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Fix special character in MDX (#14721)

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Sylvain Gugger 2021-12-10 16:02:48 -05:00 committed by GitHub
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commit 5eca742f6c
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docs/source/quicktour.mdx
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@ -246,10 +246,10 @@ objects are described in greater detail [here](main_classes/output). For now, le
```py
>>> print(pt_outputs)
SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833, 4.3364],
[ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
[ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
===PT-TF-SPLIT===
>>> print(tf_outputs)
TFSequenceClassifierOutput(loss=None, logits=&amp;lt;tf.Tensor: shape=(2, 2), dtype=float32, numpy=
TFSequenceClassifierOutput(loss=None, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-4.0833 , 4.3364 ],
[ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
```
@ -278,7 +278,7 @@ We can see we get the numbers from before:
```py
>>> print(pt_predictions)
tensor([[2.2043e-04, 9.9978e-01],
[5.3086e-01, 4.6914e-01]], grad_fn=&amp;lt;SoftmaxBackward>)
[5.3086e-01, 4.6914e-01]], grad_fn=<SoftmaxBackward>)
===PT-TF-SPLIT===
>>> print(tf_predictions)
tf.Tensor(
@ -293,13 +293,13 @@ attribute:
>>> import torch
>>> pt_outputs = pt_model(**pt_batch, labels = torch.tensor([1, 0]))
>>> print(pt_outputs)
SequenceClassifierOutput(loss=tensor(0.3167, grad_fn=&amp;lt;NllLossBackward>), logits=tensor([[-4.0833, 4.3364],
[ 0.0818, -0.0418]], grad_fn=&amp;lt;AddmmBackward>), hidden_states=None, attentions=None)
SequenceClassifierOutput(loss=tensor(0.3167, grad_fn=<NllLossBackward>), logits=tensor([[-4.0833, 4.3364],
[ 0.0818, -0.0418]], grad_fn=<AddmmBackward>), hidden_states=None, attentions=None)
===PT-TF-SPLIT===
>>> import tensorflow as tf
>>> tf_outputs = tf_model(tf_batch, labels = tf.constant([1, 0]))
>>> print(tf_outputs)
TFSequenceClassifierOutput(loss=&amp;lt;tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051e-04, 6.3326e-01], dtype=float32)>, logits=&amp;lt;tf.Tensor: shape=(2, 2), dtype=float32, numpy=
TFSequenceClassifierOutput(loss=<tf.Tensor: shape=(2,), dtype=float32, numpy=array([2.2051e-04, 6.3326e-01], dtype=float32)>, logits=<tf.Tensor: shape=(2, 2), dtype=float32, numpy=
array([[-4.0833 , 4.3364 ],
[ 0.0818, -0.0418]], dtype=float32)>, hidden_states=None, attentions=None)
```