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Add video links to the documentation (#12162)

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Sylvain Gugger
2021-06-15 06:37:37 -04:00
committed by GitHub
parent 040283170c
commit a55dc157e3
7 changed files with 167 additions and 26 deletions
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docs/source/training.rst
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@@ -27,6 +27,12 @@ negative. For examples of other tasks, refer to the :ref:`additional-resources`
Preparing the datasets
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/_BZearw7f0w" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
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
@@ -95,6 +101,12 @@ them by their `full` equivalent to train or evaluate on the full dataset.
Fine-tuning in PyTorch with the Trainer API
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/nvBXf7s7vTI" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
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.
@@ -200,6 +212,12 @@ See the documentation of :class:`~transformers.TrainingArguments` for more optio
Fine-tuning with Keras
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/rnTGBy2ax1c" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
Models can also be trained natively in TensorFlow using the Keras API. First, let's define our model:
.. code-block:: python
@@ -257,6 +275,12 @@ as a PyTorch model (or vice-versa):
Fine-tuning in native PyTorch
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. raw:: html
<iframe width="560" height="315" src="https://www.youtube.com/embed/Dh9CL8fyG80" title="YouTube video player"
frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope;
picture-in-picture" allowfullscreen></iframe>
You might need to restart your notebook at this stage to free some memory, or excute the following code:
.. code-block:: python