.keras.optimizers.Adam(learning_rate=5e-5),
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=tf.metrics.SparseCategoricalAccuracy(),
)
model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3)
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)
from transformers import AutoModelForSequenceClassification
model.save_pretrained("my_imdb_model")
pytorch_model = AutoModelForSequenceClassification.from_pretrained("my_imdb_model", from_tf=True)
.. _trainer:
.. _pytorch_native:
Trainer
Fine-tuning in native PyTorch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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.
You might need to restart your notebook at this stage to free some memory, or excute the following code:
.. code-block:: python
## PYTORCH CODE
from transformers import BertForSequenceClassification, Trainer, TrainingArguments
del model
del pytorch_model
del trainer
torch.cuda.empty_cache()
model = BertForSequenceClassification.from_pretrained("bert-large-uncased")
Let's now see how to achieve the same results as in :ref:`trainer section <trainer>` in PyTorch. First we need to
define the dataloaders, which we will use to iterate over batches. We just need to apply a bit of post-processing to
our :obj:`tokenized_datasets` before doing that to:
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
)
- remove the columns corresponding to values the model does not expect (here the :obj:`"text"` column)
- rename the column :obj:`"label"` to :obj:`"labels"` (because the model expect the argument to be named :obj:`labels`)
- set the format of the datasets so they return PyTorch Tensors instead of lists.
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 provided 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.
Our `tokenized_datasets` has one method for each of those steps:
.. code-block:: python
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
tokenized_datasets = tokenized_datasets.remove_columns(["text"])
tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
tokenized_datasets.set_format("torch")
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
}
small_train_dataset = tokenized_datasets["train"].shuffle(seed=42).select(range(1000))
small_eval_dataset = tokenized_datasets["test"].shuffle(seed=42).select(range(1000))
Finally, you can view the results, including any calculated metrics, by launching tensorboard in your specified
``logging_dir`` directory.
Now that this is done, we can easily define our dataloaders:
.. code-block:: python
from torch.utils.data import DataLoader
train_dataloader = DataLoader(small_train_dataset, shuffle=True, batch_size=8)
eval_dataloader = DataLoader(small_eval_dataset, batch_size=8)
Next, we define our model:
.. code-block:: python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained("bert-base-cased", num_labels=2)
We are almost ready to write our training loop, the only two things are missing are an optimizer and a learning rate
scheduler. The default optimizer used by the :class:`~transformers.Trainer` is :class:`~transformers.AdamW`:
.. code-block:: python
from transformers import AdamW
optimizer = AdamW(model.parameters(), lr=5e-5)
Finally, the learning rate scheduler used by default it just a linear decay form the maximum value (5e-5 here) to 0:
.. code-block:: python
from transformers import get_scheduler
num_epochs = 3
num_training_steps = num_epochs * len(train_dataloader)
lr_scheduler = get_scheduler(
"linear",
optimizer=optimizer,
num_warmup_steps=0,
num_training_steps=num_training_steps
)
One last thing, we will want to use the GPU if we have access to one (otherwise training might take several hours
instead of a couple of minutes). To do this, we define a :obj:`device` we will put our model and our batches on.
.. code-block:: python
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model.to(device)
We now are ready to train! To get some sense of when it will be finished, we add a progress bar over our number of
training steps, using the `tqdm` library.
.. code-block:: python
from tqdm.auto import tqdm
progress_bar = tqdm(range(num_training_steps))
model.train()
for epoch in range(num_epochs):
for batch in train_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
outputs = model(**batch)
loss = outputs.loss
loss.backward()
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
progress_bar.update(1)
Note that if you are used to freezing the body of your pretrained model (like in computer vision) the above may seem a
bit strange, as we are directly fine-tuning the whole model without taking any precaution. It actually works better
this way for Transformers model (so this is not an oversight on our side). If you're not familiar with what "freezing
the body" of the model means, forget you read this paragraph.
Now to check the results, we need to write the evaluation loop. Like in the :ref:`trainer section <trainer>` we will
use a metric from the datasets library. Here we accumulate the predictions at each batch before computing the final
result when the loop is finished.
.. code-block:: python
metric= load_metric("accuracy")
model.eval()
for batch in eval_dataloader:
batch = {k: v.to(device) for k, v in batch.items()}
with torch.no_grad():
outputs = model(**batch)
logits = outputs.logits
predictions = torch.argmax(logits, dim=-1)
metric.add_batch(predictions=predictions, references=batch["labels"])
metric.compute()
.. _additional-resources:
@@ -285,15 +388,10 @@ Finally, you can view the results, including any calculated metrics, by launchin
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.
To look at more fine-tuning examples you can refer to:
- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`_ including scripts for
training and fine-tuning on GLUE, SQuAD, and several other tasks.
- `🤗 Transformers Examples <https://github.com/huggingface/transformers/tree/master/examples>`__ which includes scripts
to train on all common NLP tasks in PyTorch and TensorFlow.
- `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.
- `🤗 Transformers Notebooks <notebooks.html>`__ which contains various notebooks and in particular one per task (look
for the `how to finetune a model on xxx`).