SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Doc styling (#8067)

Browse Source 
* Important files

* Styling them all

* Revert "Styling them all"

This reverts commit 7d029395fdae8513b8281cbc2a6c239f8093503e.

* Syling them for realsies

* Fix syntax error

* Fix benchmark_utils

* More fixes

* Fix modeling auto and script

* Remove new line

* Fixes

* More fixes

* Fix more files

* Style

* Add FSMT

* More fixes

* More fixes

* More fixes

* More fixes

* Fixes

* More fixes

* More fixes

* Last fixes

* Make sphinx happy
This commit is contained in:
Sylvain Gugger
2020-10-26 18:26:02 -04:00
committed by GitHub
parent 04a17f8550
commit 08f534d2da
271 changed files with 9726 additions and 8991 deletions
Download Patch File Download Diff File Expand all files Collapse all files

58
docs/source/quicktour.rst
View File

@@ -1,8 +1,8 @@
Quick tour
=======================================================================================================================
Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for
Natural Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
Let's have a quick look at the 🤗 Transformers library features. The library downloads pretrained models for Natural
Language Understanding (NLU) tasks, such as analyzing the sentiment of a text, and Natural Language Generation (NLG),
such as completing a prompt with new text or translating in another language.
First we will see how to easily leverage the pipeline API to quickly use those pretrained models at inference. Then, we
@@ -29,8 +29,8 @@ provides the following tasks out of the box:
- Translation: translate a text in another language.
- Feature extraction: return a tensor representation of the text.
Let's see how this work for sentiment analysis (the other tasks are all covered in the
:doc:`task summary </task_summary>`):
Let's see how this work for sentiment analysis (the other tasks are all covered in the :doc:`task summary
</task_summary>`):
.. code-block::
@@ -160,9 +160,10 @@ To apply these steps on a given text, we can just feed it to our tokenizer:
>>> inputs = tokenizer("We are very happy to show you the 🤗 Transformers library.")
This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__,
as mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the sequence:
This returns a dictionary string to list of ints. It contains the `ids of the tokens <glossary.html#input-ids>`__, as
mentioned before, but also additional arguments that will be useful to the model. Here for instance, we also have an
`attention mask <glossary.html#attention-mask>`__ that the model will use to have a better understanding of the
sequence:
.. code-block::
@@ -191,8 +192,8 @@ and get tensors back. You can specify all of that to the tokenizer:
... return_tensors="tf"
... )
The padding is automatically applied on the side expected by the model (in this case, on the right), with the
padding token the model was pretrained with. The attention mask is also adapted to take the padding into account:
The padding is automatically applied on the side expected by the model (in this case, on the right), with the padding
token the model was pretrained with. The attention mask is also adapted to take the padding into account:
.. code-block::
@@ -213,8 +214,8 @@ Using the model
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Once your input has been preprocessed by the tokenizer, you can send it directly to the model. As we mentioned, it will
contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the
dictionary keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
contain all the relevant information the model needs. If you're using a TensorFlow model, you can pass the dictionary
keys directly to tensors, for a PyTorch model, you need to unpack the dictionary by adding :obj:`**`.
.. code-block::
@@ -223,8 +224,8 @@ dictionary keys directly to tensors, for a PyTorch model, you need to unpack the
>>> ## TENSORFLOW CODE
>>> tf_outputs = tf_model(tf_batch)
In 🤗 Transformers, all outputs are tuples (with only one element potentially). Here, we get a tuple with just the
final activations of the model.
In 🤗 Transformers, all outputs are tuples (with only one element potentially). Here, we get a tuple with just the final
activations of the model.
.. code-block::
@@ -239,11 +240,10 @@ final activations of the model.
[ 0.08181786, -0.04179301]], dtype=float32)>,)
The model can return more than just the final activations, which is why the output is a tuple. Here we only asked for
the final activations, so we get a tuple with one element.
.. note::
the final activations, so we get a tuple with one element. .. note::
All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final
activation function (like SoftMax) since this final activation function is often fused with the loss.
All 🤗 Transformers models (PyTorch or TensorFlow) return the activations of the model *before* the final activation
function (like SoftMax) since this final activation function is often fused with the loss.
Let's apply the SoftMax activation to get predictions.
@@ -281,11 +281,11 @@ If you have labels, you can provide them to the model, it will return a tuple wi
>>> import tensorflow as tf
>>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))
Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or
`tf.keras.Model <https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual
training loop. 🤗 Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if
you are using TensorFlow) class to help with your training (taking care of things such as distributed training, mixed
precision, etc.). See the :doc:`training tutorial <training>` for more details.
Models are standard `torch.nn.Module <https://pytorch.org/docs/stable/nn.html#torch.nn.Module>`__ or `tf.keras.Model
<https://www.tensorflow.org/api_docs/python/tf/keras/Model>`__ so you can use them in your usual training loop. 🤗
Transformers also provides a :class:`~transformers.Trainer` (or :class:`~transformers.TFTrainer` if you are using
TensorFlow) class to help with your training (taking care of things such as distributed training, mixed precision,
etc.). See the :doc:`training tutorial <training>` for more details.
.. note::
@@ -336,13 +336,13 @@ The :obj:`AutoModel` and :obj:`AutoTokenizer` classes are just shortcuts that wi
pretrained model. Behind the scenes, the library has one model class per combination of architecture plus class, so the
code is easy to access and tweak if you need to.
In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's
using the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
:class:`~transformers.AutoModelForSequenceClassification` (or :class:`~transformers.TFAutoModelForSequenceClassification`
if you are using TensorFlow) was used, the model automatically created is then a
:class:`~transformers.DistilBertForSequenceClassification`. You can look at its documentation for all details relevant
to that specific model, or browse the source code. This is how you would directly instantiate model and tokenizer
without the auto magic:
In our previous example, the model was called "distilbert-base-uncased-finetuned-sst-2-english", which means it's using
the :doc:`DistilBERT </model_doc/distilbert>` architecture. As
:class:`~transformers.AutoModelForSequenceClassification` (or
:class:`~transformers.TFAutoModelForSequenceClassification` if you are using TensorFlow) was used, the model
automatically created is then a :class:`~transformers.DistilBertForSequenceClassification`. You can look at its
documentation for all details relevant to that specific model, or browse the source code. This is how you would
directly instantiate model and tokenizer without the auto magic:
.. code-block::