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update QuickTour docs to reflect model output object (#11462)

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* update docs to reflect model output object

* run make style`
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Hamel Husain
2021-04-26 19:18:37 -07:00
committed by GitHub
parent 741d48f5c7
commit 88ac60f7b5
2 changed files with 12 additions and 13 deletions
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docs/source/quicktour.rst
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@@ -238,23 +238,22 @@ keys directly to tensors, for a PyTorch model, you need to unpack the dictionary
>>> ## 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 objects that contain the model's final activations along with other metadata. These
objects are described in greater detail :doc:`here <main_classes/output>`. For now, let's inspect the output ourselves:
.. code-block::
>>> ## PYTORCH CODE
>>> print(pt_outputs)
(tensor([[-4.0833, 4.3364],
[ 0.0818, -0.0418]], grad_fn=<AddmmBackward>),)
SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833, 4.3364],
[ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
>>> ## TENSORFLOW CODE
>>> print(tf_outputs)
(<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-4.0832963 , 4.336414 ],
[ 0.08181786, -0.04179301]], dtype=float32)>,)
TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-4.0832963 , 4.3364143 ],
[ 0.081807 , -0.04178282]], dtype=float32)>, hidden_states=None, attentions=None)
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.
Notice how the output object has a ``logits`` attribute. You can use this to access the model's final activations.
.. note::
@@ -267,10 +266,10 @@ Let's apply the SoftMax activation to get predictions.
>>> ## PYTORCH CODE
>>> import torch.nn.functional as F
>>> pt_predictions = F.softmax(pt_outputs[0], dim=-1)
>>> pt_predictions = F.softmax(pt_outputs.logits, dim=-1)
>>> ## TENSORFLOW CODE
>>> import tensorflow as tf
>>> tf_predictions = tf.nn.softmax(tf_outputs[0], axis=-1)
>>> tf.nn.softmax(tf_outputs.logits, axis=-1)
We can see we get the numbers from before: