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 **.
```py
pt_outputs = pt_model(**pt_batch)
===PT-TF-SPLIT===
tf_outputs = tf_model(tf_batch)
```
In 🤗 Transformers, all outputs are objects that contain the model's final activations along with other metadata. These
objects are described in greater detail here. For now, let's inspect the output ourselves:
```py
print(pt_outputs)
SequenceClassifierOutput(loss=None, logits=tensor([[-4.0833,  4.3364],
        [ 0.0818, -0.0418]], grad_fn=), hidden_states=None, attentions=None)
