===PT-TF-SPLIT===
print(tf_outputs)
TFSequenceClassifierOutput(loss=None, logits=, hidden_states=None, attentions=None)
```
Notice how the output object has a logits attribute. You can use this to access the model's final activations.
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.
```py
from torch import nn
pt_predictions = nn.functional.softmax(pt_outputs.logits, dim=-1)
===PT-TF-SPLIT===
import tensorflow as tf
tf_predictions = tf.nn.softmax(tf_outputs.logits, axis=-1)
```
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=)
